SPELLING AND GRAMMAR CHECKING SYSTEM

Abstract: A system of correcting misspelled words in input text detects a misspelled word in the input text, determines a list of alternative words for the misspelled word, and ranks the list of alternative words based on a context of the input text. The system then selects one of the alternative words from the list, and replaces the misspelled word in the text with the selected one of the alternative words.

Description: SPELLING AND GRAMMAR CHECKING SYSTEM BACKGROUND OF THE INVENTION Field Of The Invention The present invention relates generally to a spelling and grammar checking system, and more particularly to a spelling and grammar checking system which corrects misspelled words, incorrectly -used words, and contextual and grammatical errors. The invention has particular utility in connection with machine translation systems, word processing systems, and text indexing and retrieval systems such as World Wide Web search engines. Description Of The Related Art Conventional spelling correction systems, such as those found in most common word processing applications, check whether each word in a document is found in a dictionary database. When a word is not found in the dictionary, the word is flagged as being incorrectly spelled. Suggestions for replacing the incorrectly-spelled word with its correctly- spelled counterpart are then determined by inserting, deleting and/or transposing characters in the misspelled word. For example, in a sentence like My son thre a ball at me, the word thre is not correctly-spelled. Conventional spelling correction systems, such as those described in U.S. Patent No. 4,580,241 (Kucera) and U.S. Patent No. 4,730,269 (Kucera), suggest words such as threw, three, there and the, as possible alternatives for the misspelled word by adding and deleting characters at different locations in the misspelled word. These alternative words are then displayed to a user, who must then select one of the alternatives. One of the drawbacks of conventional systems is that they lack the ability to suggest alternative words based on the context in which the misspelled word appears. For example, in the following three sentences, the word thre appears in different contexts and, therefore, should be corrected differently in each sentence. My son thre a ball through the window. He broke thre window. He moved thre years ago. More specifically, in the first sentence, the incorrectly-spelled word thre should be replaced by threw. In the second sentence, the word thre should be replaced by the. In the third sentence, the word thre should be replaced by three. In spite of these differences in context, conventional spelling correction systems suggest the same list of alternative words, ranked in the same order, for all three of the foregoing sentences. For example, the spelling correction program provided in Microsoft ® Word '97 suggests the following words, in the following order, for all three of the foregoing sentences: three, there, the, throe, threw. Since conventional spelling correction systems do not rank alternative words according to context, such systems are not able to correct spelling mistakes automatically, since to do so often leads to an inordinate number of incorrectly corrected words. Rather, such systems typically use an interactive approach to correcting misspelled words. While such an approach can be effective, it is inefficient, and oftentimes very slow, particularly when large documents are involved. Accordingly, there exists a need for a spell checking system which is capable of ranking alternative words according to context, and which is also capable of automatically correcting misspelled words without significant user intervention. Conventional spelling correction systems are also unable to correct grammatical errors in a document or other input text, particularly if those words are spelled correctly but are misused in context. By way of example, although the word too is misused in the sentence He would like too go home, conventional spelling correction systems would not change too to to, since too is correctly spelled. In this regard, grammar checking systems are available which correct improperly used words (see, e.g. , U.S. Patent No. 4,674,065 (Lange), U.S. Patent No. 5,258,909 (Damerau), U.S. Patent No. 5,537,317 (Schabes), U.S. Patent No. 4,672,571 (Bass), and U.S. Patent No. 4,847,766 (McRae))! Such systems, however, are of limited use, since they are only capable of correcting relatively short lists of predefined words. More importantly, such systems are not capable of performing grammar corrections on words that have been misspelled. Accordingly, there exists a need for a spelling and grammar checking system which is capable of correcting words that have misused in a given context in cases where the words have been spelled incorrectly and in cases where the words have been spelled correctly. SUMMARY OF THE INVENTION The present invention addresses the foregoing needs by providing a system which corrects both the spelling and grammar of words using finite state machines, such as finite state transducers and finite state automata. For each word in a text sequence, the present invention provides a list of alternative words ranked according to a context of the text sequence, and then uses this list to correct words in the text (either interactively or automatically). The invention has a variety of uses, and is of particular use in the fields of word processing, machine translation, text indexing and retrieval, and optical character recognition, to name a few. In brief, the present invention determines alternatives for misspelled words, and ranks these alternatives based on a context in which the misspelled word occurs. For example, for the sentence My son thre a ball through the window, the present invention suggests the word threw as the best correction for the word thre, whereas for the sentence He broke thre window, the present invention suggests the word the as the best correction for the word thre. In its interactive mode, the invention displays alternative word suggestions to a user and then corrects misspelled words in response to a user's selection of an alternative word. In contrast, in its automatic mode, the present invention determines, on its own, which of the alternatives should be used, and then implements any necessary corrections automatically (i.e. , without user input). Advantageously, the invention also addresses incorrect word usage in the same manner that it addresses misspelled words. Thus, the invention can be used to correct improper use of commonly-confused words such as who and whom, homophones such as then and than, and other such words that are spelled correctly, but that are improper in context. For example, the invention will correct the sentence He thre the ball to the sentence He threw the ball (and not three, the, ...); the sentence fragment flight smulator to flight simulator (and not stimulator); the sentence fragment air baze to air base (and not baize, bass, babe, or bade); the phrase 77zre Miles Island to 77tree Miles Island (and not The or Threw) ; and the phrase ar traffic controller to air traffic controller (and not are, arc, ...). The invention also can be used to restore accents (such as a, a, e, ....) or diacritic marks (such as n, c, ...) in languages such as French and Spanish. For example, the current invention corrects the sentence il I 'a releve to il I'a releve (and not releve, relevent, ...). According to one aspect, the present invention is a system (i.e. , an apparatus, a method and/or computer-executable process steps) for correcting misspelled words in input text. The system detects a misspelled word in the input text, and determines a list of alternative words for the misspelled word. The list of alternative words is then ranked based on a context of the input text. According to another aspect, the present invention is a word processing system for creating and editing text documents. The word processing system inputs text into a text document, spell-checks the text so as to replace misspelled words in the text with correctly-spelled words, and outputs the document. The spell-checking performed by the system comprises detecting misspelled words in the text, and, for each misspelled word, determining a list of alternative words for the misspelled word, ranking the list of alternative words based on a context in the text, selecting one of the alternative words from the list, and replacing the misspelled word in the text with the selected one of the alternative words. According to another aspect, the present invention is a machine translation system for translating text from a first language into a second language. The machine translation system inputs text in the first language, spell-checks the text in the first language so as to replace misspelled words in the text with correctly-spelled words, translates the text from the first language into the second language, and outputs translated text. The spell-checking performed by the system comprises detecting misspelled words in the text, and, for each misspelled word, determining a list of alternative words for the misspelled word, ranking the list of alternative words based on a context in the text, selecting one of the alternative words from the list, and replacing the misspelled word in the document with the selected one of the alternative words. According to another aspect, the present invention is a machine translation system for translating text from a first language into a second language. The machine translation system inputs text in the first language, translates the text from the first language into the second language, spell-checks the text in the second language so as to replace misspelled words in the text with correctly-spelled words, and outputs the text. The spell-checking performed by the system comprises detecting misspelled words in the text, and, for each misspelled word, determining a list of alternative words for the misspelled word, ranking the list of alternative words based on a context in the text, selecting one of the alternative words from the list, and replacing the misspelled word in the document with the selected one of the alternative words. According to another aspect, the present invention is an optical character recognition system for recognizing input character images. The optical character recognition system inputs a document image, parses character images from the document image, performs recognition processing on parsed character images so as to produce document text, spell-checks the document text so as to replace misspelled words in the document text with correctly-spelled words, and outputs the document text. The spell-checking performed by the system comprises detecting misspelled words in the document text, and, for each misspelled word, determining a list of alternative words for the misspelled word, ranking the list of alternative words based on a context in the text, selecting one of the alternative words from the list, and replacing the misspelled word in the document text with the selected one of the alternative words. According to another aspect, the present invention is a system for retrieving text from a source. The system inputs a search word, corrects a spelling of the search word to produce a corrected search word, and retrieves text from the source that includes the corrected search word. According to another aspect, the present invention is a system for retrieving text from a source. The system inputs a search phrase comprised of a plurality of words, at least one of the plurality of words being an incorrect word, and replaces the incorrect word in the search phrase with a corrected word in order to produce a corrected search phrase. Text is then retrieved from the source based on the corrected search phrase. According to another aspect, the present invention is a system for correcting misspelled words in input text sequences received from a plurality of different clients. The system stores, in a memory on a server, a lexicon comprised of a plurality of reference words, and receives the input text sequences from the plurality of different clients. The system then spell-checks the input text sequences using the reference words in the lexicon, and outputs spell-checked text sequences to the plurality of different clients. According to another aspect, the present invention is a system for selecting a replacement word for an input word in a phrase. The system determines alternative words for the input word, the alternative words including at least one compound word which is comprised of two or more separate words, each alternative word having a rank associated therewith. The system then selects, as the replacement word, an alternative word having a highest rank. According to another aspect, the present invention is a system for correcting grammatical errors in input text. The system generates a first finite state machine ("FSM") for the input text, the first finite state machine including alternative words for at least one word in the input text and a rank associated with each alternative word, and adjusts the ranks in the first FSM in accordance with one or more of a plurality of predetermined grammatical rules. The system then determines which of the alternative words is grammatically correct based on the ranks associated with the alternative words, and replaces the at least one word in the input text with a grammatically-correct alternative word determined in the determining step. According to another aspect, the present invention is a word processing system for creating and editing text documents. The word processing system inputs text into a text document, checks the document for grammatically-incorrect words, replaces grammatically-incorrect words in the document with grammatically-correct words, and outputs the document. The checking performed by the system comprises (i) generating a finite state machine ("FSM") for text in the text document, the finite state machine including alternative words for at least one word in the text and a rank associated with each alternative word, (ii) adjusting the ranks in the FSM in accordance with one or more of a plurality of predetermined grammatical rules, and (iii) determining which of the alternative words is grammatically correct based on ranks for the alternative words. According to another aspect, the present invention is a machine translation system for translating text from a first language into a second language. The machine translation system inputs text in the first language, checks the text in the first language for grammatically-incorrect words, and replaces grammatically-incorrect words in the text with grammatically-correct words. The machine translation system then translates the text with the grammatically-correct words from the first language into the second language, and outputs the text in the second language. The checking performed by the machine translation system comprises (i) generating a finite state machine ("FSM") for the text in the first language, the finite state machine including alternative words for at least one word in the text and a rank associated with each alternative word, (ii) adjusting the ranks in the FSM in accordance with one or more of a plurality of predetermined grammatical rules, and (iii) determining which of the alternative words is grammatically correct based on ranks for the alternative words. According to another aspect, the present invention is a machine translation system for translating text from a first language into a second language. The machine translation system inputs text in the first language, translates the text from the first language into the second language, checks the text in the second language for grammatically- incorrect words, replaces grammatically-incorrect words in the text with grammatically-correct words, and outputs the text with the grammatically- correct words. The checking performed by the system comprises (i) generating a finite state machine ("FSM") for the text in the second language, the finite state machine including alternative words for at least one word in the text and a rank associated with each alternative word, (ii) adjusting the ranks in the FSM in accordance with one or more of a plurality of predetermined grammatical rules, and (iii) determining which of the alternative words is grammatically correct based on ranks for the alternative words. According to another aspect, the present invention is an optical character recognition system for recognizing input character images. The optical character recognition system inputs a document image, parses character images from the document image, performs recognition processing on parsed character images so as to produce document text, checks the document text for grammatically-incorrect words, replaces grammatically-incorrect words in the document text with grammatically correct words, and outputs the document text. The checking performed by the system comprises (i) generating a finite state machine ("FSM") for the document text, the finite state machine including alternative words for at least one word in the text and a rank associated with each alternative word, (ii) adjusting the ranks in the FSM in accordance with one or more of a plurality of predetermined grammatical rules, and (iii) determining which of the alternative words is grammatically correct based on ranks for the alternative words. According to another aspect, the present invention is a system for retrieving text from a source. The system inputs a search phrase comprised of a plurality of words, at least one of the plurality of words being a grammatically-incorrect word, replaces the grammatically- incorrect word in the search phrase with a grammatically-correct word in order to produce a corrected search phrase, and retrieves text from the source based on the corrected search phrase. According to another aspect, the present invention is a system of spell-checking input text. The system detects a misspelled word in the input text, stores one or more lexicon finite state machines ("FSM") in a memory, each of the lexicon FSMs including plural reference words, generates an input FSM for the misspelled word, selects one or more reference words from the lexicon FSMs based on the input FSM, the one or more reference words substantially corresponding to a spelling of the misspelled word, and outputs selected ones of the one or more reference words. This brief summary has been provided so that the nature of the invention may be understood quickly. A more complete understanding of the invention can be obtained by reference to the following detailed description of the preferred embodiments thereof in connection with the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 shows representative computer-hardware on which the spelling and grammar checking system of the present invention may be executed. Figure 2 shows the internal construction of the hardware shown in Figure 1. Figure 3 depicts operation of the spelling and grammar checking system of the present invention in a manual mode. Figure 4 depicts operation of the spelling and grammar checking system of the present invention in an automatic mode. Figure 5 depicts operation of a spelling suggestion module used in the spelling and grammar checking system of the present invention. Figure 6 depicts an input finite state transducer ("FST") generated by the spelling suggestion module depicted in Figure 6. Figure 7 shows another example of an FST generated by the spelling suggestion module depicted in Figure 6. Figure 8 shows an example of a lexicon FST used in the spelling suggestion module depicted in Figure 6. Figure 9 shows an example of a spelling FST used in the spelling suggestion module depicted in Figure 6. Figure 10 illustrates an FST generated by an automaton conversion module used in the spelling and grammar checking code shown in Figures 3 and 4. Figure 11 shows another example of an FST generated by the automaton conversion module used in the spelling and grammar checking code shown in Figures 3 and 4. Figure 12 shows process steps used by the automaton conversion module to generate FSTs. Figure 13 shows process steps executed by a contextual ranking module in the spelling and grammar checking code to generate a ranked list of alternative words for a misspelled word. Figure 14 shows an FST which includes a compound word which is used by the contextual ranking module to generate the ranked list. Figure 15 shows an FST stored in a morphological dictionary which is used by the contextual ranking module to generate the ranked list. Figure 16 shows an FST generated by a morphology module in the contextual ranking module. Figure 17 shows operation of a grammar application module included in the contextual ranking module. Figure 18 shows an FST generated by the grammar application module in the contextual ranking module. Figure 19 shows an FST generated by a morphological deletion module of the present invention. Figure 20 shows process steps for a word processing system which includes the spelling and grammar checking system of the present invention. Figure 21 shows process steps for a machine translation system which includes the spelling and grammar checking system of the present invention. Figure 22 shows process steps for an optical character recognition system which includes the spelling and grammar checking system of the present invention. Figure 23 shows process steps for a text indexing and retrieving system which includes the spelling and grammar checking system of the present invention. Figure 24 shows a client-server architecture which implements the present invention. Figure 25 shows a text indexing and retrieving system implemented using the architecture shown in Figure 24. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Figure 1 shows a representative embodiment of a computer system on which the present invention may be implemented. As shown in Figure 1, PC 4 includes network connection 9 for interfacing to a network, such as a local area network ("LAN") or the World Wide Web (hereinafter "WWW"), and fax/modem connection 10 for interfacing with other remote sources. PC 4 also includes display screen 11 for displaying information to a user, keyboard 12 for inputting text and user commands, mouse 14 for positioning a cursor on display screen 11 and for inputting user commands, disk drive 16 for reading from and writing to floppy disks installed therein, and CD-ROM drive 17 for accessing information stored on CD-ROM. PC 4 may also have one or more peripheral devices attached thereto, such as scanner 13 for inputting document text images, graphics images, or the like, and printer 19 for outputting images, text, or the like. Figure 2 shows the internal structure of PC 4. As shown in Figure 2, PC 4 includes memory 20, which comprises a computer-readable medium such as a computer hard disk. Memory 20 stores data 21, applications 22, print driver 24, and an operating system 26. In preferred embodiments of the invention, operating system 26 is a windowing operating system, such as Microsoft ® Windows95; although the invention may be used with other operating systems as well. Among the applications stored in memory 20 are word processing programs 41, such as WordPerfect ® and Microsoft ® Word '97; Internet access program 42 (i.e. , a web browser), such as Netscape ® , which includes one or more search engines, such as Infoseek, Lycos, Yahoo! , Excite, AOL NetFind, HotBot, LookSmart, Snap! , and WebCrawler; other text indexing and retrieving programs 44, such as such as programs for accessing Lexis ® -Nexis ® and Westlaw ® databases; machine translation system 46, such as Professional by Systran ® , which translates words and/or documents retrieved, e.g. , from the WWW, from one language (e.g. , French) to another language (e.g. , English); and optical character recognition ("hereinafter "OCR") system 47 for recognizing characters from scanned-in documents or the like. Other applications may be stored in memory 20 as well. Among these other applications is spelling and grammar checking code 49 which comprises computer-executable process steps for performing contextual spelling and grammatical correction in the manner set forth in detail below. Also included in PC 4 are display interface 29, keyboard interface 30, mouse interface 31, disk drive interface 32, CD-ROM drive interface 34, computer bus 36, RAM 37, processor 38, and printer interface 40. Processor 38 preferably comprises a microprocessor or the like for executing applications, such those noted above, out of RAM 37. Such applications, including spelling and grammar checking code 49 of the present invention, may be stored in memory 20 (as noted above) or, alternatively, on a floppy disk in disk drive 16 or a CD-ROM in CD-ROM drive 17. In this regard, processor 38 accesses applications (or other data) stored on a floppy disk via disk drive interface 32 and accesses applications (or other data) stored on a CD-ROM via CD-ROM drive interface 34. Application execution and other tasks of PC 4 may be initiated using keyboard 12 or mouse 14, commands from which are transmitted to processor 38 via keyboard interface 30 and mouse interface 31, respectively. Output results from applications running on PC 4 may be processed by display interface 29 and then displayed to a user on display 11. To this end, display interface 29 preferably comprises a display processor for forming images based on data provided by processor 38 over computer bus 36, and for outputting those images to display 11. Output results from applications, such spelling and grammar checking code 49, running on PC 4 may also be provided to printer 19 via printer interface 40. In this case, processor 38 also executes print driver 24 so as to perform appropriate formatting of the output results prior to their transmission to printer 19. Turning to spelling and grammar checking code 49, this code is comprised of computer-executable process steps for, among other things, detecting a misspelled word in input text, determining a list of alternative words for the misspelled word, ranking the list of alternative words based on a context of the input text, selecting one of the alternative words from the list, and replacing the misspelled word in the text with the selected one of the alternative words. In preferred embodiments, the present invention is operable in an interactive mode, in which the selecting step is performed manually (i.e. , a user selects an alternative word from the list), or in an automatic mode, in which the selecting step is performed automatically (i.e. , without user intervention) based on predetermined criteria. These modes are described in more detail below. Interactive Mode Figure 3 depicts operation of spelling and grammar checking code 49 in the interactive mode, and the various modules (i.e. , computer- executable process steps) included therein. To begin, text 50 is input into the spelling and grammar checking system. Next, in step 51, a misspelled word in the text is detected by a spell-checking module (not shown). In preferred embodiments of the invention, step 51 detects misspelled words by comparing each word in the input text to a dictionary database and characterizing a word as misspelled when the word does not match any words in the dictionary database. To this end, step 51 also checks for proper placement of accent marks and/or diacritic marks in the input word. In cases where these marks are improperly placed, step 51 characterizes the word as misspelled. Following step 51, the misspelled word is passed to spelling suggestion module 52. Spelling suggestion module 52 suggests "out-of- context" corrections for the misspelled word. That is, spelling suggestion module 52 determines a list of correctly-spelled alternative (or "replacement") words for the misspelled word without regard to the context in which the misspelled word appears in input text 50. A detailed description of the operation of spelling suggestion module 52 is provided below. For now, suffice it to say that spelling suggestion module 52 determines this list of alternative words by inserting, deleting, replacing, and/or transposing characters in the misspelled word until correctly-spelled alternative words are obtained. Spelling suggestion module 52 also identifies portions (e.g. , characters) of the misspelled word which sound substantially similar to portions of correctly-spelled alternative words in order to obtain additional correctly-spelled alternatives words. Once all alternative words have been determined, spelling suggestion module 52 ranks these words in a list based, e.g. , on a number of typographical and/or phonetic modifications that must be made to the misspelled word in order to arrive at each alternative word. List 54 of alternative words, which was output by spelling suggestion module 52, is then passed to automaton conversion module 55, along with original text 50. A detailed description of the operation of automaton conversion module 55 is provided below. For now, suffice it to say that automaton conversion module 55 converts text 50 and list 54 into an input finite state machine (hereinafter "FSM"), such as a finite state transducer (hereinafter "FST") or a finite state automaton (hereinafter "FSM"), having a plurality of arcs. Each arc in the input FSM includes an alternative word and a rank (e.g. , a weight, a probability, etc.) associated with each alternative word. This rank corresponds to a likelihood that the alternative word, taken out of context, comprises a correctly-spelled version of the original misspelled word. In this regard, the concept of FSTs is described in Roche, Emmanuel, "Text Disambiguation by Finite-State Automata: An Algorithm and Experiments on Corpora", Proceedings of the Conference, Nantes (1992), Roche, Emmanuel and Schabes, Yves, "Introduction to Finite-State Language Processing", Finite-State Language Processing. (1997), Koskenniemi, Kimmo, "Finite-State Parsing and Disambiguation", Proceedings of the Thirteenth International Conference on Computational Linguistics, Helsinki, Finland (1990), and Koskenniemi et al. "Compiling and using Finite-State Syntactic Rules", Proceedings of the Fifteenth International Conference on Computational Linguistics. (1992). The contents of these articles are hereby incorporated by reference into the subject application as if set forth herein in full. To summarize, FSTs are FSMs have a finite number of states with arcs between the states. These arcs have one input and one or more outputs. Generally speaking, an FST functions as a particular method for mapping inputs to outputs. The present invention uses FSTs with weights, such as the those described in Pereira et al. "Weighted Rational Transductions and Their Application to Human Language Processing", ARPA Workshop on Human Language Technology (1994). The contents of this article is hereby incorporated by reference into the subject application as if set forth herein in full. Returning to Figure 3 , in preferred embodiments of the invention, automaton conversion module 55 also identifies predetermined words in the input text which are commonly confused, but which are correctly spelled. Examples of such word are principal and principle and who and whom. Specifically, in these embodiments of the invention, automaton conversion module 55 identifies such words by reference to a pre- stored database, and then either adds such words to the FSM or creates a new FSM specifically for these words. In other embodiments of the invention, these commonly-confused words may be identified by spelling suggestion module 52, characterized as misspelled words by virtue of their identification, and then processed in the same manner as misspelled words. In either case, the output of the automaton conversion module 55 is the same, i.e. , an FSM containing arcs with alternative words for a misspelled word. Automaton conversion module 55 then transmits input FSM 56 (which in preferred embodiments is an FST) to contextual ranking module 57. Contextual ranking module 57 ranks alternative words in input FSM 56 by taking into account the context (e.g. , grammar, parts-of-speech, etc.) of text 50. In brief, contextual ranking module 57 generates a second FSM for text 50 and the alternative words in accordance with one or more of a plurality of predetermined grammatical rules. This second FSM is comprised of a plurality of arcs which include the alternative words and ranks (e.g. , weights) associated therewith, where a rank of each alternative word corresponds to a likelihood that the alternative word, taken in grammatical context, comprises a correctly-spelled version of the misspelled word. Contextual ranking module 57 then combines corresponding ranks of input FSM 56 and the second FSM (e.g. , contextual ranking module 57 adds weights from respective FSMs) so as to generate an "in-context" ranking of the alternative words. Then, contextual ranking module 57 outputs a list 59 of alternative words for the misspelled word, which are ranked according to context. A more detailed description of the operation of contextual ranking module 57 is provided below. Ranked list 59 of alternative words, which was generated by contextual ranking module 57, is then displayed to a user, e.g., on display screen 11. In step 60, the user can then manually select (using, e.g. , mouse 14, keyboard 12, etc.) one of the alternative words from ranked list 59. Thereafter, the selected one of the alternative words (i.e. , selected alternative 61) is provided to replacement module 62, along with original text 50. Replacement module 62 replaces the misspelled word in text 50 with user-selected alternative word 61, and then outputs corrected text 63. Automatic Mode Figure 4 shows the operation of the automatic mode of the present invention. More specifically, figure 4 depicts operation of spelling and grammar checking code 49 in the automatic mode, and the various modules (i.e. , computer-executable process steps) included therein. Those modules which are identical to modules described above with respect to the interactive mode are described only briefly. To begin, text 50 is input to spell checking module 64. Spell checking module 64 is identical to that described above in the interactive mode, except that, in this mode, spell checking module 64 searches through all of text 50 in order to detect all misspelled words. Which mode (i.e., interactive or automatic) spell checking module 64 operates in is set beforehand, e.g., in response to a user input. Once all misspelled words have been detected, spell checking module 64 outputs text 66 with the incorrectly- spelled words appropriately identified. Next, text 66, i.e. , the text with the incorrectly spelled words identified, is provided to spelling suggestion module 52. Spelling suggestion module 52 is identical to that described above, except that, in this mode, spelling suggestion module 52 determines and outputs a list of correctly-spelled alternative (or "replacement") words for every misspelled word in text 50, rather than for just one misspelled word. Which mode (i.e. , interactive or automatic) spelling suggestion module 52 operates in is set beforehand, e.g. , in response to a user input. As before, spelling suggestion module 52 outputs a list of "out-of-context" alternative words to automaton conversion module 55. Automaton conversion module 55 is identical to that described above, except that, in this mode, automaton conversion module 55 generates an FSM 56 (see above) for each misspelled word in input text 50. These FSMs are then transmitted to contextual ranking module 57. Contextual ranking module 57 is identical to that described above, in that it generates a second FSM for input text 50 based on a plurality of predetermined grammatical rules and combines this second FSM with FSM 56 generated by automaton conversion module 55 in order to provide a contextually- ranked list 59 of the alternatives for each misspelled word in text 50. Thereafter, list 59 is provided from contextual ranking module 57 to best suggestion selection module 60. Best suggestion selection module 60 selects the "best" alternative for each misspelled word, replaces each misspelled word in the text with its corresponding best alternative, and outputs corrected text 61, which includes these best alternatives in place of the misspelled words. In preferred embodiments of the invention, best suggestion selection module 60 selects each best alternative based on list 59 without any user intervention. For example, best suggestion module 60 may select the first, or highest, ranked alternative word in list 59, and then use that word to correct the input text. Spelling Suggestion Module In brief, spelling suggestion module 52 determines and outputs alternative words for a misspelled word in input text 50. In preferred embodiments of the invention, these alternative words are not ranked according to context, but rather are ranked based on the number of typographical changes that must be made to the misspelled word to arrive at an alterative word. To this end, spelling suggestion module 52 is comprised of computer-executable process steps to store one or more lexicon FSTs (in general, FSMs), where each of the lexicon FSTs includes plural reference words and a phonetic representation of each reference word, and to generate an input FST (in general, an FSM) for a misspelled word, where the input FST includes the misspelled word and a phonetic representation of the misspelled word. Spelling suggestion module 52 also includes computer-executable process steps to select one or more reference words from the lexicon FSTs based on the input FST, where the one or more reference words substantially corresponds to either a spelling of the misspelled word or to the phonetic representation of the misspelled word. In more detail, Figure 5 shows process steps comprising spelling suggestion module 52, together with sub-modules included therein. To begin, word 70 is input from a spell-checking module (see. e.g. , Figure 4). Pronunciation conversion module 73 then converts input word 70 into input FST 71. The details of the operation of pronunciation conversion module 73 are provided below. Input FST 71 represents the spelling and pronunciation of input word 70. More specifically, each arc of input FST 71 includes a pair of characters dp, where c is a character in input word 70 and p is a phonetic symbol representing the pronunciation of character c. Figure 6 shows such an input FST for the word asthmatic (with its pronunciation azmatic). Figure 7 shows an example of another input FST, this time for the misspelled word cati (with its pronunciation c@ti). The phonetic symbol "-" shown in Figure 6 is used to represent a character which is not pronounced. In this regard, although the present invention mostly employs standard characters to illustrate pronunciation, the invention is not limited to using such characters. In fact, any convention can be adopted. Lexicon FST 74 is preferably stored in a single memory, and comprises one or more lexicon FSTs (or FSMs, in general) which have been generated by the process steps of the present invention. Each of these lexicon FSTs includes plural reference words (e.g. , English-language words, French-language words, German-language words, etc.) and a phonetic representation of each reference word. An example of a lexicon FST is shown in Figure 8. This FST represents the following word/pronunciation pairs: cacti/k@ktA, caws/kc-s, face/fes-, fire/fAr-, and foci/fosA. Spelling FSA 76 comprises an additional FSM which has been generated by the process steps of the present invention. Specifically, spelling FSA 78 includes a plurality of states, the states comprising at least states of lexicon FST 74 and states of input FST 71. Spelling FSA 76 is used to select one or more reference words from lexicon FST 74. These selected reference words comprise the alternative words for output by spelling suggestion module 52. In more detail, each state of spelling FSA 76 is identified by a quadruple (i, I, t, cost) , in which the first element i is a state in input FST 71 and records which portion of input word 70 has been already processed; the second element / is a state in lexicon FST 74 which records words that are potential alternatives for the input word; the third element t indicates whether a character transposition has occurred in the input word (e.g. , rluer to ruler, in which the / and u have been transposed) and thus whether characters preceding the transposed characters must be re-examined; and the fourth element cost is the cost associated with a current suggested alternative to input word 70, meaning an indication of the likelihood that the current suggested alternative is actually the correct spelling of input word 70. In this regard, in preferred embodiments of the invention, the lower the cost of a state in spelling FSA 76, the more likely that state represents a path to the correct spelling of input word 70. Figure 9 shows a representative embodiment of spelling FSA 76. As shown in Figure 9, the arcs of spelling FSA 76 are labeled with characters which represent suggested alternatives for input word 70. To begin operation, spelling FSA 76 is initialized to state (i=0,l=0,t=0,cost=0) , which represents the fact that the process starts at the initial state 0 in input FST 71, and at initial state 0 in lexicon FST 74, with no character transpositions (represented by t=0) and a 0 cost. Thereafter, each state of spelling FSA 76 is processed. Of course, the invention can be modified to process less than all states of spelling FSA 76. To this end, spelling suggestion module 52 includes state selection module 77. State selection module 77 selects which states of spelling FSA 76 are to be processed. For example, state selection module 77 may select states having lowest costs, so as to assure that potentially best solutions are processed first. Other embodiments of the present invention, of course, may use a different strategy. Once state selection module 77 has selected a state (i, I, t, cost) to be processed, this state is provided as input to each of following modules: character identity module 80, phonetic identity module 81, character insertion module 82, character deletion module 83, character replacement module 84, character transposition module 85, and character transposition completion module 86. Each of these modules process the current state (i, I, t, cost) 78 of spelling FSA 76 (as set by state selection unit 77), and may also add new states to spelling FSA 76. In brief, character identity module 80 determines whether characters of a reference word in lexicon FST 74 match characters of word 70 in input FST 71. Phonetic identity module 81 determines whether characters of the reference word are pronounced the same as characters of the input word. Character insertion module 82 determines whether a character inserted in the input word causes at least part of the input word to match at least part of the reference word. Character deletion module 83 determines whether a character deleted from the input word causes at least part of the input word to match at least part of the reference word. Character replacement module 84 replaces characters in the input word with characters in the reference word in order to determine whether at least part of the input word matches at least part of the reference word. Character transposition module 85 changes the order of two or more characters in the input word and compares a changed character in the input word to a corresponding character in the reference word. Finally, character transposition completion module 86 compares characters in the input word which were not compared by character transposition module 85 in order to determine if at least part of the input word matches at least part of the reference word. In more detail, character identity module 80 checks whether there is a word in lexicon FST 74 which starts at state 1 and which has a next character that is the same as the next character in input FST 71 at state i. Given a current spelling FSA state of (i,l,t,cost), for all outgoing arcs from state / in lexicon FST 74 going to a state ' and labeled with pair c/p (where c is a character and p is a pronunciation of that character), and for all outgoing arcs from state i in input FST 71 going to state ' and labeled with the pair d ' (where c is a character and p ' is a pronunciation of the character) , character identity module 80 creates an arc in spelling FSA 76 from state (i,l,t,cost) to a newly-added state (i',V,0,cost), and labels that arc with character c. Phonetic identity module 81 checks whether there is a word in lexicon FST 74 starting at state / whose next character is pronounced the same as the next character in input FST 71 at state i. For this processing, the phonetic representations of characters are processed. That is, given a current spelling FSA state of (i,l,t,cost), for all outgoing arcs from state / in lexicon FST 74 going to a state /' and labeled with the pair dp (where c is a character and p is a pronunciation of that character), and for all outgoing arcs from state i in input FST 71 going to state ' and labeled with the pair c '/p (where c ' is a character and p is a pronunciation of the character), phonetic identify module 81 creates an arc in spelling FSA 76 from state (i,l,t,cost) to a newly-added state (i',l',0,cost+phonetic_identity_cost), and labels that arc with character c. This newly-added state has its cost increased by a predetermined cost, called phonetic dentity _cost , which has a pre-set value that is associated with the fact that the pronunciation of a current character in input FST 71 is identical to the pronunciation of the current character in lexicon FST 74 even though the characters are different. Character insertion module 82 inserts a character from lexicon FST 74 into input word 70 in input FST 71. More specifically, given a current spelling FSA state of (i,l,t,cost), for all outgoing arcs from state / in lexicon FST 74 going to a state /' and labeled with the pair dp (where c is a character and p is a pronunciation of that character), character insertion module 82 creates an arc in spelling FSA 76 from state (i,l,t,cost) to state (i, l',0 insertion _cost), and labels that arc with character c. This newly-added state has its cost increased by a predetermined cost, called insertion _cost, which has a pre-set value that is associated with the fact that a character has been inserted into word 70 in input FST 71. Character deletion module 83 deletes a character from input word 70 in input FST 71. More specifically, given a current spelling FSA state of (i,l,t,cost), for all outgoing arcs from state i in input FST 71 going to a state i ' and labeled with the pair dp (where c is a character and p is a pronunciation of that character), character deletion module 83 creates an arc in spelling FSA 76, which is labeled with "empty character" ε from state (i,l,t,cost) to a newly added state (V, 1,0, cost + deletion _cost). This newly added state has a cost that is increased by a predetermined cost, called deletion _cost, which has a pre-set value that is associated with the fact that a character has been deleted from input word 70 in input FST 71. Character replacement module 84 replaces a next character in input word 70 with a next character in lexicon FST 74. More specifically, given a current spelling FSA state of (i, I, t, cost) , for all outgoing arcs from state / in lexicon FST 74 going to a state /' and labeled with the pair dp (where c is a character and /? is a pronunciation of that character), and for all outgoing arcs from state in input FST 71 going to a state ' and labeled with the pair c '/p ' (where c ' is a character and p ' is a pronunciation of that character), character replacement module 84 creates an arc in spelling FSA 76 to a newly added state (i ', I ',0, cost + replacement _cost), and labels that arc with character c from state (i, I, t, cost) . This newly-added state has its cost increased by a predetermined cost, called replacement _cost, that has a pre-set value and that is associated with the fact that a character has been replaced by another character in input word 70. Character transposition module 85 interchanges the order of two consecutive characters in input word 70, and checks the validity of the next character while remembering the original order of the characters. More specifically, given a current spelling FSA state of (i,l,t,cost), for all outgoing arcs from state in input FST 71 going to a state il and labeled with the pair cl/pl (where cl is a character and pi is a pronunciation of that character), for all outgoing arcs from state il in input FST 71 going to a state 12 and labeled with the pair c2/p2 (where c2 is a character and p2 is a pronunciation of that character), and for all outgoing arcs in lexicon FST 74 going from state / to state / ' labeled with the pair c2/p ' (where c2 is a character and /? ' is a pronunciation of that character), character transposition module 85 creates an arc in spelling FSA 76 from state (i,l,t,cost) to a newly-added state (i2,V , cl ,cost+transposition_cost), and labels that arc with character c2. This newly-added state has its cost increased by a predetermined cost, called transposition _cost, which has a value that is pre-set and that is associated with the fact that two characters have been transposed in input word 70. Character transposition completion module 86 completes the transposition of two characters that was started by character transposition module 85. More specifically, given a current spelling FSA state (i,l,t,cost), where t is not zero (indicating that character transposition has occurred), for all outgoing arcs in lexicon FST 74 going from state / to state /' labeled with the pair tip ' (where t is a character and p is a pronunciation of that character), character transposition completion module 86 creates an arc in spelling FSA 76 from the state (i, I, t, cost) to a newly- added state (i,l',0,cost+transposition_ completion _cost) , and labels that arc with the character t. This newly- added state has its cost increased by a predetermined cost, called transposition _completion_cost, which has a value that is pre-set and that is associated with the fact that the second of the transposed characters has been read. The following describes operation of some of the foregoing modules in an actual example. More specifically, with reference to Figures 7, 8 and 9, when input FST 71 (see Figure 7) moves from state 0 (i) to state 1 ( '), and lexicon FST 74 (see Figure 8) moves from state 0 (/) to state 2 (/'), state 88 is created in spelling FSA 76 (see Figure 9), which has a state of (1,2,0,0) or (i ',l',0,cost) and an arc with the character c. In this example, there is no character transposition or cost, since character identity module 80 was used (i.e. , there is a "c" in the arcs of both input FST 71 and lexicon FST 74). Accordingly, at state 88, spelling FSA 76 has no cost. Following this processing (i.e. , if state selection module 77 selects the following additional states), when input FST 71 moves from state 1 ( ) to state 2 ( '), and lexicon FST 74 moves from state 2 (I) to state 3 (/'), state 89 is created in spelling FSA 76, which has a state of (2,3,0,0) or (i',l',0,cost) and an arc with the character a. Again, there is no character transposition or cost, since character identity module 80 was used. Next, input FST 71 remains at state 2, while lexicon FST 74 moves from state 3 to state 4, thereby creating state 90 in spelling FSA 76, which has a state of (2,4,0, 1). In this case, an additional character, namely a c, is added in lexicon FST 74 which is not present in input FST 7, i.e. , character insertion module 82 was used. As a result, a cost of 1 is added to state 90 of spelling FSA 76. Next, input FST 71 moves from state 2 (0 to state 3 ( '), and lexicon FST 74 moves from state 4 (/) to state 5 (/'), thereby creating state 91 in spelling FSA 76, which has a state of (3,5,0, 1) or (i',l',0,cost) and an arc with the character t. In this case, there is no character transposition or additional cost, since character identity module 80 was used. Finally, input FST 71 moves from state 3 (i) to end state 4 ( ') (marked by double circle 93), and lexicon FST 74 moves from state 5 (0 to end state 13 (/') (marked by double circle 94), thereby generating state 95 in spelling FSA 76, which has a state of (4, 13,0, 1) or (i ',l',0,cost) and an arc with the character i. Again, there is no character transposition or additional cost, since character identity module 80 was used. Similar processing is also performed for the other states shown in lexicon FST 74 to create additional states 97 to 101 with character deletion module 83 being used between states 97 and 99, and with an ε in arcs between those states indicating that a character has been deleted from the word in input FST 71. Once this processing is finished, as shown in Figure 9, the cost of state 101 (i.e. , 4) is higher than the cost of state 95 (i.e. , 1). Accordingly, the word corresponding to the path of state 95 (in this case, cacti) is ranked by spelling suggestion module 52 higher than the word corresponding to the path of state 101 (in this case, caws) . At this point, it is noted that although spelling suggestion module 52, and the rest of the invention for that matter, is described with respect to a word in an input text sequence comprised of plural words, the spell-checking aspect of the invention can be used equally well with a single-word input. Of course, the grammar checking aspects of the invention would not apply in this instance. Accordingly, those modules shown in Figures 2 and 3 which deal solely with grammar checking would simply be skipped when checking a single- word input. Once all states of input FST 71 and lexicon FST 74 have been processed in the foregoing manner, as determined in block 103 of Figure 5, the spelling FSA generated by the process is provided to path enumeration module 104. Path enumeration module 104 analyzes the spelling FSA in order to associate words therein with appropriate costs, and outputs list 105 of suggested alternative words with their associated costs (e.g. , weight). Thereafter, processing ends. Pronunciation Conversion Module As noted above, pronunciation conversion module 73 converts input word 70 into input FST 71. In general, pronunciation conversion module 73 converts any word, whether correctly spelled or misspelled, into an input FST which includes a phonetic representation of the input word, together with the input word. As noted above, Figure 6 shows an input FST for the word asthmatic with its pronunciation azmatic. Pronunciation conversion module 73 utilizes a pre-stored phonetic dictionary of words, in which a pronunciation of each character of a word is associated with a phonetic symbol which represents the pronunciation of that character in the context of a word. In order to associate to each character of an input word with a pronunciation, pronunciation conversion module 73 reads the input word from left to right and finds the longest context in the phonetic dictionary which matches the input word. Pronunciation conversion module 73 then transcribes that longest match with phonetic characters until no characters in the input word are left unpronounced. The output is represented as an FST (see, e.g. , Figure 6), in which each arc is labeled with a pair dp. Automaton Conversion Module Returning to Figure 3 , in brief, automaton conversion module 55 is comprised of computer-executable process steps to generate an FSM for input text 50, which includes a plurality of arcs. Each of these arcs includes an alternative word provided by spelling suggestion module 52 and a corresponding rank (e.g. , weight) of that word. As noted above, a rank (e.L , a weight) of each alternative word corresponds to a likelihood that the alternative word, taken out of grammatical context, comprises a correctly-spelled version of a misspelled word. The ranks may be derived from the cost provided by spelling suggestion module 52. In more detail, in preferred embodiments of the invention, automaton conversion module 55 generates an FST; although an FSM may be used in the present invention as well. For the sake of brevity, however, the invention will be described with respect to an FST. In this regard, such an FST comprises a finite-number of states, with arcs between the states. Each arc is labeled with a pair of symbols. The first symbol in each pair is an alternative word to the misspelled word found in text 50. The second symbol of each pair is a number representing a rank for that word. As noted above, these rankings are determined based on the number of character transpositions, deletions, additions, etc. that must be performed on the misspelled word in order to arrive at each alternative word. Figure 10 illustrates an FST generated by automaton conversion module 55 for the input text he thre a ball. In this text, the word thre is misspelled (as determined by the spell-checking module). Accordingly, spelling suggestion module 52 provides the following alternative words to automaton conversion module 55: then, there, the, thew and three. Of course, the number and identity of these alternative words may vary depending upon the exact implementation of spelling suggestion module 52. In this embodiment of the invention, however, the alternative words are limited to those shown above. As shown in Figure 10, ranks associated with the alternative words are negative, and correspond to a number of typographical changes that were made to the original word thre to arrive at each alternative word. For example, then has an associated weight of -2 because then can be obtained from thre by deleting the letter r and then inserting the letter n from thre. Figure 11 shows another example of an FST generated by automaton conversion module 55. In the example shown in Figure 11, the FST is generated for the text He left the air baze. In this text, the incorrectly spelled word is baze, and the "out-of-context" alternative words provided by spelling suggestion module 52 are baize, bass, baba, base, bade. As noted above, the second symbol of each arc in the FST comprises a ranking, in this case a weight, for the alternative word on that arc. The higher the weight, the more likely the alternative word associated with that weight is the correct replacement word for the misspelled word. In the examples shown in Figures 10 and 11, suggested alternative words have negative weights which reflect the number of typographical and phonetic changes were made to the original misspelled word. In this regard, as shown in Figure 11, the alternative words baize, babe, base and bade have the same weight, since each of these words differs from the misspelled word base by the same number of typographical changes. Figure 12 shows computer-executable process steps in automaton conversion module 55 for generating such an FST. More specifically, in step S1201, text 50 is input into automaton conversion module 55, together with alternative words from spelling suggestion module 52. In step S1202, variables are initialized. Specifically, in this example, word number i is set to 1 so that, initially, the FST has a single state labeled . Also, the variable n is set to the number of words in the input text. Thereafter, step S1203 determines whether the i* input word in the text is misspelled and, in preferred embodiments of the invention, if the i th word is one of a plurality of predetermined words that are commonly confused. This aspect of automaton conversion module 55 is described in more detail below. If step S1203 determines that the i Λ input word is misspelled, step SI 204 generates a new state labeled i+1 for each of the alternative words provided by spelling suggestion module 52. Step S1204 also adds a transition from state i to state i + 1. This transition is labeled with an alternative word and with a ranking (e.g. , a negative weight). If, on the other hand, step S1203 determines that the i th input word is not misspelled, step S1205 creates a new state i+1 and a transition from state i to state i+1. This transition is labeled with the i th word and has a weight of zero. Thereafter, in step SI 206, current state is increased by one, and processing proceeds to step S1207. If step S1207 determines that a current state i is less than the number of words n, meaning that there are words in the input text still to be processed, flow returns to step S1203. If i equals n, processing ends, and the FST generated by steps S1201 to S1207 is output in step S1208. As noted above, in preferred embodiments of the invention, automaton conversion module 55 may characterize words which are correctly spelled, but which are commonly confused, as misspelled words. This is done in order to flag these words as possible candidates for the grammar correction process which is described in more detail below. Examples of such words include who and whom. That is, these words are often misused, such as in the sentence / need an assistant who I can trust. Similarly, homophones, such s principal and principle are often confused. Appendix B shows a short lists of such words. Of course, this list is merely representative, and, in the actual invention, the list is much more extensive. This list is preferably stored in a database, e.g. , in memory 20, and can be updated or modified via, e.g. , fax/modem line 10. Alternatively, this list may be accessed from a remote location via network connection 9. Thus, automaton conversion module 55 identifies words which are often misused or confused based on such a list, and treats these words in the same manner as misspelled words provided by spelling suggestion module 52. That is, such words are included in arcs in the FST generated by automaton conversion module 55. Contextual Ranking Module Returning to Figure 3, in brief, contextual ranking module 57 includes computer executable process steps to generate a second FST for the input text and the alternative words in accordance with one or more of a plurality of predetermined grammatical rules (with the first FSM being FST 56 described above) . The second FST has a plurality of arcs therein which include the alternative words and ranks (e.g. , weights) associated therewith. In this second FST, a weight of each alternative word corresponds to a likelihood that the alternative word, taken in grammatical context, comprises a correctly-spelled version of the misspelled word. Contextual ranking module 57 also includes computer-executable process steps to add corresponding weights of the first FST and the second FSM, to rank the alternative words in accordance with the added weights, and to output a list of the alternative words ranked according to context. In more detail, Figure 13 shows computer-executable process steps in contextual ranking unit 57, together with executable modules included therein. To begin, FST 56 is input. As noted above, FST 56 was generated by automaton conversion module 55, and includes alternative words (e.g. , misspelled words, commonly-confused words, etc.) ranked out of context. As also noted above, an example of such an FST is shown in Figure 11 for the input text he left the air baze. As shown in Figure 13, FST 56 is provided to compound words and lexical phrases module 110. Compound Words And Lexical Phrases Module Compound word and lexical phrases module 110 identifies words which may comprise part of a predetermined list of compound words (i.e. , a word comprised of two separate words), and also adds these words as arcs in FST 56. By way of example, in the sentence Pilots practice with flight stimulators, the word stimulators is not necessarily misspelled, but is incorrect in context. That is, the typist meant to type flight simulators, but accidentally included an extra t in simulators. Compound words and lexical phrases module 110 compares the word stimulators to a pre-stored database of compound words. In a case that an input word, in this case stimulators, is similar to a word in a compound word (as measured, e.g. , by a number of typographical changes between the input word and a word in a compound word, in this case simulators), compound words and lexical phrases module 110 includes the compound word as an alternative word in an arc of FST 56, together with a single rank associated with the compound word. In the present invention, a database of compound words is preferably pre-stored, e.g. , in memory 20. In preferred embodiments of the invention, each of the compounds words in the database is associated with a part-of-speech that defines a syntactic behavior of the compound word in a sentence. For example, a noun- noun compound, such as air base may be stored in the database and defined therein as a noun ("N"). Another example of a compound word is commercial passenger flight , which is defined in the database as a noun ("N"). Similarly, the phrase according to will be defined in the database as a preposition ("Prep"). As borne out in the examples provided above, in the database, each compound word or phrase has a single part-of-speech (e.g. , part-of-speech tag "N", "Adv" , etc.) associated therewith. Moreover, these words and phrases exhibit very little morphological or syntactic variation. For example, according to exhibits no morphological or syntactic variation. Similarly air base can be pluralized (air bases), but little else. Appendix C shows a list of representative compound words and phrases, together with their associated parts-of-speech, that are included in the database that is used by compound words and lexical phrase module 110. In preferred embodiments of the invention, compound words and lexical phrases module 110 also adds, to FST 56, a part-of-speech tag for each compound word or phrase. In addition, compound words and lexical phrases module 110 also adds a relatively large weight to arcs containing potential compound words, reflecting the fact a word may, more likely than not, be a compound word. For the example FST shown in Figure 11 , compound words and lexical phrases module 57 produces the FST shown in Figure 14. That is, compound words and lexical phrases module 110 adds a new arc labeled "air base#NOUN/9" from state 3 to state 5 in Figure 14. As shown in the figure, this arc passes over both the word air and the five alternative words (baize, bass, babe, base, and bade). This new arc treats "air base" as if it were one word acting as a noun with relatively high weight of 9. Returning to Figure 13, FST 111 output by compound words and lexical phrase module 110 is provided to morphology module 112. Morphology Module Morphology module 112 adds all possible morphological analyses of each word to FST 111. This morphological analysis is performed using a pre-stored morphological dictionary of words. In preferred embodiments of the invention, this morphological dictionary is represented as a collection of small FSTs, each representing a possible morphological analysis of each word. Weights in such FSTs correspond to a relative likelihood that a word is a particular part-of-speech. For example, for the word left, FST 114 shown in Figure 15 is stored in the morphological dictionary. As shown in Figure 15, each path of the FST has a length of length three, with a first element being the initial word (in this case left) with a corresponding weight, the second element being a part-of-speech tag with a corresponding weight, and the third element being a root form of the initial word with a corresponding weight. Thus, FST 114 shown in Figure 15 indicates that left can be an adjective ("ADJ") having a base form of left and a weight of 5, a noun ("N") having a base form of left and a weight of 1, a verb in past participle form ("Vpp") having a base form of leave and a weight of 4, or a verb in past tense form having a base form of leave and a weight of 3. In the present invention, a weight of a particular path through an FST is computed as the sum of the weights of each of the arcs in the FST. For example, in the FST shown in Figure 15, the path from states 1 to 2 to 4 to 5, in which the word left is a verb in past participle form of the base verb leave, has a weight of 4 (i.e. , 0+4+0 = 4). Morphology module 112 replaces every arc in the FST which does not represent a compound word or a lexical phrase with an FST from the morphological dictionary. In addition, for each arc corresponding to a compound word or lexical phrase, such an arc is replaced by three arcs, where a first arc includes the compound word or lexical phrase, the second arc includes the part-of-speech of the compound word or lexical phrase, and the third arc also includes the compound word or lexical phrase. Thus, given as input the FST shown in Figure 14, morphology module 112 outputs FST 116 shown in Figure 16. Grammar Application Module Returning to Figure 13, FST 116 produced by morphology module 112 is provided to grammar application module 117. In brief, grammar application module 117 comprises computer-executable process steps to receive a first FST 116 (or, in general, an FSM) from morphology module 112, where the first FST includes alternative words for at least one word in the input text and a weight (or, in general, a rank) associated with each alternative word. Grammar application module 117 then executes process steps to adjust the ranks in the first FST in accordance with one or more of a plurality of predetermined grammatical rules. Specifically, grammar application module 117 does this by generating a second FST (or, in general, an FSM) for the input text based on the predetermined grammatical rules, where the second FST includes the alternative words and ranks associated with each alternative word. The ranks in the second FST are then combined with the ranks in the first FST in order to generate a "contextual" FST in which weights of words therein are adjusted according to grammar. In more detail, Figure 17 depicts operation of grammar application module 117. As shown in Figure 17, grammar application module 117 includes weight application module 119. Weight application module 119 inputs FST 116 which was generated by morphology unit 112, together with grammar FST 120 (described below) which includes corresponding weights. In this regard, grammar FST 120 comprises general grammatical structures of a language, such as French, English, Spanish, etc., together with predetermined phrases in that language. Grammar FST 120 has substantially the same format as parts of input FST 116. Every path in grammar FST 116 has a length which is a multiple of three. Each arc therein includes three elements, with a first element comprising a reference word with a corresponding weight, a second element comprising a part-of-speech tag with a corresponding weight, and a third element comprising a root form of the reference word with a corresponding weight. A detail description of the construction of grammar FST 120 is provided below. Weights application module 119 combines (e.g. , adds) weights of input FST 116 and grammar FST 120 in order to produce a combined FST 121 in which weights therein are adjusted according to grammatical rules. More specifically, for each path from an initial state to a final state of grammar FST 120, weights application module finds a corresponding path in input FST 116. Thereafter, weights application module 119 replaces weights of input FST 116 with the combined weights of input FST 116 and grammar FST 120. By doing this, weights application module 119 reinforces paths in input FST 116 which are also found in grammar FST 120. For example, grammar FST 120 might include a path which indicates that a singular noun precedes a verb in the third person. Such a path can be used to reinforce portions of input FST 116 where a noun precedes a verb in third person. Figure 18 is an example of FST 121 which was produced by grammar application module 117 from the FST shown in Figure 16. As shown in Figure 18, the weights on the path 125 corresponding to he left, where he is analyzed as a pronoun and left is analyzed as a verb in past tense, have been increased by weights application module 119. The weight for this path has been increased since it matches the subject-verb agreement rule, which indicates that a pronoun can be the subject of a verb. This and other rules are described in more detail below. Construction Of Grammar FST Grammar FST 120 (see Figure 17) is constructed from contextual grammatical rules, examples of which are set forth in Appendix A. In the present invention, there are two types of such rules: application rules and definition rules. Application rules indicate which rules must be applied, whereas definition rules define the rules themselves. Taking application rules first, application rules comprise items which do not contain an "equals" sign. For example, the application rule "*NP/0" indicates that a noun phrase rule (i.e. , a rule stating that all nouns must be preceded by determiners, such as a, an, this, etc.) must be applied with a weight of 0. The weight of 0 means that, in the event that words in input FST 116 comply with this rule, a value of 0 is added to the weight of the matching words in input FST 116. A "*" before an item in a rule, such as "*NP/0", indicates that the item is defined elsewhere by a definition rule. When there is no "*" before an item, the item refers to a word which can be specified with the word itself, its root form, and its part of speech. For example, the application rule there,Adv/10 is;be:V3sg/20 indicates that the word there should be matched as an adverb, followed immediately by the word be in the third person singular, i.e. , is. If a match is found, meaning that words in input FST 116 comply with this rule, weights 10 and 20 are added to weights of the matching words in input FST 116. Items to be matched by application rules can have any of the following formats: ♦ SYMBOL./NUMBER where SYMBOL is any symbol, and NUMBER is a weight; * indicates that the SYMBOL is defined elsewhere in the grammar. WORD,POS/NUMBER where WORD is a word, POS a part- of-speech, and NUMBER is a weight; the root form is not specified and matches any root form. WORD;ROOT:POS/NUMBER where WORD is a word, ROOT its root form, POS its part-of-speech, and NUMBER is a weight. :POS/NUMBER where POS is a part-of-speech and NUMBER a weight; in this item, the word and its root form are not specified and match any word and root form. Examples of some of the foregoing items are shown in the FST of Figure 10. Definition rules include an "equal" sign. The left side of the equal sign includes an item of the form "*SYMBOL"; and the right side of the equal sign includes any sequence of items. For example, *NP3S - *ADJP/0 :N/10 is a definition rule. In this example, *NP3S indicates that a noun phrase in the third person singular is formed by an adjective (*ADJP/0) and a noun (:N/10). In a case that words in input FST comply with this rule, a noun in such words is incremented by 10 (from the 10 in " :N/10") and the adjective is not incremented (from the 0 in "*ADJP/0"). In the present invention, the grammatical rules are non- recursive, meaning that at no point does a symbol refer to itself. As a result, the rules can be combined into a grammar FST for comparison with input FST 116. Specifically, to generate grammar FST 120, items with a "*" preceding them are recursively replaced by their definitions. Next, the grammatical rules are converted into an FST by concatenating an FST of each obtained item. Application rules are then used to define paths from an initial state to a final state in the constructed FST. In addition to general grammatical rules (such as subject-verb agreement rules), the present invention also includes specific grammatical constructions in the grammar FST. For example the application rule too,Adv/10 ,A/40 to, Prep/10 corresponds to the construction "too ADJECTIVE to", as in the sentence "He is too young to drive" . Another example of such a construction is: there,Adv/10 is;be:V3sg/20, which is used for sentences such as "There is a car in his parking space". Grammar FST 120 also includes auxiliary verb groups ("*VG"), examples of which are also shown in Appendix A. Post-Grammar Application Module Processing Returning to Figure 13, FST 121 generated by grammar application module 117 (see, e.g. , Figure 18) is output to morphology deletion module 130. Morphology deletion module 130 deletes unnecessary morphological information from the FST, such as part-of-speech information. Morphology deletion module 130 also reorganizes weights in the FST so that the weights correspond to possible alternatives to a misspelled word. An example of such an FST is shown in Figure 19, in which only words and weights remain. As shown in Figure 19, base 132 has a weight of 14, since morphology deletion module 130 moved the weight of the compound "air base" to "base" (see Figure 18). FST 134, having words and weights only, is then output from morphology deletion module 130 to best path enumeration module 135. Best path enumeration module 135 sums the weights of each path of FST 134, and outputs a ranked list 136 of alternative words that can be used to replace a misspelled word or a grammatically-incorrect word in the input text. In accordance with the invention, and particularly in cases where the invention is used in a non-English-language context, this list of alternative words may contain words having an accent mark and/or a diacritic which is different from, and/or missing from, the original word. In addition, in preferred embodiments of the invention, this ranked list ranks the alternative words according to which have the highest weights. Of course, in a case that weights are not used, or different types of weights are used, the ranking can be performed differently. The spelling and grammar checking system of the present invention may be used in conjunction with a variety of different types of applications. Examples of such uses of the invention are provided in more detail below. Word Processing Spelling and grammar checking code 49 of the present invention may be used in the context of a word processing application, such as those described above. Figure 20 is a flow diagram depicting computer- executable process steps which are used in such a word processing application. More specifically, step S2001 inputs text into a text document. Next, step S2002 spell-checks the text so as to replace misspelled words in the text with correctly-spelled words. In preferred embodiments of the invention, step S2002 is performed in accordance with Figures 3 or 4 described above, and comprises detecting misspelled words in the text, and, for each misspelled word, determining a list of alternative words for the misspelled word, ranking the list of alternative words based on a context in the text, selecting one of the alternative words from the list, and replacing the misspelled word in the text with the selected one of the alternative words. Next, step S2003 checks the document for grammatically-incorrect words. In preferred embodiments of the invention, step S2003 checks the document by (i) generating a finite state machine ("FSM") for text in the text document, the finite state machine including alternative words for at least one word in the text and a rank associated with each alternative word, (ii) adjusting the ranks in the FSM in accordance with one or more of a plurality of predetermined grammatical rules, and (iii) determining which of the alternative words is grammatically correct based on ranks for the alternative words. Finally, step S2004 replaces grammatically-incorrect words in the document with grammatically-correct word, and step S2005 outputs the document with little or no grammatical and/or spelling errors Machine Translation Spelling and grammar checking code 49 of the present invention may be used in the context of a machine translation system which translates documents from one language to another language, such as those described above. Figure 21 is a flow diagram depicting computer- executable process steps which are used in such a machine translation system. More specifically, step S2101 inputs text in a first language, and step S2102 spell-checks the text in the first language so as to replace misspelled words in the text with correctly-spelled words. In preferred embodiments of the invention, this spell-checking step is performed in accordance with Figures 3 or 4 described above, and comprises detecting misspelled words in the text, and, for each misspelled word, determining a list of alternative words for the misspelled word, ranking the list of alternative words based on a context in the text, selecting one of the alternative words from the list, and replacing the misspelled word in the document with the selected one of the alternative words. Next, step S2103 checks the text in the first language for grammatically-incorrect words. Step S2103 does this by (i) generating a finite state machine ("FSM") for the text in the first language, the finite state machine including alternative words for at least one word in the text and a rank associated with each alternative word, (ii) adjusting the ranks in the FSM in accordance with one or more of a plurality of predetermined grammatical rales, and (iii) determining which of the alternative words is grammatically correct based on ranks for the alternative words. Grammatically-incorrect words in the text are then replaced with grammatically-correct words in step S2104. Following step S2104, step S2105 translates the text from the first language into the second language, and step S2106 spell-checks the text in the second language so as to replace misspelled words in the text with correctly-spelled words. In preferred embodiments of the invention, step S2106 spell checks the text in the same manner as did step S2102. Accordingly, a detailed description of this process is not repeated. Thereafter, step S2107 checks the text in the second language for grammatically-incorrect words in the same manner that step S2103 checked the text in the first language. Accordingly, a detailed description of this process is not repeated. Step S2108 then replaces grammatically-incorrect words in the text with grammatically-correct words, and step S2109 outputs the text with little or no grammatical and/or spelling errors. Optical Character Recognition Spelling and grammar checking code 49 of the present invention may be used in the context of an optical character recognition system which recognizes input character images. Figure 22 is a flow diagram depicting computer-executable process steps which are used in such an optical character recognition system. More specifically, step S2201 inputs a document image, e.g. , via scanner 13, and step S2202 parses character images from the document image. Thereafter, step S2203 performs character recognition processing on parsed character images so as to produce document text. Step S2204 then spell-checks the document text so as to replace misspelled words in the document text with correctly-spelled words. This spell checking is performed in accordance with Figures 3 or 4 described above, and comprises detecting misspelled words in the document text, and, for each misspelled word, determining a list of alternative words for the misspelled word, ranking the list of alternative words based on a context in the text, selecting one of the alternative words from the list, and replacing the misspelled word in the document text with the selected one of the alternative words. Next, step S2205 checks the document text for grammatically-incorrect words. This checking is performed in accordance with Figures 3 or 4 described above, and comprises (i) generating a finite state machine ("FSM") for the document text, the finite state machine including alternative words for at least one word in the text and a rank associated with each alternative word, (ii) adjusting the ranks in the FSM in accordance with one or more of a plurality of predetermined grammatical rules, and (iii) determining which of the alternative words is grammatically correct based on ranks associated for the alternative words. Thereafter, step S2206 replaces grammatically-incorrect words in the document text with grammatically correct words, and step S2207 outputs the document text with little or no grammatical and/or spelling errors. Text Indexing And Retrieval Spelling and grammar checking code 49 of the present invention may be used in the context of a text indexing and retrieval system for retrieving text from a source based on an input search word. Examples of such text indexing and retrieving systems in which the present invention may be used include, but are not limited to, Internet search engines, document retrieval software, etc. Figure 23 is a flow diagram depicting computer-executable process steps which are used in such a text indexing and retrieval system. More specifically, step S2301 comprises inputting a search word or a search phrase comprised of plural search words, and step S2302 comprises correcting a spelling of each search word to produce corrected search word(s). Next, in a case that a search phrase is input, step S2303 replaces grammatically-incorrect words in the search phrase with a grammatically-correct word in order to produce a corrected search phrase. In the invention, steps S2302 and S2303 are preferably performed by spelling and grammar checking code 49 shown in Figures 3 or 4. Step S2304 then retrieves text from a source (e.g. , a pre-stored database or a remote location such as a URL on the WWW) that includes the corrected search word/phrase, and step S2305 displays the retrieved text on local display, such as display screen 11. Client-Server Configuration The spelling and grammar checking system of the present invention may also be utilized in a plurality of different hardware contexts. For example, the invention may be utilized in a client-server context. In this aspect of the invention, a single computer, such as PC 4, can service multiple requests for spelling correction at the same time by executing multiple threads of the same program, such as spelling suggestion module 52. To perform this function, in this embodiment of the invention, processor 38 is multi-tasking. In brief, this aspect of the invention comprises computer- executable process steps to correct misspelled words in input text sequences received from a plurality of different clients. The process steps include code to store in a memory on a server (e.g. , PC 4 shown in Figure 1 or a stand-alone server), a lexicon comprised of a plurality of reference words, code to receive the input text sequences from the plurality of different clients (e.g. , over fax/modem line 10, network interface 9, etc.), code to spell-check the input text sequences using the reference words in the lexicon, and code to output spell-checked text sequences to the plurality of different clients. In preferred embodiments of the invention, the lexicon comprises one or more lexicon FSTs (in general, FSMs), stored in a single memory, where the lexicon FSTs include the plurality of reference words and a phonetic representation each reference word. In these embodiments, the spell-checking code comprises a code to correct misspelled words in each of the input text sequences substantially in parallel using the lexicon FSTs stored in the single memory. This code corresponds to that described above in Figures 3 and 4. Figure 24 shows representative architecture of the client- server multi-threaded spelling correction system of the present invention. As shown in Figure 24, lexicon memory 150 (which stores lexicon FSTs of the type described above) is shared across each program thread 151, 152 and 153 of the client-server spelling correction system. In this regard, each program thread comprises a substantially complete copy of spelling and grammar checking code 49. Each of program threads 151 to 153 contains a corresponding memory (i.e. , memories 154, 155 and 156) that is used by processor 38 to execute that thread, as well as to perform other processing in relation thereto. Each spelling memory also stores an FSA generated by spelling suggestion module 52 (see Figure 5), and may also store additional programs and variables. Lexicon memory 150 is identical to a memory used to store the lexicon FSTs described with respect to Figure 5, but, unlike that in Figure 5, is being shared by plural program threads on the server. In operation, multiple text sequences (TEXT1 160, TEXT2 161...TEXTn 162) from a plurality of different clients are input to the server from remote sources, such as a LAN, the Internet, a modem, or the like, and are processed by respective program threads. Specifically, each program thread identifies misspelled words in the text, and, using lexicon memory 150, outputs corrected text, as shown in Figure 24. In this regard, the operation of the spelling and grammar checking code used in this aspect of the invention is identical to that described above, with the only difference being memory allocation. Client-Server Information Retrieval System Figure 25 shows the multi-threaded client-server spelling correction system described above used in a text indexing and retrieval context (e.g. , in conjunction with a WWW search engine, database searching software, etc.). In this regard, in text indexing and retrieving systems, textual queries are sent to a database, and information related to the textual queries is retrieved from the database. Often, however, queries are misspelled and, as a result, correct information cannot be retrieved from the database. The system shown in Figure 25 addresses this problem. More specifically, in Figure 25, as was the case above with respect to Figure 24, multiple queries are input at the same time to the server (i.e. , PC 4). As was the case in Figure 24, lexicon memory 750 is shared among all of program threads 151, 152 and 153. In addition, as before, each program thread contains its own spelling memory. In operation, multiple queries (i.e. , QUERYl 164, QUERY2 165...QUERYn 166) are input to the client-server spelling correction system of the present invention before each query is actually used to retrieve information from database 169. The present invention then corrects each query in the manner described above with respect to Figures 3, 4 and in particular, Figure 5. Each corrected query is then used to retrieve information from database 169. The present invention has been described with respect to particular illustrative embodiments. It is to be understood that the invention is not limited to the above-described embodiments and modifications thereto, and that various changes and modifications may be made by those of ordinary skill in the art without departing from the spirit and scope of the appended claims. Appendix A Copyright 1998 Teragram Corporation All Rights Reserved ft APPLICATIONS of RULES # # Subject- Verb agreement # *SubjectVerb/9 :PN/10 :PN/10 # verb groups *VG/0 # # # Application of Noun phrases # Noun phrase agreement # *NP/0 ♦ NP3S/0 that,C/10 ♦ NP3S/0 that,C/10 *DETSING/10 # # whom/who # :Preρ/10 whom,WPro/10 :Preρ/10 whom,WPro/10 :Prep/10 whom,WPro/10 *SubjectVerb/10 #:Prep/10 whom,WPro/10 did,Vpt/10 #:Prep/10 whom,WPro/10 does,V3sg/10 #:Prep/10 whom,WPro/10 do,V/10 # who took the call # whom I saw who,WPro/10 *VG/10 *NP/10 whom,WPro/10 *NP/10 *VG/10 # ft whomever/ whoever # Prep/ 10 whomever, WPro/ 10 Prep/ 10 whomever, WPro/ 10 Prep/ 10 whomever, WPro/ 10 *SubjectVerb/10 whoever, WPro/ 10 *VG/10 *NP/10 whomever,WPro/10 *NP/10 *VG/10 ft — ft to # want,V/10 to,Prep/20 wanted, Vpt 10 to,Prep/20 wants, V3sg/ 10 to,Prep/20 wish,V/10 to,Prep/20 wished,Vpt/10 to,Prep/20 wishes, V3sg/ 10 to,Prep/20 to,Prep/10 :V/10 :V/10 to,Prep/10 :Vpt/10 to,Prep/10 :V3sg/10 to,Prep/10 whether.C/lO to,Prep/10 :Ving/10 to,Prep/10 ft to a new home ft to det to,Prep/10 :Det 20 ft 40 to 50 :digit/10 to,Preρ/10 :digit/40 ft resemblance to resemblance,N/10 to,Prep/40 ft membership to membership,N/10 to,Prep/40 ft transition to transition,N/10 to,Prep/40 # critics to critics,Npl/10 to,Prep/40 ft trip to trips,Npl/10 to,Prep/40 ft extradition to extradition,N/10 to, Prep/40 ft ambassador to ambassador,N/10 to, Prep/40 ft subject to subject,N/10 to,Prep/40 ft in contrast to in,Prep/10 contrast,N/10 to,Prep/40 # risk to risk,N/10 to,Prep/40 # relief to relief,N/10 to,Prep/40 ft give birth to #give+V/10 birth,N/10 to,Prep/40 give;:V/10 birth,N/10 to,Preρ/40 gave;give:Vpt/10 birth,N/10 to,Prep/40 given;give:Vpp/10 birth,N/10 to,Prep/40 gives;give:V3sg/10 birth,N/10 to,Prep/40 # ft t o # two,Num/10 :Npl/20 two,Num/10 :A/10 :Npl/20 two,Num/10 hundred, Num/30 two,Num/10 thousand,Num/30 two,Num/10 million,Num/30 two,Num/10 billion,Num/30 twenty,Num/10 two,Num/30 Λirty,Num/10 two,Num/30 forty,Num/10 two,Num/30 fιfty,Num/10 two,Num/30 sixty,Num/10 two,Num/30 seventy,Num/10 two,Num/30 eighty, Num/ 10 two,Num/30 ninety, Num 10 two, Num/30 ft — # too # tt too much too,Adv/10 much,Adv/40 ft too ADV too ADV too,Adv/200 :Adv/200 too,Adv/200 :Adv/200 ft far too many far,Adv/10 too,Adv/40 many,A/10 ft far too little far,Adv/10 too,Adv/40 little,Adv/10 ft far too much far,Adv/10 too,Adv/40 much,Adv/10 ft far too A far,Adv/10 too,Adv/20 ,A/10 ft too many too,Adv/10 many,A/20 ft too many of too,Adv/40 many,A/10 of,Prep/10 ft too A too,Adv/10 ,A/20 ft too self too,Adv/10 self, A/ 1 # BE too A he is too big is;be:V3sg/10 too,Adv/10 ,A/40 am,xx/10 too,Adv/10 ,A/40 are,xx/10 too,Adv/10 ,A/40 was;be:xx/10 too,Adv/10 ,A/40 were;be:xx/10 too,Adv/10 ,A/40 been;be:Vpp/10 too,Adv/10 ,A/40 being;be:Ving/10 too,Adv/10 ,A/40 for,Prep/10 too,Adv/10 ,A/40 # too small to too,Adv/10 ,A/40 to,Preρ/10 # ft then # .;:inc/10 then,Adv/20 # ft than # ftff Acomp than ft better than ,Acomρ/10 than,C/20 tttt more A than ft more courageous than more, Adv/ 10 ,A/10 than,C/20 tttt more than more, Adv/ 10 than,C/20 tttt more often than more,Adv/10 often,Adv/10 than,C/20 tttt more Npl than # more duds than more,Adv/10 ,Npl/10 than,C/20 tttt more Ns than # more business than more,Adv/10 ,N/10 than,C/20 ftff more Adv than # more sharply than more,Adv/10 ,Adv/10 than,C/20 tttt less A than ft less courageous than less,Adv/10 ,A/10 than,C/20 tttt less than less, Adv/ 10 than,C/20 ftff less often than less, Adv/ 10 often, Adv/ 10 than,C/20 ftff less Npl than ft less duds than less,Adv/10 ,Npl/10 than,C/20 tttt less Ns than # less business than less,Adv/10 ,N/10 than,C/20 tttt less Adv than ft less sharply than less,Adv/10 ,Adv/10 than,C/20 tttt other than other,Adv/10 than,C/20 tttt rather than rather,Adv/10 than,C/20 tttt further than further, Adv/ 10 than,C/20 tfff than most than,C/20 most,A/20 ft no other Ns than ft no other reason than no,Adv/10 other,A/10 ,N/10 than,C/20 ft ft cloth/clothe ft broach/broch ft ft there there,Adv/10 is;be:V3sg/20 there, Adv/ 10 are;be:xx/20 there,Adv/10 was;be:xx/20 there,Adv/10 were;be:xx/20 ft bridle/bridal bridal,A/30 fashion,N/30 :V/10 :Adv/10 mwmmmmmmmmmmmιt#ftιtttιtttιtitιtititιtitιtιtttftιtttιtιtιt # DEFINIΗON of RULES mmmmmmmmmmmmmmιtttitιtttttιtttιtttιtftιtttιtttιtιttt # _# ft Verb Group ft ft *VG = *VG-Simple/0 *VG = *VG-Complex/0 *VG = *VG-not-Simple/0 *VG = *VG-not-Complex/0 # # ft Verb Groups (not negated) # # ft VG-Simple ♦ VG-Simple = :V3sg/10 ♦ VG-Simple = :Vpt/10 *VG-Simple = *VG-A/0 *VG-Simple = *VG-B/0 *VG-Simple = *VG-C/0 ♦ VG-Simple = *VG-D/0 ft VG-Complex ♦ VG-Complex = *VG-AB/0 ♦ VG-Complex = ♦ VG-AC/O ♦ VG-Complex = *VG-AD/0 ♦ VG-Complex = *VG-BC/0 ♦ VG-Complex = ♦ VG-BD/O *VG-Complex = *VG-CD/0 ♦ VG-Complex = *VG-ABC/0 *VG-Complex = *VG-ABD/0 *VG-Complex = *VG-ACD/0 *VG-Complex = *VG-BCD/0 *VG-Complex = *VG-ABCD/0 tt should add the verb to be ft MODAL ♦ MODAL = must,Md/10 *MODAL = could,Md/10 *MODAL = should,Md/10 ♦ MODAL = might,Md/10 ♦ MODAL = may,Md/10 ♦ MODAL = can,Md/10 ♦ MODAL = would,Md/10 ♦ MODAL = shall,Md/10 ♦ MODAL = will,Md/10 ♦ MODALnt = mus 't,Mdn't/10 ♦ MODALnt = couldn't.Mdn't/lO ♦ MODALnt = shouldn't.Mdn't/lO ♦ MODALnt = mightn't,Mdn't/10 ♦ MODALnt = can't.Mdn't/lO ♦ MODALnt = wouldn't,Mdn't/10 ♦ MODALnt = shan't,Mdn't/10 ♦ MODALnt = won't,Mdn't/10 # VG-A ft MODAL+inf # must examine ♦ VG-A = ♦ MODAL/0 :V/10 # VG-B ft HAVE + -ed ft has examined ♦ VG-B = have, V/ 10 : Vpp/ 10 ♦ VG-B = has,V3sg/10 :Vpp/10 *VG-B = had,Vpt/10 :Vpp/10 ft BE + -ing ft is examining ♦ VG-C = am;be:xx/10 :Ving/10 ♦ VG-C = is,V3sg/10 :Ving/10 ♦ VG-C = are;be:xx/10 :Ving/10 ♦ VG-C = was;be:xx/10 :Ving/10 ♦ VG-C = were;be:xx/10 :Ving/10 ft VG-D TO FIX ft BE + -ed ft is examined ♦ VG-D = am;be:xx/10 :Vpp/10 ♦ VG-D = is,V3sg/10 :Vpp/10 ♦ VG-D = are;be:xx/10 :Vpp/10 ♦ VG-D = was;be:xx/10 :Vpp/10 ♦ VG-D = were;be:xx/10 :Vpp/10 ft VG-AB tt may have examined ♦ VG-AB = ♦ MODAL/10 have,V/10 :Vpp/10 # AC tt may be examining ♦ VG-AC = ♦ MODAL/10 be,V/10 :Ving/10 # AD tt may be examined ♦ VG-AD = ♦ MODAL/10 be,V/10 :Vpp/10 # BC tt has been examining ♦ VG-BC = have,V/10 been,Vpp:/10 :Ving/10 ♦ VG-BC = has,V3sg/10 been,Vpp:/10 :Ving/10 ♦ VG-BC = had,Vpt/10 been,Vpp:/10 :Ving/10 # BD tt has been examined ♦ VG-BD = have,V/10 been,Vpp:/10 :Vpp/10 ♦ VG-BD = has,V3sg/10 been,Vpp:/10 :Vpp/10 ♦ VG-BD = had,Vpt/10 been,Vpp:/10 :Vpp/10 # CD tt is being examined ♦ VG-CD = am;be:xx/10 being, Ving/ 10 : Vpp/ 10 ♦ VG-CD = is,V3sg/10 being,Vrng/10 :Vpp/10 ♦ VG-CD = are;be:xx/10 being,Ving/10 :Vpp/10 ♦ VG-CD = was;be:xx/10 being,Ving/10 :Vpp/10 ♦ VG-CD = were;be:xx/10 being,Ving/10 :Vpp/10 tt ABC tt may have been examining ♦ VG-ABC = ♦ MODAL/0 have,V/10 been,Vpp:/10 :Ving/10 tt ABD tt may have been examined ♦ VG-ABD = ♦ MODAL/0 have,V/10 been,Vpp:/10 :Vpp/10 tt ACD ft may be being examined ♦ VG-ACD = ♦ MODAL/0 be,V/10 being,Ving:/10 :Vpp/10 # BCD tt has been being examined ♦ VG-BCD = have,V/10 been,Vpp:/10 being,Ving/10 :Vpp/10 ♦ VG-BCD = has,V3sg/10 been,Vpp:/10 being,Ving/10 :Vpp/10 ♦ VG-BCD = had,Vpt/10 been,Vpp:/10 being,Ving/10 :Vpp/10 tt ABCD tt may have been being examined ♦ VG-ABCD = ♦ MODAL/0 have,V/10 been,Vpp:/10 being,Ving/10 :Vpp/10 # # tt Verb Groups negated ft ft ft VG-not-Simple ♦ VG-not-Simple = does,V3sg/10 not, Adv/ 10 :V/10 ♦ VG-not-Simple = do,V/10 not,Adv/10 :V/10 ♦ VG-not-Simple = did,Vpt 10 not,Adv/10 :V/10 ♦ VG-not-Simple = ♦ MODAL/0 not, Adv/ 10 :V/10 ♦ VG-not-Simple = ♦ MODALnt/0 :V/10 tt VG-not-Complex ♦ VG-not-Complex = ♦ VG-not-AB/0 ♦ VG-not-Complex = ♦ VG-not-AC/0 ♦ VG-not-Complex = ♦ VG-not-AD/0 ♦ VG-not-Complex = ♦ VG-not-BC/O ♦ VG-not-Complex = ♦ VG-not-BD/0 ♦ VG-not-Complex = ♦ VG-not-CD/0 ♦ VG-not-Complex = ♦ VG-not-ABC/0 ♦ VG-not-Complex = ♦ VG-not-ABD/0 ♦ VG-not-Complex = ♦ VG-not-ACD/0 tt ♦ VG-not-Complex = ♦ VG-not-BCD/0 ft ♦ VG-not-Complex = ♦ VG-not-ABCD/O ft should add the verb to be # MODAL ♦ MODAL = must,Md/10 ♦ MODAL = could,Md/10 ♦ MODAL = should,Md/10 ♦ MODAL = might,Md/10 ♦ MODAL = may,Md/10 ♦ MODAL = can,Md/10 ♦ MODAL = would,Md/10 ♦ MODAL = shall,Md/10 ♦ MODAL = will,Md/10 ♦ MODALnt = mustn't,Mdn't/10 ♦ MODALnt = couldn't.Mdn't/lO ♦ MODALnt = shouldn't,Mdn't/10 ♦ MODALnt = can't,Mdn't/10 ♦ MODALnt = wouldn't,Mdn't/10 ♦ MODALnt = shalln't,Mdn't/10 ♦ MODALnt = won't,Mdn't/10 tt VG-not-A # MODAL +inf tt must examine ♦ VG-not-A = ♦ MODAL/0 not,Adv/10 :V/10 ♦ VG-not-A = ♦ MODALnt/0 :V/10 tt VG-not-B tt HAVE + -ed ft has examined ♦ VG-not-B = have,V/10 not,Adv/10 :Vpp/10 ♦ VG-not-B = has,V3sg/10 not,Adv/10 :Vρρ/10 ♦ VG-not-B = had,Vpt/10 not,Adv/10 :Vpp/10 tt VG-not-C TO FIX (tags for am.is,...) tt BE + -ing tt is examining ♦ VG-not-C = am;be:xx/10 not,Adv/10 :Ving/10 ♦ VG-not-C = is,V3sg/10 not,Adv/10 :Ving/10 ♦ VG-not-C = are;be:xx/10 not,Adv/10 :Ving/10 tt VG-not-D TO FD # BE + -ed tt is examined ♦ VG-not-D = am;be:xx/10 not,Adv/10 :Vpp/10 ♦ VG-not-D = is,V3sg/10 not,Adv/10 :Vpp/10 ♦ VG-not-D = are;be:xx/10 not,Adv/10 :Vpp/10 ft VG-not-AB tt may have examined ♦ VG-not-AB = ♦ MODAL/10 not,Adv/10 have,V/10 :Vpp/10 ♦ VG-not-AB = ♦ MODALnt/10 have,V/10 :Vpp/10 # AC # may be examining ♦ VG-not-AC = ♦ MODAL/0 not,Adv/10 be,V/10 :Ving/10 ♦ VG-not-AC = ♦ MODALnt/0 be,V/10 :Ving/10 # AD tt may be examined ♦ VG-not-AD = ♦ MODAL/0 not,Adv/10 be,V/10 :Vpp/10 ♦ VG-not-AD = ♦ MODALnt/0 be,V/10 :Vpp/10 # BC tt has been examining ♦ VG-not-BC = have,V/10 not,Adv/10 been,Vpp:/10 :Ving/10 ♦ VG-not-BC = has,V3sg/10 not,Adv/10 been,Vpp:/10 :Ving/10 ♦ VG-not-BC = had,Vpt/10 not,Adv/10 been,Vpp:/10 :Ving/10 # BD ft has been examined ♦ VG-not-BD = have,V/10 not,Adv/10 been,Vpp:/10 :Vpρ/10 ♦ VG-not-BD = has,V3sg/10 not,Adv/10 been,Vρp:/10 :Vρρ/10 ♦ VG-not-BD = had,Vpt/10 not,Adv/10 been,Vpp:/10 :Vpp/10 # CD # is being examined ♦ VG-not-CD = am;be:xx/10 not,Adv/10 being,Ving/10 :Vpρ/10 ♦ VG-not-CD = is,V3sg/10 not,Adv/10 being,Ving/10 :Vpp/10 ♦ VG-not-CD = are;be:xx 10 not,Adv/10 being,Ving/10 :Vpp/10 # ABC ft may have been examining ♦ VG-not-ABC = ♦ MODAL/10 not,Adv/10 have,V/10 been,Vpp:/10 :Ving/10 ♦ VG-not-ABC = ♦ MODALnt 10 have,V/10 been,Vpp:/10 :Ving/10 # ABD tt may have been examined ♦ VG-not-ABD = ♦ MODAL/ 10 not, Adv/ 10 have, V/ 10 been,Vpp:/10 :Vpp/10 ♦ VG-not-ABD = ♦ MODALnt/10 have,V/10 been,Vpp:/10 :Vpp/10 # ACD # may be being examined ♦ VG-not-ACD = ♦ MODAL/10 not,Adv/10 be,V/10 being,Ving:/10 :Vpp/10 ♦ VG-not-ACD = ♦ MODALnt 10 be,V/10 being, Ving:/ 10 :Vpp/10 # BCD tt has been being examined ♦ VG-not-BC = have,V/10 not,Adv/10 been,Vpp:/10 being,Ving/10 : Vpp/ 10 ♦ VG-not-BC = has,V3sg/10 not,Adv/10 been,Vpp:/10 being,Ving/10 :Vpp/10 ♦ VG-not-BC = had,Vpt/10 not,Adv/10 been,Vpp:/10 being,Ving/10 : Vpp/ 10 tt ABCD tt may have been being examined ♦ VG-not-ABC = ♦ MODAL/0 not,Adv/10 have,V/10 been,Vpp:/10 being,Ving/10 :Vρp/10 ♦ VG-not-ABC = ♦ MODALnt/0 have,V/10 been,Vpp:/10 being,Ving/10 : Vpp/ 10 # ft POSSPRO tt Possessive pronoun, no number agreeement tt ♦ POSSPRO = his,PossPro/10 ♦ POSSPRO = her,PossPro/10 ♦ POSSPRO = its,PossPro/10 ♦ POSSPRO = my,PossPro/10 ♦ POSSPRO = our,PossPro/10 ♦ POSSPRO = their,PossPro/10 ♦ POSSPRO = your,PossPro/10 # tt DETSING # ♦ DETSING = a,Det/10 ♦ DETSING = one,Num/ll ♦ DETSING = an,Det/10 ♦ DETSING = the,Det 10 ♦ DETSING = this,Det/10 ♦ DETSING = ♦ POSSPRO/0 # # DETPL # ♦ DETPL = these,Det/10 ♦ DETPL = ♦ POSSPRO/0 ♦ DETPL = ♦ NUM/O ♦ DETPL = ♦ NUM/O ♦ NUM/l ♦ DETPL = ♦ NUM/O and,C/10 + NUM/1 ♦ DETPL = ♦ NUM/O + NUM/1 + NUM/1 ♦ DETPL = +NUM/0 +NUM/1 and,C/10 + NUM/1 ♦ NUM = one,Num/10 ♦ NUM = two,Num/10 ♦ NUM = three,Num/10 ♦ NUM = four,Num/10 ♦ NUM = fιve,Num/10 ♦ NUM = six.Num/lO ♦ NUM = seven, Num/ 10 ♦ NUM = eight,Num/10 ♦ NUM = nine,Num/10 ♦ NUM = ten,Num/10 ♦ NUM = eleven,Num/10 ♦ NUM = twelve,Num 10 ♦ NUM = thirteen,Num/10 + NUM = fourteen,Num 10 ♦ NUM = fιfteen,Num/10 ♦ NUM = sixteen,Num/10 ♦ NUM = seventeen,Num 10 ♦ NUM = eighteen,Num/10 + NUM = nineteen,Num/10 ♦ NUM = twenty,Num/10 ♦ NUM = thirty,Num/10 ♦ NUM = forty, Num/ 10 + NUM = fιfty,Num/10 ♦ NUM = sixty, Num/ 10 ♦ NUM = seventy,Num/10 ♦ NUM = eighty,Num/10 ♦ NUM = ninety,Num 10 ♦ NUM = hundred,Num/10 ♦ NUM = thousand,Num/10 ♦ NUM = million,Num/10 tt tt PRO-3S tt pronouns not with 3rd person singular morphological sign # ♦ PRO-3S = they, Pro/ 10 ♦ PRO-SS = we,Pro/10 ♦ PRO-3S = I,Pro/10 ♦ PRO-3S = you,Pro/10 ♦ PRO-3S = you,Pro/10 tt ft PRO3S ft pronouns with 3rd person singular moφhological sign # ♦ PRO3S = this,Pro/10 ♦ PRO3S = it, Pro/30 ♦ PRO3S = he,Pro/10 ♦ PRO3S = she,Pro/10 ♦ PRO3S = that, Pro/ 10 # tt ADJP Adjectival Phrase # ♦ ADJP = : Adv/ 10 ♦ ADJJ/0 ♦ ADJP = ♦ ADJJ/0 # tt ADJJ sequence of adjective(s) # #A/11 is there to compensante against Vpp/5 + 5 unigram ♦ ADJJ = A/11 ♦ ADJJ = A/10 :A/10 ♦ ADJJ = Vpp/5 ♦ ADJJ = Ving/5 # # NP # NOUN Phrase # *NP = ♦ NPSS/O ♦ NP = ♦ NP-SS/O # # NP3S tt NOUN Phrase with 3rd person singular moφhological sign # ♦ NP3S = ♦ PRO3S/0 ♦ NP3S = :N/10 ♦ NP3S = ♦ ADJP/0 :N/10 ♦ NPSS = ♦ DETSING/0 :N/10 ♦ NP3S = ♦ DETSING/0 ♦ ADJP/0 :N/10 ♦ NP3S = each,Pro/10 of,Prep/10 ♦ PRO-3S/10 ♦ NP3S = each,Pro/10 of,Prep/10 ♦ DETPL/10 :Npl 10 ♦ NP3S = each,Pro/10 of,Prep/10 ♦ DETPL/10 ♦ ADJP/0 :Npl/10 # # NP-3S tt NOUN Phrase without 3rd person singular moφhological sign # ♦ NP-3S = ♦ PRO-3S/0 ♦ NP-3S = ♦ DETPL/0 :Npl/10 ♦ NP-3S = ♦ DETPL/0 ♦ ADJP/0 : Npl/ 10 ♦ NP-3S = ♦ ADJP/SO : Npl/ 10 ♦ NP-3S = : Npl/ 10 # # tt Subject- Verb Agreement # # ♦ SubjectVerb = ♦ NPSS/O :V3sg/10 ♦ SubjectVerb = ♦ NPSS/O :Vpt/10 ♦ SubjectVerb = ♦ NP-SS/O :V/10 ♦ SubjectVerb = ♦ NP-SS/O :Vpt/10 ft he/John was ♦ SubjectVerb = ♦ NPSS/O was;be:xx/10 ft I was ♦ SubjectVerb = I,Pro/10 was;be:xx/10 # I am ♦ SubjectVerb = I,Pro/10 am;be:xx/10 ft you are ♦ SubjectVerb = you,Pro/10 are;be:xx/10 tt they are ♦ SubjectVerb = they,Pro/10 are;be:xx/10 tt WH Questions tt this should be done differently tt Where can I rent a car ... tt Who can rent a car? tt Can you rent a car? :WAdv/10 ♦ MODAL/ 10 :WPro/10 ♦ MODAL/10 Appendix B The following is a list of pairs of often-confused words. If a first word in a pair is found, the second word is also processed by the invention, meaning that a substantially similar word thereto is search for and, if found, output. adept,adapt adept,adopt adopt.adept adopt,adapt adapt,adept adapt, adopt ads, adds adds, ads advice, advise advise, advice aid, aide aide, aid alter,altar altar.alter backwards,backward backward.backwards bare,bear bear,bare beet,beat beat,beet border,boarder boarder,border breath,breathe breathe,breath to,two to,too too.to too.two two.to two.too than,then then, than cloth.clothe clothe, cloth broach.brooch brooch,broach bridal,bridle bridle,bridal brows,browse browse.brows cant.can't can't.cant confident , confidant confidant , confident decent,descent dependent,dependant dependant, dependent desert,dessert dessert, desert downward , downwards downwards , downward elicit, illicit illicit, elicit envelop, envelope envelope,envelop feat, feet feet.feat find,fined fined,find flare, flair flair, flare flea.flee flee, flea herd,heard heard,herd hew.hue hue,hew hoard.horde horde.hoard incite.insight insight, incite indoor.indoors indoors,indoor inward, inwards inwards, inward its.it's it's.its laps.lapse lapse.laps let's.lets lets.let's mmd,mined mmed,mind mmer,minor mmor,miner morn.moura mourn.morn navel,naval naval.navel new.knew knew.new no, know know, no outdoor, outdoors outdoors, outdoor outwards, outward outward , outwards pedal.peddle pray,prey prey, pray pride.pried pried.pride principal.principle principle,principal thats.that's that's.thats their.there their.they're there,their there,they're they're,their tide,tied tied,tide upwards.upward upward,upwards who's.whose whose.who'S who,whom whom, who whoever,whomever whomever.whoever wont.won't wont.won won't, wont you're,your your.you're Appendix C according as = C according to = Prep again and again = Adv all in all = Adv all of a sudden = Adv all the better = Adv all the closer = Adv all the farther = Adv all the worse = Adv alongside of = Prep an arm and a leg = N anything but = Prep arm in arm = Adv as much again = Adv at all costs = Adv at arm's length = Adv away with = Prep back and forth = Adv behind his back = Adv between ourselves = Adv bit by bit = Adv by and large = Adv charter flight = N commercial flight = N commercial passenger flight = N connecting flight = N cream of the crop = N cum lauda = Adv daily flight = N domestic flight = N flight attendant = N flight bag = N flight control = N flight crew = N flight deck = N flight engineer = N flight feather = N flight line = N flight plan = N flight shooting = N flight simulator = N flight strip = N flight surgeon = N for crying out loud = Adv in advance = Adv in due course = Adv in the abstract = Adv in the affirmative = Adv in the altogether = Adv in the bag = Adv in the balance = Adv in the course of = Prep in the meantime = Adv in the same boat = Adv international flight = N medical evacuation flight = N military flight = N nonsmoking flight = N nonstop flight = N of a certain age = Adv on account of = Prep on the alert = Adv once in a blue moon = Adv out of bounds = Adv out of the blue = Adv overnight flight = N regular flight = N under arrest = Adv under cover = Adj weekly flight = N Exchange.3-011705.1

Claims: WHAT IS CLAIMED IS: 1. A method of correcting misspelled words in input text, the method comprising the steps of: detecting a misspelled word in the input text; determining a list of alternative words for the misspelled word; and ranking the list of alternative words based on a context of the input text. 2. A method according to Claim 1, further comprising the steps of: selecting one of the alternative words from the list; and replacing the misspelled word in the text with the selected one of the alternative words. 3. A method according to Claim 1, wherein the detecting step comprises comparing each word in the input text to a dictionary database and characterizing a word as misspelled when the word either (i) does not match any words in the dictionary database, or (ii) is spelled correctly but corresponds to one of a plurality of words which are substantially similar. 4. A method according to Claim 1, wherein the determining step comprises: storing one or more lexicon finite state machines ("FSM"), each of the lexicon FSMs including plural reference words and a phonetic representation of each reference word; generating an input FSM for the misspelled word, the input FSM including the misspelled word and a phonetic representation of the misspelled word; selecting one or more reference words from the lexicon FSMs based on the input FSM, the one or more reference words substantially corresponding to either a spelling of the misspelled word or to the phonetic representation of the misspelled word; and adding selected ones of the one or more reference words to the list of alternative words. 5. A method according to Claim 4, wherein the selecting step comprises the step generating an additional FSM having a plurality of states, the states comprising at least states of a lexicon FSM and states of the input FSM, and selecting the one or more reference words from the lexicon FSM using the additional FSM. 6. A method according to Claim 5, wherein the lexicon FSMs and the input FSM comprise finite state transducers ("FST"), and the additional FSM comprises a finite state automaton ("FSA"). 7. A method according to Claim 5, wherein the selecting step selects the one or more reference words by applying each state of the additional FSM to one or more of (i) a character identity module, (ii) a phonetic identity module, (iii) a character insertion module, (iv) a character deletion module, (v) a character replacement module, (vi) a character transposition module, and (vii) a character transposition completion module; and wherein (i) the character identity module determines whether characters of a reference word in the lexicon FSM match characters of the misspelled word in the input FSM, (ii) the phonetic identity module determines whether characters of the reference word are pronounced the same as characters of the misspelled word, (iii) the character insertion module determines whether a character inserted in the misspelled word causes at least part of the misspelled word to match at least part of the reference word, (iv) the character deletion module determines whether a character deleted from the misspelled word causes at least part of the misspelled word to match at least part of the reference word, (v) the character replacement module replaces characters in the misspelled word with characters in the reference word in order to determine whether at least part of the misspelled word matches at least part of the reference word, (vi) the character transposition module changes the order of two or more characters in the misspelled word and compares a changed character in the misspelled word to a corresponding character in the reference word, and (vi) the character transposition completion module compares characters in the misspelled word which were not compared by the character transposition module in order to determine if at least part of the misspelled word matches at least part of the reference word. 8. A method according to Claim 1, wherein the ranking step comprises: generating a first finite state machine ("FSM") for the input text, the first FSM having a plurality of arcs which include the alternative words and weights associated therewith, where a weight of each alternative word corresponds to a likelihood that the alternative word, taken out of grammatical context, comprises a correctly-spelled version of the misspelled word; generating a second FSM for the input text and the alternative words in accordance with one or more of a plurality of predetermined grammatical rules, the second FSM having a plurality of arcs which include the alternative words and weights associated therewith, where a weight of each alternative word corresponds to a likelihood that the alternative word, taken in grammatical context, comprises a correctly- spelled version of the misspelled word; and adding corresponding weights of the first FSM and the second FSM and ranking the alternative words in accordance with the weights of the alternative words. 9. A method according to Claim 2, wherein the selecting step comprises displaying the list of alternative words and manually selecting one of the alternative words. 10. A method according to Claim 2, wherein the selecting step is performed automatically. 11. A method according to Claim 10, wherein the selecting step selects a one of the alternative words that is ranked highest in the list of alternative words. 12. A word processing method for creating and editing text documents, the word processing method comprising the steps of: inputting text into a text document; spell-checking the text so as to replace misspelled words in the text with correctly-spelled words; and outputting the document; wherein the spell-checking step comprises detecting misspelled words in the text, and, for each misspelled word, determining a list of alternative words for the misspelled word, ranking the list of alternative words based on a context in the text, selecting one of the alternative words from the list, and replacing the misspelled word in the text with the selected one of the alternative words. 13. A machine translation method for translating text from a first language into a second language, the machine translation method comprising the steps of: inputting text in the first language; spell-checking the text in the first language so as to replace misspelled words in the text with correctly-spelled words; translating the text from the first language into the second language; and outputting translated text; wherein the spell-checking step comprises detecting misspelled words in the text, and, for each misspelled word, determining a list of alternative words for the misspelled word, ranking the list of alternative words based on a context in the text, selecting one of the alternative words from the list, and replacing the misspelled word in the document with the selected one of the alternative words. 14. A machine translation method for translating text from a first language into a second language, the machine translation method comprising the steps of: inputting text in the first language; translating the text from the first language into the second language; spell-checking the text in the second language so as to replace misspelled words in the text with correctly-spelled words; and outputting the text; wherein the spell-checking step comprises detecting misspelled words in the text, and, for each misspelled word, determining a list of alternative words for the misspelled word, ranking the list of alternative words based on a context in the text, selecting one of the alternative words from the list, and replacing the misspelled word in the document with the selected one of the alternative words. 15. An optical character recognition method for recognizing input character images, the optical character recognition method comprising the steps of: inputting a document image; parsing character images from the document image; performing recognition processing on parsed character images so as to produce document text; spell-checking the document text so as to replace misspelled words in the document text with correctly-spelled words; and outputting the document text; wherein the spell-checking step comprises detecting misspelled words in the document text, and, for each misspelled word, determining a list of alternative words for the misspelled word, ranking the list of alternative words based on a context in the text, selecting one of the alternative words from the list, and replacing the misspelled word in the document text with the selected one of the alternative words. 16. A method of retrieving text from a source, the method comprising the steps of: inputting a search word; correcting a spelling of the search word to produce a corrected search word; and retrieving text from the source that includes the corrected search word. 17. A method according to Claim 16, wherein the correcting step comprises the steps of: storing one or more lexicon finite state machines ("FSM"), each of the lexicon FSMs including plural reference words and a phonetic representation of each reference word; generating an input FSM for the search word, the input FSM including the search word and a phonetic representation of the search word; selecting one or more reference words from the lexicon FSMs based on the input FSM, the one or more reference words substantially corresponding to either a spelling of the search word or to the phonetic representation of the search word; and setting a selected one of the reference words as the corrected search word. 18. A method according to Claim 17, wherein the selecting step comprises the step generating an additional FSM having a plurality of states, the states comprising at least states of a lexicon FSM and states of the input FSM, and selecting the one or more reference words from the lexicon FSM using the additional FSM. 19. A method according to Claim 17, wherein the lexicon FSMs and the input FSM comprise finite state transducers ("FST"), and the additional FSM comprises a finite state automaton ("FSA"). 20. A method according to Claim 18, wherein the selecting step selects the one or more reference words by applying each state of the additional FSM to one or more of (i) a character identity module, (ii) a phonetic identity module, (iii) a character insertion module, (iv) a character deletion module, (v) a character replacement module, (vi) a character transposition module, and (vii) a character transposition completion module; and wherein (i) the character identity module determines whether characters of a reference word in the lexicon FSM match characters of the search word in the input FSM, (ii) the phonetic identity module determines whether characters of the reference word are pronounced the same as characters of the search word, (iii) the character insertion module determines whether a character inserted in the search word causes at least part of the search word to match at least part of the reference word, (iv) the character deletion module determines whether a character deleted from the search word causes at least part of the search word to match at least part of the reference word, (v) the character replacement module replaces characters in the search word with characters in the reference word in order to determine whether at least part of the search word matches at least part of the reference word, (vi) the character transposition module changes the order of two or more characters in the search word and compares a changed character in the search word to a corresponding character in the reference word, and (vi) the character transposition completion module compares characters in the search word which were not compared by the character transposition module in order to determine if at least part of the search word matches at least part of the reference word. 21. A method according to Claim 16, wherein the source comprises a remote network location; and wherein the method further comprises the step of displaying the retrieved text on a local display screen. 22. A method according to Claim 16, wherein the correcting step comprises displaying one or more corrected search words and manually selecting one of plural corrected search words. 23. A method according to Claim 16, wherein the correcting step comprises automatically selecting one of the corrected search words. 24. A method according to Claim 16, wherein the source comprises a pre-stored database. 25. A method of retrieving text from a source, the method comprising the steps of: inputting a search phrase comprised of a plurality of words, at least one of the plurality of words being an incorrect word; replacing the incorrect word in the search phrase with a corrected word in order to produce a corrected search phrase; and retrieving text from the source based on the corrected search phrase. 26. A method according to Claim 25, further comprising, between the inputting and replacing steps, the steps of: generating a first finite state machine ("FSM") comprised of two or more arcs which include alternatives to the incorrect word, each alternative having a rank associated therewith; selecting, as the corrected word, an alternative having a highest rank. 27. A method according to Claim 26, wherein the generating step comprises the steps of: storing one or more lexicon FSMs, each of the lexicon FSMs including plural reference words and a phonetic representation of each reference word; generating an input FSM for the incorrect word, the input FSM including the incorrect word and a phonetic representation of the incorrect word; a second selecting step for selecting two or more reference words from the lexicon FSMs based on the input FSM, the two or more reference words substantially corresponding to either a spelling of the incorrect word or to the phonetic representation of the incorrect word and having a rank associated therewith; and storing, in a first FSM, the two or more reference words and corresponding ranks associated therewith. 28. A method according to Claim 27, wherein the second selecting step comprises the step of generating an additional FSM having a plurality of states, the states comprising at least states of a lexicon FSM and states of the input FSM, and selecting the two or more reference words from the lexicon FSMs using the additional FSM. 29. A method according to Claim 28, wherein the first FSM, the lexicon FSMs, and the input FSM comprise finite state transducers ("FST"), and wherein the additional FSM comprises a finite state automaton ("FSA"). 30. A method according to Claim 28, wherein the second selecting step selects the two or more reference words by applying each state of the additional FSM to one or more of (i) a character identity module, (ii) a phonetic identity module, (iii) a character insertion module, (iv) a character deletion module, (v) a character replacement module, (vi) a character transposition module, and (vii) a character transposition completion module; and wherein (i) the character identity module determines whether characters of a reference word in the lexicon FSM match characters of the incorrect word in the input FSM, (ii) the phonetic identity module determines whether characters of the reference word are pronounced the same as characters of the incorrect word, (iii) the character insertion module determines whether a character inserted in the incorrect word causes at least part of the incorrect word to match at least part of the reference word, (iv) the character deletion module determines whether a character deleted in the incorrect word causes at least part of the incorrect word to match at least part of the reference word, (v) the character replacement module replaces characters in the incorrect word with characters in the reference word in order to determine whether at least part of the incorrect word matches at least part of the reference word, (vi) the character transposition module changes the order of two or more characters in the incorrect word and compares a changed character in the incorrect word to a corresponding character in the reference word, and (vi) the character transposition completion module compares characters in the incorrect word which were not compared by the character transposition module in order to determine if at least part of the incorrect word matches at least part of the reference word. 31. A method according to Claim 25, wherein the source comprises a remote network location; and wherein the method further comprises the step of displaying the retrieved text on a local display screen. 32. A method according to Claim 25, wherein the replacing step comprises displaying one or more corrected words and manually selecting one of the corrected words as a replacement for the incorrect word. 33. A method according to Claim 25, wherein the replacing step comprises automatically selecting one of plural corrected words as a replacement for the incorrect word. 34. A method according to Claim 25, wherein the source comprises a pre-stored database. 35. A method of correcting misspelled words in input text sequences received from a plurality of different clients, the method comprising the steps of: storing, in a memory on a server, a lexicon comprised of a plurality of reference words; receiving the input text sequences from the plurality of different clients; spell-checking the input text sequences using the reference words in the lexicon; and outputting spell-checked text sequences to the plurality of different clients. 36. A method according to Claim 35, wherein the lexicon comprises one or more lexicon finite state machines ("FSM"), the lexicon FSMs including the plurality of reference words and a phonetic representation each reference word; and wherein the spell-checking step comprises a correcting step for correcting misspelled words in each of the input text sequences substantially in parallel using the lexicon FSMs stored in the single memory. 37. A method according to Claim 36, wherein, for each text sequence, the correcting step comprises: generating an input FSM for a misspelled word in the text sequence, the input FSM including the misspelled word and a phonetic representation of the misspelled word; selecting one or more reference words from the lexicon FSMs based on the input FSM, the one or more reference words substantially corresponding to either a spelling of the misspelled word or to the phonetic representation of the misspelled word; and replacing the misspelled word in the text sequence with a selected one of the one or more reference words. 38. A method according to Claim 37, wherein the selecting step comprises the step of: generating an additional FSM having a plurality of states for the misspelled word, the states comprising at least states of a lexicon FSM and states of the input FSM; and a second selecting step for selecting the one or more reference words from the lexicon FSMs using the additional FSM. 39. A method according to Claim 38, wherein the lexicon FSMs and the input FSM comprise finite state transducers ("FST"), and wherein the additional FSM comprises a fmite state automaton ("FSA"). 40. A method according to Claim 35, further comprising, after the outputting step, the step of retrieving a document from a source using one of the spell-checked text sequences. 41. A method of selecting a replacement word for an input word in a phrase, the method comprising the steps of: determimng alternative words for the input word, the alternative words including at least one compound word which is comprised of two or more separate words, each alternative word having a rank associated therewith; and selecting, as the replacement word, an alternative word having a highest rank. 42. A method according to Claim 41, further comprising, between the determining and selecting steps, the step of generating a finite state machine ("FSM") comprised of two or more arcs which include the alternatives to the input words. 43. A method according to Claim 41, wherein, in a case that the selecting step selects a compound word as the replacement word, the method further comprises the step of replacing the input word and at least one other word in the phrase with the compound word. 44. A method according to Claim 41, further comprising, between the detecting and selecting steps, the step of adjusting the rank of each alternative based on a grammatical context of the input word in the phrase. 45. A method according to Claim 44, wherein each word in the phrase has a part of speech associated therewith, and each of the alternative words has a part of speech associated therewith; and wherein the adjusting step adjusts the rank of each alternative word based on whether a part of speech of the alternative word fits with a part of speech of at least one word adjacent to the input word. 46. A method according to Claim 45, wherein each compound word in the phrase has a single part of speech associated therewith. 47. A method according to Claim 41, further comprising, between the determining and selecting steps, the step of displaying the alternative words ranked in order; wherein the selecting step is performed manually. 48. A method according to Claim 41, wherein at least one of the alternative words comprises a word having an accent mark and/or a diacritic which is different from, and/or missing from, the input word. 49. A method of correcting grammatical errors in input text, the method comprising the steps of: generating a first finite state machine ("FSM") for the input text, the first finite state machine including alternative words for at least one word in the input text and a rank associated with each alternative word; adjusting the ranks in the first FSM in accordance with one or more of a plurality of predetermined grammatical rules; determining which of the alternative words is grammatically correct based on the ranks associated with the alternative words; and replacing the at least one word in the input text with a grammatically-correct alternative word determined in the determining step. 50. A method according to Claim 49, wherein the first FSM also includes one or more parts-of-speech for the alternative words; and wherein the determining step determines which of the alternative words is grammatically correct based in addition on the parts-of- speech of the alternative words. 51. A method according to Claim 50, wherein the adjusting step comprises: generating a second FSM for the input text based on the one or more of a plurality of predetermined grammatical rules, the second FSM including the alternative words and ranks associated with each alternative word; and combining the ranks in the second FSM with the ranks in the first FSM. 52. A method according to Claim 50, wherein the generating step comprises performing a moφhological analysis on each word in the input text in order to provide the parts-of-speech and ranks. 53. A word processing method for creating and editing text documents, the word processing method comprising the steps of: inputting text into a text document; checking the document for grammatically-incorrect words; replacing grammatically-incorrect words in the document with grammatically-correct words; and outputting the document; wherein the checking step comprises (i) generating a finite state machine ("FSM") for text in the text document, the finite state machine including alternative words for at least one word in the text and a rank associated with each alternative word, (ii) adjusting the ranks in the FSM in accordance with one or more of a plurality of predetermined grammatical rales, and (iii) determining which of the alternative words is grammatically correct based on ranks for the alternative words. 54. A machine translation method for translating text from a first language into a second language, the machine translation method comprising the steps of: inputting text in the first language; checking the text in the first language for grammatically- incorrect words; replacing grammatically-incorrect words in the text with grammatically-correct words; translating the text with the grammatically-correct words from the first language into the second language; and outputting the text in the second language; wherein the checking step comprises (i) generating a finite state machine ("FSM") for the text in the first language, the finite state machine including alternative words for at least one word in the text and a rank associated with each alternative word, (ii) adjusting the ranks in the FSM in accordance with one or more of a plurality of predetermined grammatical rules, and (iii) determining which of the alternative words is grammatically correct based on ranks for the alternative words. 55. A machine translation method for translating text from a first language into a second language, the machine translation method comprising the steps of: inputting text in the first language; translating the text from the first language into the second language; checking the text in the second language for grammatically- incorrect words; replacing grammatically-incorrect words in the text with grammatically-correct words; and υutputting the text with the grammatically-correct words; wherein the checking step comprises (i) generating a finite state machine ("FSM") for the text in the second language, the finite state machine including alternative words for at least one word in the text and a rank associated with each alternative word, (ii) adjusting the ranks in the FSM in accordance with one or more of a plurality of predetermined grammatical rales, and (iii) determining which of the alternative words is grammatically correct based on ranks for the alternative words. 56. An optical character recognition method for recognizing input character images, the optical character recognition method comprising the steps of: inputting a document image; parsing character images from the document image; performing recognition processing on parsed character images so as to produce document text; checking the document text for grammatically-incorrect words; replacing grammatically-incorrect words in the document text with grammatically correct words; and outputting the document text; wherein the checking step comprises (i) generating a finite state machine ("FSM") for the document text, the finite state machine including alternative words for at least one word in the text and a rank associated with each alternative word, (ii) adjusting the ranks in the FSM in accordance with one or more of a plurality of predetermined grammatical rales, and (iii) determining which of the alternative words is grammatically correct based on ranks for the alternative words. 57. A method of retrieving text from a source, the method comprising the steps of: inputting a search phrase comprised of a plurality of words, at least one of the plurality of words being a grammatically-incorrect word; replacing the grammatically-incorrect word in the search phrase with a grammatically-correct word in order to produce a corrected search phrase; and retrieving text from the source based on the corrected search phrase. 58. A method of spell-checking input text, the method comprising the steps of: detecting a misspelled word in the input text; storing one or more lexicon finite state machines ("FSM") in a memory, each of the lexicon FSMs including plural reference words; generating an input FSM for the misspelled word; selecting one or more reference words from the lexicon FSMs based on the input FSM, the one or more reference words substantially corresponding to a spelling of the misspelled word; and outputting selected ones of the one or more reference words. 59. A method according to Claim 58, wherein each of the lexicon FSMs also includes a phonetic representation of each reference word; wherein the input FSM also includes a phonetic representation of the misspelled word; and wherein the selecting step selects reference words from the lexicon FSMs which also substantially correspond to the phonetic representation of the misspelled word. 60. A method according to Claim 59, wherein the selecting step comprises the step of generating an additional FSM having a plurality of states, the states comprising at least states of a lexicon FSM and states of the input FSM, and selecting the one or more reference words from the lexicon FSMs using the additional FSM. 61. A method according to Claim 60, wherein the lexicon FSMs and the input FSM comprise finite state transducers ("FST"), and wherein the additional FSM comprises a finite state automaton ("FSA"). 62. A method according to Claim 61, wherein the selecting step selects the one or more reference words by applying each state of the additional FSM to one or more of (i) a character identity module, (ii) a phonetic identity module, (iii) a character insertion module, (iv) a character deletion module, (v) a character replacement module, (vi) a character transposition module, and (vii) a character transposition completion module; and wherein (i) the character identity module determines whether characters of a reference word in the lexicon FSM match characters of the misspelled word in the input FSM, (ii) the phonetic identity module determines whether characters of the reference word are pronounced the same as characters of the misspelled word, (iii) the character insertion module determines whether a character inserted in the misspelled word causes at least part of the misspelled word to match at least part of the reference word, (iv) the character deletion module determines whether a character deleted from the misspelled word causes at least part of the misspelled word to match at least part of the reference word, (v) the character replacement module replaces characters in the misspelled word with characters in the reference word in order to determine whether at least part of the misspelled word matches at least part of the reference word, (vi) the character transposition module changes the order of two or more characters in the misspelled word and compares a changed character in the misspelled word to a corresponding character in the reference word, and (vi) the character transposition completion module compares characters in the misspelled word which were not compared by the character transposition module in order to determine if at least part of the misspelled word matches at least part of the reference word. 63. A computer-readable medium which stores computer- executable process steps, the computer-executable process steps to correct misspelled words in input text, the computer-executable process steps comprising: code to detect a misspelled word in the input text; code to determine a list of alternative words for the misspelled word; and code to rank the list of alternative words based on a context of the input text. 64. A computer-readable medium according to Claim 63, further comprising: code to select one of the alternative words from the list; and code to replace the misspelled word in the text with the selected one of the alternative words. 65. A computer-readable medium according to Claim 63, wherein the code to detect comprises code to compare each word in the input text to a dictionary database and code to characterize a word as misspelled when the word either (i) does not match any words in the dictionary database, or (ii) is spelled correctly but corresponds to one of a plurality of words which are substantially similar. 66. A computer-readable medium according to Claim 63, wherein code to determine comprises: code to store one or more lexicon finite state machines ("FSM"), each of the lexicon FSMs including plural reference words and a phonetic representation of each reference word; code to generate an input FSM for the misspelled word, the input FSM including the misspelled word and a phonetic representation of the misspelled word; code to select one or more reference words from the lexicon FSMs based on the input FSM, the one or more reference words substantially corresponding to either a spelling of the misspelled word or to the phonetic representation of the misspelled word; and code to add selected ones of the one or more reference words to the list of alternative wqrds. 67. A computer-readable medium according to Claim 66, wherein the code to select comprises code to generate an additional FSM having a plurality of states, the states comprising at least states of a lexicon FSM and states of the input FSM, and code to select the one or more reference words from the lexicon FSM using the additional FSM. 68. A computer-readable medium according to Claim 67, wherein the lexicon FSMs and the input FSM comprise finite state transducers ("FST"), and the additional FSM comprises a finite state automaton ("FSA"). 69. A computer-readable medium according to Claim 67, wherein the code to select selects the one or more reference words by applying each state of the additional FSM to one or more of (i) a character identity module, (ii) a phonetic identity module, (iii) a character insertion module, (iv) a character deletion module, (v) a character replacement module, (vi) a character transposition module, and (vii) a character transposition completion module; and wherein (i) the character identity module determines whether characters of a reference word in the lexicon FSM match characters of the misspelled word in the input FSM, (ii) the phonetic identity module deterrnines whether characters of the reference word are pronounced the same as characters of the misspelled word, (iii) the character insertion module determines whether a character inserted in the misspelled word causes at least part of the misspelled word to match at least part of the reference word, (iv) the character deletion module determines whether a character deleted from the misspelled word causes at least part of the misspelled word to match at least part of the reference word, (v) the character replacement module replaces characters in the misspelled word with characters in the reference word in order to determine whether at least part of the misspelled word matches at least part of the reference word, (vi) the character transposition module changes the order of two or more characters in the misspelled word and compares a changed character in the misspelled word to a corresponding character in the reference word, and (vi) the character transposition completion module compares characters in the misspelled word which were not compared by the character transposition module in order to determine if at least part of the misspelled word matches at least part of the reference word. 70. A computer-readable medium according to Claim 63, wherein the code to rank comprises: code to generate a first finite state machine ("FSM") for the input text, the first FSM having a plurality of arcs which include the alternative words and weights associated therewith, where a weight of each alternative word corresponds to a likelihood that the alternative word, taken out of grammatical context, comprises a correctly-spelled version of the misspelled word; code to generate a second FSM for the input text and the alternative words in accordance with one or more of a plurality of predetermined grammatical rales, the second FSM having a plurality of arcs which include the alternative words and weights associated therewith, where a weight of each alternative word corresponds to a likelihood that the alternative word, taken in grammatical context, comprises a correctly- spelled version of the misspelled word; and code to add corresponding weights of the first FSM and the second FSM and ranking the alternative words in accordance with the weights of the alternative words. 71. A computer-readable medium according to Claim 64, wherein the code to select comprises code to display the list of alternative words and code to select one of the alternative words in response to a user input. 72. A computer-readable medium according to Claim 64, wherein the code to select performs selection automatically. 73. A computer-readable medium according to Claim 72, wherein the code to select selects a one of the alternative words that is ranked highest in the list of alternative words. 74. A computer-readable medium which stores computer- executable process steps, the computer-executable process steps to create and edit text documents, the computer-executable process steps comprising: code to input text into a text document; code to spell-check the text so as to replace misspelled words in the text with correctly-spelled words; and code to output the document; wherein the code to spell-check comprises code to detect misspelled words in the text, and, for each misspelled word, to determine a list of alternative words for the misspelled word, to rank the list of alternative words based on a context in the text, to select one of the alternative words from the list, and to replace the misspelled word in the text with the selected one of the alternative words. 75. A computer-readable medium which stores computer- executable process steps, the computer-executable process steps to translate text from a first language into a second language, the computer-executable process steps comprising: code to input text in the first language; code to spell-check the text in the first language so as to replace misspelled words in the text with correctly-spelled words; code to translate the text from the first language into the second language; and code to output translated text; wherein the code to spell-check step comprises code to detect misspelled words in the text, and, for each misspelled word, to determine a list of alternative words for the misspelled word, to rank the list of alternative words based on a context in the text, to select one of the alternative words from the list, and to replace the misspelled word in the document with the selected one of the alternative words. 76. A computer-readable medium which stores computer- executable process steps, the computer-executable process steps to translate text from a first language into a second language, the computer-executable process steps comprising: code to input text in the first language; code to translate the text from the first language into the second language; code to spell-check the text in the second language so as to replace misspelled words in the text with correctly-spelled words; and code to output the text; wherein the code to spell-check comprises code to detect misspelled words in the text, and, for each misspelled word, to determine a list of alternative words for the misspelled word, to rank the list of alternative words based on a context in the text, to select one of the alternative words from the list, and to replace the misspelled word in the document with the selected one of the alternative words. 77. A computer-readable medium which stores computer- executable process steps, the computer-executable process steps to perform an optical character recognition method for recognizing input character images, the computer-executable process steps comprising: code to input a document image; code to parse character images from the document image; code to perform recognition processing on parsed character images so as to produce document text; code to spell-check the document text so as to replace misspelled words in the document text with correctly-spelled words; and code to output the document text; wherein the coded to spell-check comprises code to detect misspelled words in the document text, and, for each misspelled word, to determine a list of alternative words for the misspelled word, to rank the list of alternative words based on a context in the text, to select one of the alternative words from the list, and to replace the misspelled word in the document text with the selected one of the alternative words. 78. A computer-readable medium which stores computer- executable process steps, the computer-executable process steps to retrieve text from a source, the computer-executable process comprising: code to input a search word; code to correct a spelling of the search word to produce a corrected search word; and code to retrieve text from the source that includes the corrected search word. 79. A computer-readable medium according to Claim 78, wherein the code to correct comprises: code to store one or more lexicon finite state machines ("FSM"), each of the lexicon FSMs including plural reference words and a phonetic representation of each reference word; code to generate an input FSM for the search word, the input FSM including the search word and a phonetic representation of the search word; code to select one or more reference words from the lexicon FSMs based on the input FSM, the one or more reference words substantially corresponding to either a spelling of the search word or to the phonetic representation of the search word; and code to set a selected one of the reference words as the corrected search word. 80. A computer-readable medium according to Claim 79, wherein the code to select comprises code to generate an additional FSM having a plurality of states, the states comprising at least states of a lexicon FSM and states of the input FSM, and code to select the one or more reference words from the lexicon FSM using the additional FSM. 81. A computer-readable medium according to Claim 79, wherein the lexicon FSMs and the input FSM comprise finite state transducers ("FST"), and the additional FSM comprises a finite state automaton ("FSA"). 82. A computer-readable medium according to Claim 80, wherein the code to select selects the one or more reference words by applying each state of the additional FSM to one or more of (i) a character identity module, (ii) a phonetic identity module, (iii) a character insertion module, (iv) a character deletion module, (v) a character replacement module, (vi) a character transposition module, and (vii) a character transposition completion module; and wherein (i) the character identity module determines whether characters of a reference word in the lexicon FSM match characters of the search word in the input FSM, (ii) the phonetic identity module determines whether characters of the reference word are pronounced the same as characters of the search word, (iii) the character insertion module determines whether a character inserted in the search word causes at least part of the search word to match at least part of the reference word, (iv) the character deletion module determines whether a character deleted from the search word causes at least part of the search word to match at least part of the reference word, (v) the character replacement module replaces characters in the search word with characters in the reference word in order to determine whether at least part of the search word matches at least part of the reference word, (vi) the character transposition module changes the order of two or more characters in the search word and compares a changed character in the search word to a corresponding character in the reference word, and (vi) the character transposition completion module compares characters in the search word which were not compared by the character transposition module in order to determine if at least part of the search word matches at least part of the reference word. 83. A computer-readable medium according to Claim 78, wherein the source comprises a remote network location; and wherein the computer-executable process steps further comprise code to display the retrieved text on a local display screen. 84. A computer-readable medium according to Claim 78, wherein the code to correct comprises code to display one or more corrected search words and code to select of one of the corrected search words in response to a user input. 85. A computer-readable medium according to Claim 78, wherein the code to correct comprises code to select, automatically, one of plural corrected search words. 86. A computer-readable medium according to Claim 78, wherein the source comprises a pre-stored database. 87. A computer-readable medium which stores computer- executable process steps, the computer-executable process steps to retrieve text from a source, the computer-executable process steps comprising: code to input a search phrase comprised of a plurality of words, at least one of the plurality of words being an incorrect word; code to replace the incorrect word in the search phrase with a corrected word in order to produce a corrected search phrase; and code to retrieve text from the source based on the corrected search phrase. 88. A computer-readable medium according to Claim 87, further comprising: code to generate a first finite state machine ("FSM") comprised of two or more arcs which include alternatives to the incorrect word, each alternative having a rank associated therewith; code to select, as the corrected word, an alternative having a highest rank. 89. A computer-readable medium according to Claim 88, wherein the code to generate comprises: code to store one or more lexicon FSMs, each of the lexicon FSMs including plural reference words and a phonetic representation of each reference word; code to generate an input FSM for the incorrect word, the input FSM including the incorrect word and a phonetic representation of the incorrect word; code to select two or more reference words from the lexicon FSMs based on the input FSM, the two or more reference words substantially corresponding to either a spelling of the incorrect word or to the phonetic representation of the incorrect word and having a rank associated therewith; and code to store, in a first FSM, the two or more reference words and corresponding ranks associated therewith. 90. A computer-readable medium according to Claim 89, wherein the code to select two or more reference words comprises code to generate an additional FSM having a plurality of states, the states comprising at least states of a lexicon FSM and states of the input FSM, and code to select the two or more reference words from the lexicon FSMs using the additional FSM. 91. A computer-readable medium according to Claim 90, wherein the first FSM, the lexicon FSMs, and the input FSM comprise finite state transducers ("FST"), and wherein the additional FSM comprises a finite state automaton ("FSA"). 92. A computer-readable medium according to Claim 90, wherein the code to select two or more reference words selects the two or more reference words by applying each state of the additional FSM to one or more of (i) a character identity module, (ii) a phonetic identity module, (iii) a character insertion module, (iv) a character deletion module, (v) a character replacement module, (vi) a character transposition module, and (vii) a character transposition completion module; and wherein (i) the character identity module determines whether characters of a reference word in the lexicon FSM match characters of the incorrect word in the input FSM, (ii) the phonetic identity module determines whether characters of the reference word are pronounced the same as characters of the incorrect word, (iii) the character insertion module determines whether a character inserted in the incorrect word causes at least part of the incorrect word to match at least part of the reference word, (iv) the character deletion module determines whether a character deleted in the incorrect word causes at least part of the incorrect word to match at least part of the reference word, (v) the character replacement module replaces characters in the incorrect word with characters in the reference word in order to determine whether at least part of the incorrect word matches at least part of the reference word, (vi) the character transposition module changes the order of two or more characters in the incorrect word and compares a changed character in the incorrect word to a corresponding character in the reference word, and (vi) the character transposition completion module compares characters in the incorrect word which were not compared by the character transposition module in order to determine if at least part of the incorrect word matches at least part of the reference word. 93. A computer-readable medium according to Claim 87, wherein the source comprises a remote network location; and wherein the computer-executable process steps further comprise code to display the retrieved text on a local display screen. 94. A computer-readable medium according to Claim 87, wherein the code to replace comprises code to display one or more corrected words and code to permit manual selection one of the corrected words as a replacement for the incorrect word. 95. A computer-readable medium according to Claim 87, wherein the code to replace comprises code to select, automatically, one of plural corrected words as a replacement for the incorrect word. 96. A computer-readable medium according to Claim 87, wherein the source comprises a pre-stored database. 97. A computer-readable medium which stores computer- executable process steps, the computer-executable process steps to correct misspelled words in input text sequences received from a plurality of different clients, the computer-executable process steps comprising: code to store, in a memory on a server, a lexicon comprised of a plurality of reference words; code to receive the input text sequences from the plurality of different clients; code to spell-check the input text sequences using the reference words in the lexicon; and code to output spell-checked text sequences to the plurality of different clients. 98. A computer-readable medium according to Claim 97, wherein the lexicon comprises one or more lexicon finite state machines ("FSM"), the lexicon FSMs including the plurality of reference words and a phonetic representation each reference word; and wherein the code to spell-check comprises code to correct misspelled words in each of the input text sequences substantially in parallel using the lexicon FSMs stored in the single memory. 99. A computer-readable medium according to Claim 98, wherein, for each text sequence, the code to correct comprises: code to generate an input FSM for a misspelled word in the text sequence, the input FSM including the misspelled word and a phonetic representation of the misspelled word; code to select one or more reference words from the lexicon FSMs based on the input FSM, the one or more reference words substantially corresponding to either a spelling of the misspelled word or to the phonetic representation of the misspelled word; and code to replace the misspelled word in the text sequence with a selected one of the one or more reference words. 100. A computer-readable medium according to Claim 99, wherein the code to select comprises: code to generate an additional FSM having a plurality of states for the misspelled word, the states comprising at least states of a lexicon FSM and states of the input FSM; and code to select the one or more reference words from the lexicon FSMs using the additional FSM. 101. A computer-readable medium according to Claim 100, wherein the lexicon FSMs and the input FSM comprise finite state transducers ("FST"), and wherein the additional FSM comprises a finite state automaton ("FSA"). 102. A computer-readable medium according to Claim 97, further comprising code to retrieve a document from a source using one of the spell-checked text sequences. 103. A computer-readable medium which stores computer- executable process steps, the computer-executable process steps to select a replacement word for an input word in a phrase, the computer-executable process steps comprising: code to determine alternative words for the input word, the alternative words including at least one compound word which is comprised of two or more separate words, each alternative word having a rank associated therewith; and code to select, as the replacement word, an alternative word having a highest rank. 104. A computer-readable medium according to Claim 103, further comprising code to generate a finite state machine ("FSM") comprised of two or more arcs which include the alternatives to the input words. 105. A computer-readable medium according to Claim 103, wherein, in a case that the code to select selects a compound word as the replacement word, the computer-executable process steps further comprise code to replace the input word and at least one other word in the phrase with the compound word. 106 A computer-readable medium according to Claim 103, further comprising code to adjust the rank of each alternative based on a grammatical context of the input word in the phrase. 107. A computer-readable medium according to Claim 106, wherein each word in the phrase has a part of speech associated therewith, and each of the alternative words has a part of speech associated therewith; and wherein code to adjust adjusts the rank of each alternative word based on whether a part of speech of the alternative word fits with a part of speech of at least one word adjacent to the input word. 108. A computer-readable medium according to Claim 107, wherein each compound word in the phrase has a single part of speech associated therewith. 109. A computer-readable medium according to Claim 103, further comprising code to display the alternative words ranked in order; wherein the code to select selects an alternative in response to a user input. 110. A computer-readable medium according to Claim 103, wherein at least one of the alternative words comprises a word having an accent mark and/or a diacritic which is different from, and/or missing from, the input word. 111. A computer-readable medium which stores computer- executable process steps, the computer-executable process steps to correct grammatical errors in input text, the computer-executable process steps comprising: code to generate a first finite state machine ("FSM") for the input text, the first finite state machine including alternative words for at least one word in the input text and a rank associated with each alternative word; code to adjust the ranks in the first FSM in accordance with one or more of a plurality of predetermined grammatical rales; code to determine which of the alternative words is grammatically correct based on the ranks associated with the alternative words; and code to replace the at least one word in the input text with a grammatically-correct alternative word determined by the code to deteπnine. 112. A computer-readable medium according to Claim 111, wherein the first FSM also includes one or more parts-of-speech for the alternative words; and wherein the code to determine determines which of the alternative words is grammatically correct based in addition on the parts-of- speech of the alternative words. 113. A computer-readable medium according to Claim 112, wherein the code to adjust comprises: code to generate a second FSM for the input text based on the one or more of a plurality of predetermined grammatical rales, the second FSM including the alternative words and ranks associated with each alternative word; and code to combine the ranks in the second FSM with the ranks in the first FSM. 114. A computer-readable medium according to Claim 112, wherein the code to generate comprises code to perform a moφhological analysis on each word in the input text in order to provide the parts-of- speech and ranks. 115. A computer-readable medium which stores computer- executable process steps, the computer-executable process steps to create and edit text documents, the computer-executable process steps comprising: code to input text into a text document; code to check the document for grammatically-incorrect words; code to replace grammatically-incorrect words in the document with grammatically-correct words; and code to output the document; wherein the code to check comprises code (i) to generate a finite state machine ("FSM") for text in the text document, the finite state machine including alternative words for at least one word in the text and a rank associated with each alternative word, (ii) to adjust the ranks in the FSM in accordance with one or more of a plurality of predetermined grammatical rales, and (iii) to determine which of the alternative words is grammatically correct based on ranks for the alternative words. 116. A computer-readable medium which stores computer- executable process steps, the computer-executable process steps to translate text from a first language into a second language, the computer-executable process steps comprising: code to input text in the first language; code to check the text in the first language for grammatically-incorrect words; code to replace grammatically-incorrect words in the text with grammatically-correct words; code to translate the text with the grammatically-correct words from the first language into the second language; and code to output the text in the second language; wherein the code to check comprises code (i) to generate a finite state machine ("FSM") for the text in the first language, the finite state machine including alternative words for at least one word in the text and a rank associated with each alternative word, (ii) to adjust the ranks in the FSM in accordance with one or more of a plurality of predetermined grammatical rales, and (iii) to determine which of the alternative words is grammatically correct based on ranks for the alternative words. 117. A computer-readable medium which stores computer- executable process steps, the computer-executable process steps to translate text from a first language into a second language, the computer-executable process steps comprising: code to input text in the first language; code to translate the text from the first language into the second language; code to check the text in the second language for grammatically-incorrect words; code to replace grammatically-incorrect words in the text with grammatically-correct words; and code to output the text with the grammatically-correct words; wherein the code to check comprises code (i) to generate a finite state machine ("FSM") for the text in the second language, the finite state machine including alternative words for at least one word in the text and a rank associated with each alternative word, (ii) to adjust the ranks in the FSM in accordance with one or more of a plurality of predetermined grammatical rules, and (iii) to determine which of the alternative words is grammatically correct based on ranks for the alternative words. 118. A computer-readable medium which stores computer- executable process steps, the computer-executable process steps for performing an optical character recognition method which recognizes input character images, the computer-executable process steps comprising: code to input a document image; code to parse character images from the document image; code to perform recognition processing on parsed character images so as to produce document text; code to check the document text for grammatically-incorrect words; code to replace grammatically-incorrect words in the document text with grammatically correct words; and code to output the document text; wherein the code to check comprises code (i) to generate a finite state machine ("FSM") for the document text, the finite state machine including alternative words for at least one word in the text and a rank associated with each alternative word, (ii) to adjust the ranks in the FSM in accordance with one or more of a plurality of predetermined grammatical rules, and (iii) to determine which of the alternative words is grammatically correct based on ranks for the alternative words. 119. A computer-readable medium which stores computer- executable process steps, the computer-executable process steps to retrieve text from a source, the computer-executable process steps comprising: code to input a search phrase comprised of a plurality of words, at least one of the plurality of words being a grammatically- incorrect word; code to replace the grammatically-incorrect word in the search phrase with a grammatically-correct word in order to produce a corrected search phrase; and code to retrieve text from the source based on the corrected search phrase. 120. A computer-readable medium which stores computer- executable process steps, the computer-executable process steps to spell- check input text, the computer-executable process steps comprising: code to detect a misspelled word in the input text; code to store one or more lexicon finite state machines ("FSM") in a memory, each of the lexicon FSMs including plural reference words; code to generate an input FSM for the misspelled word; code to select one or more reference words from the lexicon FSMs based on the input FSM, the one or more reference words substantially corresponding to a spelling of the misspelled word; and code to output selected ones of the one or more reference words. 121. A computer-readable medium according to Claim 120, wherein each of the lexicon FSMs also includes a phonetic representation of each reference word; wherein the input FSM also includes a phonetic representation of the misspelled word; and wherein the code to select selects reference words from the lexicon FSMs which also substantially correspond to the phonetic representation of the misspelled word. 122. A computer-readable medium according to Claim 121, wherein the code to select comprises code to generate an additional FSM having a plurality of states, the states comprising at least states of a lexicon FSM and states of the input FSM, and code to select the one or more reference words from the lexicon FSMs using the additional FSM. 123. A computer-readable medium according to Claim 122, wherein the lexicon FSMs and the input FSM comprise finite state transducers ("FST"), and wherein the additional FSM comprises a finite state automaton ("FSA"). 124. A computer-readable medium according to Claim 123, wherein the code to select selects the one or more reference words by applying each state of the additional FSM to one or more of (i) a character identity module, (ii) a phonetic identity module, (iii) a character insertion module, (iv) a character deletion module, (v) a character replacement module, (vi) a character transposition module, and (vii) a character transposition completion module; and wherein (i) the character identity module determines whether characters of a reference word in the lexicon FSM match characters of the misspelled word in the input FSM, (ii) the phonetic identity module determines whether characters of the reference word are pronounced the same as characters of the misspelled word, (iii) the character insertion module determines whether a character inserted in the misspelled word causes at least part of the misspelled word to match at least part of the reference word, (iv) the character deletion module determines whether a character deleted from the misspelled word causes at least part of the misspelled word to match at least part of the reference word, (v) the character replacement module replaces characters in the misspelled word with characters in the reference word in order to determine whether at least part of the misspelled word matches at least part of the reference word, (vi) the character transposition module changes the order of two or more characters in the misspelled word and compares a changed character in the misspelled word to a corresponding character in the reference word, and (vi) the character transposition completion module compares characters in the misspelled word which were not compared by the character transposition module in order to determine if at least part of the misspelled word matches at least part of the reference word. 125. An apparatus for correcting misspelled words in input text, the apparatus comprising: a memory which stores computer-executable process steps; and a processor which executes the computer-executable process steps so as to detect a misspelled word in the input text, to determine a list of alternative words for the misspelled word, and to rank the list of alternative words based on a context of the input text. 126. An apparatus according to Claim 125, wherein the processor further executes process steps to select one of the alternative words from the list, and to replace the misspelled word in the text with the selected one of the alternative words. 127. An apparatus according to Claim 125, wherein the processor detects the misspelled word by comparing each word in the input text to a dictionary database and characterizing a word as misspelled when the word either (i) does not match any words in the dictionary database, or (ii) is spelled correctly but corresponds to one of a plurality of words which are substantially similar. 128. An apparatus according to Claim 125, wherein the processor determines the list of alternative words by (i) storing one or more lexicon finite state machines ("FSM"), each of the lexicon FSMs including plural reference words and a phonetic representation of each reference word, (ii) generating an input FSM for the misspelled word, the input FSM including the misspelled word and a phonetic representation of the misspelled word, (iii) selecting one or more reference words from the lexicon FSMs based on the input FSM, the one or more reference words substantially corresponding to either a spelling of the misspelled word or to the phonetic representation of the misspelled word, and (iv) adding selected ones of the one or more reference words to the list of alternative words. 129. An apparatus according to Claim 128, wherein the processor selects the one or more reference words by (i) generating an additional FSM having a plurality of states, the states comprising at least states of a lexicon FSM and states of the input FSM, and (ii) selecting the one or more reference words from the lexicon FSM using the additional FSM. 130. An apparatus according to Claim 129, wherein the lexicon FSMs and the input FSM comprise finite state transducers ("FST"), and the additional FSM comprises a finite state automaton ("FSA"). 131. An apparatus according to Claim 129, wherein the processor selects the one or more reference words by applying each state of the additional FSM to one or more of (i) a character identity module, (ii) a phonetic identity module, (iii) a character insertion module, (iv) a character deletion module, (v) a character replacement module, (vi) a character transposition module, and (vii) a character transposition completion module; and wherein (i) the character identity module determines whether characters of a reference word in the lexicon FSM match characters of the misspelled word in the input FSM, (ii) the phonetic identity module determines whether characters of the reference word are pronounced the same as characters of the misspelled word, (iii) the character insertion module determines whether a character inserted in the misspelled word causes at least part of the misspelled word to match at least part of the reference word, (iv) the character deletion module determines whether a character deleted from the misspelled word causes at least part of the misspelled word to match at least part of the reference word, (v) the character replacement module replaces characters in the misspelled word with characters in the reference word in order to determine whether at least part of the misspelled word matches at least part of the reference word, (vi) the character transposition module changes the order of two or more characters in the misspelled word and compares a changed character in the misspelled word to a corresponding character in the reference word, and (vi) the character transposition completion module compares characters in the misspelled word which were not compared by the character transposition module in order to determine if at least part of the misspelled word matches at least part of the reference word. 132. An apparatus according to Claim 125, wherein the processor ranks the list of alternative words by (i) generating a first finite state machine ("FSM") for the input text, the first FSM having a plurality of arcs which include the alternative words and weights associated therewith, where a weight of each alternative word corresponds to a likelihood that the alternative word, taken out of grammatical context, comprises a correctly-spelled version of the misspelled word, (ii) generating a second FSM for the input text and the alternative words in accordance with one or more of a plurality of predetermined grammatical rales, the second FSM having a plurality of arcs which include the alternative words and weights associated therewith, where a weight of each alternative word corresponds to a likelihood that the alternative word, taken in grammatical context, comprises a correctly-spelled version of the misspelled word, and (iii) adding corresponding weights of the first FSM and the second FSM and ranking the alternative words in accordance with the weights of the alternative words. 133. An apparatus according to Claim 126, wherein the processor selects one of the alterative words by executing process steps to display the list of alternative words and in response to manual selection of one of the displayed alternative words. 134. An apparatus according to Claim 126, wherein the processor selects the one of the alternative words automatically. 135. An apparatus according to Claim 134, wherein the processor selects a one of the alternative words that is ranked highest in the list of alternative words. 136. An apparatus for creating and editing text documents, the apparatus comprising: a memory which stores computer-executable process steps; and a processor which executes the process steps stored in the memory so as (i) to input text into a text document, (ii) to spell-check the text so as to replace misspelled words in the text with correctly-spelled words, and (iii) to output the document; wherein the processor spell-checks the text by detecting misspelled words in the text, and, for each misspelled word, determining a list of alternative words for the misspelled word, ranking the list of alternative words based on a context in the text, selecting one of the alternative words from the list, and replacing the misspelled word in the text with the selected one of the alternative words. 137. An apparats for translating text from a first language into a second language, the apparatus comprising: a memory which stores computer-executable process steps; and a processor which executes the process steps stored in the memory so as (i) to input text in the first language, (ii) to spell-check the text in the first language so as to replace misspelled words in the text with correctly-spelled words, (iii) to translate the text from the first language into the second language, and (iv) to output translated text; wherein the processor spell-checks the text in the first language by detecting misspelled words in the text, and, for each misspelled word, determining a list of alternative words for the misspelled word, ranking the list of alternative words based on a context in the text, selecting one of the alternative words from the list, and replacing the misspelled word in the document with the selected one of the alternative words. 138. An apparatus for translating text from a first language into a second language, the apparatus comprising: a memory which stores computer-executable process steps; and a processor which executes the process steps stored in the memory so as (i) to input text in the first language, (ii) to translate the text from the first language into the second language, (iii) to spell-check the text in the second language so as to replace misspelled words in the text with correctly-spelled words, and (iv) to output text; wherein the processor spell-checks the text by detecting misspelled words in the text, and, for each misspelled word, determining a list of alternative words for the misspelled word, ranking the list of alternative words based on a context in the text, selecting one of the alternative words from the list, and replacing the misspelled word in the document with the selected one of the alternative words. 139. An apparatus for performing an optical character recognition method to recognize input character images, the apparatus comprising: a memory which stores computer-executable process steps; and a processor which executes the process steps stored in the memory so as (i) to input a document image, (ii) to parse character images from the document image, (iii) to perform recognition processing on parsed character images so as to produce document text, (iv) to spell-check the document text so as to replace misspelled words in the document text with correctly-spelled words, and (v) to output the document text; wherein the processor spell-checks the document text by detecting misspelled words in the document text, and, for each misspelled word, determining a list of alternative words for the misspelled word, ranking the list of alternative words based on a context in the text, selecting one of the alternative words from the list, and replacing the misspelled word in the document text with the selected one of the alternative words. 140. An apparatus for retrieving text from a source, the apparatus comprising: a memory which stores computer-executable process steps; and a processor which executes the process steps stored in the memory so as (i) to input a search word, (ii) to correct a spelling of the search word to produce a corrected search word, and (iii) to retrieve text from the source that includes the corrected search word. 141. An apparatus according to Claim 140, wherein the processor corrects the spelling of the search word by (i) storing one or more lexicon finite state machines ("FSM"), each of the lexicon FSMs including plural reference words and a phonetic representation of each reference word, (ii) generating an input FSM for the search word, the input FSM including the search word and a phonetic representation of the search word, (iii) selecting one or more reference words from the lexicon FSMs based on the input FSM, the one or more reference words substantially corresponding to either a spelling of the search word or to the phonetic representation of the search word, and (iv) setting a selected one of the reference words as the corrected search word. 142. An apparatus according to Claim 141, wherein the processor selects the one or more reference words by generating an additional FSM having a plurality of states, the states comprising at least states of a lexicon FSM and states of the input FSM, and selecting the one or more reference words from the lexicon FSM using the additional FSM. 143. An apparatus according to Claim 141, wherein the lexicon FSMs and the input FSM comprise finite state transducers ("FST"), and the additional FSM comprises a finite state automaton ("FSA"). 144. An apparatus according to Claim 142, wherein the processor selects the one or more reference words by applying each state of the additional FSM to one or more of (i) a character identity module, (ii) a phonetic identity module, (iii) a character insertion module, (iv) a character deletion module, (v) a character replacement module, (vi) a character transposition module, and (vii) a character transposition completion module; and wherein (i) the character identity module determines whether characters of a reference word in the lexicon FSM match characters of the search word in the input FSM, (ii) the phonetic identity module determines whether characters of the reference word are pronounced the same as characters of the search word, (iii) the character insertion module determines whether a character inserted in the search word causes at least part of the search word to match at least part of the reference word, (iv) the character deletion module determines whether a character deleted from the search word causes at least part of the search word to match at least part of the reference word, (v) the character replacement module replaces characters in the search word with characters in the reference word in order to determine whether at least part of the search word matches at least part of the reference word, (vi) the character transposition module changes the order of two or more characters in the search word and compares a changed character in the search word to a corresponding character in the reference word, and (vi) the character transposition completion module compares characters in the search word which were not compared by the character transposition module in order to determine if at least part of the search word matches at least part of the reference word. 145. An apparatus according to Claim 140, wherein the source comprises a remote network location; and wherein the processor further executes process steps to display the retrieved text on a local display screen. 146. An apparatus according to Claim 140, wherein the processor corrects the spelling of the search word by displaying one or more corrected search words and selecting one of the corrected search words in response to a user input. 147. An apparatus according to Claim 140, wherein the processor corrects the spelling of the search word by automatically selecting one of plural corrected search words. 148. An apparatus according to Claim 140, wherein the source comprises a pre-stored database. 149. An apparatus for retrieving text from a source, the apparatus comprising: a memory which stores computer-executable process steps; and a processor which executes the process steps stored in the memory so as (i) to input a search phrase comprised of a plurality of words, at least one of the plurality of words being an incorrect word, (ii) to replace the incorrect word in the search phrase with a corrected word in order to produce a corrected search phrase, and (iii) to retrieve text from the source based on the corrected search phrase. 150. An apparatus according to Claim 149, wherein the processor, between inputting the search phrase and replacing the incorrect word, executes computer-executable process step so as (i) to generate a first finite state machine ("FSM") comprised of two or more arcs which include alternatives to the incorrect word, each alternative having a rank associated therewith, and (ii) to select, as the corrected word, an alternative having a highest rank. 151. An apparatus according to Claim 150, wherein the processor generates the first FSM by (i) storing one or more lexicon FSMs, each of the lexicon FSMs including plural reference words and a phonetic representation of each reference word, (ii) generating an input FSM for the incorrect word, the input FSM including the incorrect word and a phonetic representation of the incorrect word, (iii) selecting two or more reference words from the lexicon FSMs based on the input FSM, the two or more reference words substantially corresponding to either a spelling of the incorrect word or to the phonetic representation of the incorrect word and having a rank associated therewith, and (iv) storing, in a first FSM, the two or more reference words and corresponding ranks associated therewith. 152. An apparatus according to Claim 151, wherein the processor selects the two or more reference words by generating an additional FSM having a plurality of states, the states comprising at least states of a lexicon FSM and states of the input FSM, and selecting the two or more reference words from the lexicon FSMs using the additional FSM. 153. An apparatus according to Claim 152, wherein the first FSM, the lexicon FSMs, and the input FSM comprise finite state transducers ("FST"), and wherein the additional FSM comprises a finite state automaton ("FSA"). 154. An apparatus according to Claim 152, wherein the processor selects the two or more reference words by applying each state of the additional FSM to one or more of (i) a character identity module, (ii) a phonetic identity module, (iii) a character insertion module, (iv) a character deletion module, (v) a character replacement module, (vi) a character transposition module, and (vii) a character transposition completion module; and wherein (i) the character identity module determines whether characters of a reference word in the lexicon FSM match characters of the incorrect word in the input FSM, (ii) the phonetic identity module determines whether characters of the reference word are pronounced the same as characters of the incorrect word, (iii) the character insertion module determines whether a character inserted in the incorrect word causes at least part of the incorrect word to match at least part of the reference word, (iv) the character deletion module determines whether a 10- character deleted in the incorrect word causes at least part of the incorrect word to match at least part of the reference word, (v) the character replacement module replaces characters in the incorrect word with characters in the reference word in order to determine whether at least part of the incorrect word matches at least part of the reference word, (vi) the character transposition module changes the order of two or more characters in the incorrect word and compares a changed character in the incorrect word to a corresponding character in the reference word, and (vi) the character transposition completion module compares characters in the incorrect word which were not compared by the character transposition module in order to determine if at least part of the incorrect word matches at least part of the reference word. 155. An apparatus according to Claim 149, wherein the source comprises a remote network location; and wherein the processor further executes process steps to display the retrieved text on a local display screen. 156. An apparatus according to Claim 149, wherein the processor replaces the incorrect word by displaying one or more corrected words and replacing the incorrect word with a user-selected one of the one or more corrected words. 157. An apparatus according to Claim 149, wherein the processor replaces the incorrect word automatically by selecting one of plural corrected words as a replacement for the incorrect word. 158. An apparatus according to Claim 149, wherein the source comprises a pre-stored database. 159. An apparatus for correcting misspelled words in input text sequences received from a plurality of different clients, the apparatus comprising: a server which includes a memory to store computer- executable process steps; and a processor which executes the process steps stored in the memory so as (i) to store, in a memory on the server, a lexicon comprised of a plurality of reference words, (ii) to receive the input text sequences from the plurality of different clients, (iii) to spell-check the input text sequences using the reference words in the lexicon, and (iv) to output spell- checked text sequences to the plurality of different clients. 160. An apparatus according to Claim 159, wherein the lexicon comprises one or more lexicon finite state machines ("FSM"), the lexicon FSMs including the plurality of reference words and a phonetic representation each reference word; and wherein the processor spell-checks the input text sequences by correcting misspelled words in each of the input text sequences substantially in parallel using the lexicon FSMs stored in the memory. 161. An apparatus according to Claim 160, wherein, for each text sequence, the processor corrects the misspelled words by (i) generating an input FSM for a misspelled word in the text sequence, the input FSM including the misspelled word and a phonetic representation of the misspelled word, (ii) selecting one or more reference words from the lexicon FSMs based on the input FSM, the one or more reference words substantially corresponding to either a spelling of the misspelled word or to the phonetic representation of the misspelled word, and (iii) replacing the misspelled word in the text sequence with a selected one of the one or more reference words. 162. An apparatus according to Claim 161, wherein the processor selects the one or more reference words by (i) generating an additional FSM having a plurality of states for the misspelled word, the states comprising at least states of a lexicon FSM and states of the input FSM, and (ii) selecting the one or more reference words from the lexicon FSMs using the additional FSM. 163. An apparatus according to Claim 162, wherein the lexicon FSMs and the input FSM comprise finite state transducers ("FST"), and wherein the additional FSM comprises a finite state automaton ("FSA"). 164. An apparatus according to Claim 159, wherein the processor executes process steps to retrieve a document from a source using one of the spell-checked text sequences. 165. An apparatus for selecting a replacement word for an input word in a phrase, the apparatus comprising: a memory which stores computer-executable process steps; and a processor which executes the process steps stored in the memory so as (i) to determine alternative words for the input word, the alternative words including at least one compound word which is comprised of two or more separate words, each alternative word having a rank associated therewith, and (ii) to select, as the replacement word, an alternative word having a highest rank. 166. An apparatus according to Claim 165, wherein the processor, between determining and selecting, further executes process steps to generate a finite state machine ("FSM") comprised of two or more arcs which include the alternatives to the input words. 167. An apparatus according to Claim 165, wherein, in a case that the processor selects a compound word as the replacement word, the processor further executes process steps to replace the input word and at least one other word in the phrase with the compound word. 168. An apparatus according to Claim 165, wherein, between determining and selecting, the processor executes process steps to adjust the rank of each alternative based on a grammatical context of the input word in the phrase. 169. An apparatus according to Claim 168, wherein each word in the phrase has a part of speech associated therewith, and each of the alternative words has a part of speech associated therewith; and wherein the processor adjusts the rank of each alternative word based on whether a part of speech of the alternative word fits with a part of speech of at least one word adjacent to the input word. 170. An apparatus according to Claim 169, wherein each compound word in the phrase has a single part of speech associated therewith. 171. An apparatus according to Claim 165, wherein the processor, between determining and selecting, executes process steps to display the alternative words ranked in order; and wherein the selecting step is performed by the processor in response to a user input. 172. An apparatus according to Claim 165, wherein at least one of the alternative words comprises a word having an accent mark and/or a diacritic which is different from, and/or missing from, the input word. 173. An apparatus for correcting grammatical errors in input text, the apparatus comprising: a memory which stores computer-executable process steps; and a processor which executes the process steps stored in the memory so as (i) to generate a first finite state machine ("FSM") for the input text, the first finite state machine including alternative words for at least one word in the input text and a rank associated with each alternative word, (ii) to adjust the ranks in the first FSM in accordance with one or more of a plurality of predetermined grammatical rales, (iii) to determine which of the alternative words is grammatically correct based on the ranks associated with the alternative words, and (iv) to replace the at least one word in the input text with a grammatically-correct alternative word determined in the determining step. 174. An apparatus according to Claim 173, wherein the first FSM also includes one or more parts-of-speech for the alternative words; and wherein the processor determines which of the alternative words is grammatically correct based in addition on the parts-of-speech of the alternative words. 175. An apparatus according to Claim 174, wherein the processor adjusts the ranks of the first FSM by (i) generating a second FSM for the input text based on the one or more of a plurality of predetermined grammatical rales, the second FSM including the alternative words and ranks associated with each alternative word, and (ii) combining the ranks in the second FSM with the ranks in the first FSM. 176. An apparatus according to Claim 175, wherein the processor generates the second FSM by performing a moφhological analysis on each word in the input text in order to provide the parts-of- speech and ranks. 177. An apparatus for creating and editing text documents, the apparatus comprising: a memory which stores computer-executable process steps; and a processor which executes the process steps stored in the memory so as (i) to input text into a text document, (ii) to check the document for grammatically-incorrect words, (iii) to replace grammatically- incorrect words in the document with grammatically-correct words, and (iv) to output the document; wherein the processor checks the document for grammatically-incorrect words by (i) generating a finite state machine ("FSM") for text in the text document, the finite state machine including alternative words for at least one word in the text and a rank associated with each alternative word, (ii) adjusting the ranks in the FSM in accordance with one or more of a plurality of predetermined grammatical rales, and (iii) determining which of the alternative words is grammatically correct based on ranks for the alternative words. 178. An apparatus for translating text from a first language into a second language, the apparatus comprising: a memory which stores computer-executable process steps; and a processor which executes the process steps stored in the memory so as (i) to input text in the first language, (ii) to check the text in the first language for grammatically-incorrect words, (iii) to replace grammatically-incorrect words in the text with grammatically-correct words, (iv) to translate the text with the grammatically-correct words from the first language into the second language, and (v) to output the text in the second language; wherein the processor checks the text in the first language by (i) generating a finite state machine ("FSM") for the text in the first language, the finite state machine including alternative words for at least one word in the text and a rank associated with each alternative word, (ii) adjusting the ranks in the FSM in accordance with one or more of a plurality of predetermined grammatical rales, and (iii) determining which of the alternative words is grammatically correct based on ranks for the alternative words. 179. An apparatus for translating text from a first language into a second language, the apparatus comprising: a memory which stores computer-executable process steps; and a processor which executes the process steps stored in the memory so as (i) to input text in the first language, (ii) to translate the text from the first language into the second language, (iii) to check the text in the second language for grammatically-incorrect words, (iv) to replace grammatically-incorrect words in the text with grammatically-correct words, and (v) to output the text with the grammatically-correct words; wherein the processor checks the text in the second language by (i) generating a finite state machine ("FSM") for the text in the second language, the finite state machine including alternative words for at least one word in the text and a rank associated with each alternative word, (ii) adjusting the ranks in the FSM in accordance with one or more of a plurality of predetermined grammatical rales, and (iii) determining which of the alternative words is grammatically correct based on ranks for the alternative words. 180. An apparatus for performing an optical character recognition method to recognize input character images, the apparatus comprising: a memory which stores computer-executable process steps; and a processor which executes the process steps stored in the memory so as (i) to input a document image, (ii) to parse images from the document image, (iii) to perform recognition processing on parsed character images so as to produce document text, (iv) to check the document text for grammatically-incorrect words, (v) to replace grammatically-incorrect words in the document text with grammatically correct words, and (vi) to output the document text; wherein the processor checks the document text by (i) generating a finite state machine ("FSM") for the document text, the finite state machine including alternative words for at least one word in the text and a rank associated with each alternative word, (ii) adjusting the ranks in the FSM in accordance with one or more of a plurality of predetermined grammatical rales, and (iii) determining which of the alternative words is grammatically correct based on ranks for the alternative words. 181. An apparatus for retrieving text from a source, the apparatus comprising: a memory which stores computer-executable process steps; and a processor which executes the process steps stored in the memory so as (i) to input a search phrase comprised of a plurality of words, at least one of the plurality of words being a grammatically- incorrect word, (ii) to replace the grammatically-incorrect word in the search phrase with a grammatically-correct word in order to produce a corrected search phrase, and (iii) to retrieve text from the source based on the corrected search phrase. 182. An apparatus for spell-checking input text, the apparatus comprising: a memory which stores computer-executable process steps; and a processor which executes the process steps stored in the memory so as (i) to detect a misspelled word in the input text, (ii) to store one or more lexicon finite state machines ("FSM") in the memory, each of the lexicon FSMs including plural reference words, (iii) to generate an input FSM for the misspelled word, (iv) to select one or more reference words from the lexicon FSMs based on the input FSM, the one or more reference words substantially corresponding to a spelling of the misspelled word, and (v) to output selected ones of the one or more reference words. 183. An apparatus according to Claim 182, wherein each of the lexicon FSMs also includes a phonetic representation of each reference word; wherein the input FSM also includes a phonetic representation of the misspelled word; and wherein the processor executes process steps to select reference words from the lexicon FSMs which also substantially correspond to the phonetic representation of the misspelled word. 184. An apparatus according to Claim 183, wherein the processor selects reference words by generating an additional FSM having a plurality of states, the states comprising at least states of a lexicon FSM and states of the input FSM, and selecting the one or more reference words from the lexicon FSMs using the additional FSM. 185. An apparatus according to Claim 184, wherein the lexicon FSMs and the input FSM comprise finite state transducers ("FST"), and wherein the additional FSM comprises a finite state automaton ("FSA"). 186. An apparatus according to Claim 185, wherein the processor selects the one or more reference words by applying each state of the additional FSM to one or more of (i) a character identity module, (ii) a phonetic identity module, (iii) a character insertion module, (iv) a character deletion module, (v) a character replacement module, (vi) a character transposition module, and (vii) a character transposition completion module; and wherein (i) the character identity module determines whether characters of a reference word in the lexicon FSM match characters of the misspelled word in the input FSM, (ii) the phonetic identity module determines whether characters of the reference word are pronounced the same as characters of the misspelled word, (iii) the character insertion module determines whether a character inserted in the misspelled word causes at least part of the misspelled word to match at least part of the reference word, (iv) the character deletion module determines whether a character deleted from the misspelled word causes at least part of the misspelled word to match at least part of the reference word, (v) the character replacement module replaces characters in the misspelled word with characters in the reference word in order to determine whether at least part of the misspelled word matches at least part of the reference word, (vi) the character transposition module changes the order of two or more characters in the misspelled word and compares a changed character in the misspelled word to a corresponding character in the reference word, and (vi) the character transposition completion module compares characters in the misspelled word which were not compared by the character transposition module in order to determine if at least part of the misspelled word matches at least part of the reference word. 187. An apparatus for correcting misspelled words in input text, the apparatus comprising: detecting means for detecting a misspelled word in the input text; determining means for determining a list of alternative words for the misspelled word; and ranking means for ranking the list of alternative words based on a context of the input text. 188. An apparatus for retrieving text from a source, the apparatus comprising: inputting means for inputting a search word; correcting means for correcting a spelling of the search word to produce a corrected search word; and retrieving means for retrieving text from the source that includes the corrected search word. 189. An apparatus for retrieving text from a source, the apparatus comprising: inputting means for inputting a search phrase comprised of a plurality of words, at least one of the plurality of words being an incorrect word; replacing means for replacing the incorrect word in the search phrase with a corrected word in order to produce a corrected search phrase; and retrieving means for retrieving text from the source based on the corrected search phrase. 190. An apparams for correcting misspelled words in input text sequences received from a plurality of different clients, the apparams comprising: storing means for storing, in a memory on a server, a lexicon comprised of a plurality of reference words; receiving means for receiving the input text sequences from the plurality of different clients; spell-checking means for spell-checking the input text sequences using the reference words in the lexicon; and outputting means for outputting spell-checked text sequences to the plurality of different clients. 191. An apparatus for selecting a replacement word for an input word in a phrase, the apparams comprising: determining means for determining alternative words for the input word, the alternative words including at least one compound word which is comprised of two or more separate words, each alternative word having a rank associated therewith; and selecting means for selecting, as the replacement word, an alternative word having a highest rank. 192. An apparams for correcting grammatical errors in input text, the apparams comprising: generating means for generating a first finite state machine ("FSM") for the input text, the first finite state machine including alternative words for at least one word in the input text and a rank associated with each alternative word; adjusting means for adjusting the ranks in the first FSM in accordance with one or more of a plurality of predetermined grammatical rales; determining means for determining which of the alternative words is grammatically correct based on the ranks associated with the alternative words; and replacing means for replacing the at least one word in the input text with a grammatically-correct alternative word determined by the determining means. 193. An apparams for retrieving text from a source, the apparams comprising: inputting means for inputting a search phrase comprised of a plurality of words, at least one of the plurality of words being a grammatically-incorrect word; replacing means for replacing the grammatically-incorrect word in the search phrase with a grammatically-correct word in order to produce a corrected search phrase; and retrieving means for retrieving text from the source based on the corrected search phrase. 194. An apparatus for spell-checking input text, the apparatus comprising: detecting means for detecting a misspelled word in the input text; storing means for storing one or more lexicon finite state machines ("FSM") in a memory, each of the lexicon FSMs including plural reference words; generating means for generating an input FSM for the misspelled word; selecting means for selecting one or more reference words from the lexicon FSMs based on the input FSM, the one or more reference words substantially corresponding to a spelling of the misspelled word; and outputting means for outputting selected ones of the one or more reference words.

Inventors: ROCHE EMMANUEL, SCHABES YVES, ROCHE, EMMANUEL, SCHABES, YVES
Applicants: TERAGRAM CORP, TERAGRAM CORPORATION
Application year: 1999
Main classification: G06F17
IPC codes: G06F17