Abstract:
A computer implemented method and system for natural language processing ambiguity resolution includes storing an ontology specifying a set of grammatical rules. A phrase comprising at least one current word to be processed is retrieved. A current word from the phrase is annotated with possible ontological classes according to the ontology. Any ontological rules associated with the possible ontological classes are retrieved. Ontological classes are eliminated based on the ontological rules. A surviving possible ontological class is determined to be an accurate ontological class for the current word. In another aspect of this disclosure, an ontology is stored in computer memory, the ontology having multiple ontological classifications, and word instances, each word instance associated with at least one of the ontological classifications. All word instances belonging to the selected ontological classification are retrieved.

Description:
BACKGROUND 
     1. Field 
     This disclosure relates generally to data processing of linguistic data, and, more particularly, to ontology-driven natural language processing. 
     2. Background 
     Natural language processing utilizes software to analyze and understand human languages. Understanding a human language requires knowing what a word or phrase stands for, and how to link concepts together in meaningful ways. One method in which this is accomplished is dictionary-based annotation. Dictionaries are prepared with lists of words, including common parts of speech, such as nouns, verbs, conjunctions, etc. The dictionaries are then used to annotate each word in a phrase to be processed. Subsequently, post-processing must be done to eliminate redundant annotations by utilizing grammatical rules. Because of the large number of possible grammatical rules in a language, hundreds or thousands of rules may need to be applied to each word. 
     BRIEF SUMMARY 
     In one aspect of this disclosure, a computer implemented system and method is disclosed for natural language processing ambiguity resolution. The system and method comprise storing, in computer memory, an ontology specifying a set of grammatical rules. Using the computer processor, a phrase comprising at least one current word to be processed is retrieved. A current word from the phrase is annotated with possible ontological classes to which the current word belongs according to the ontology. Using the processor, any ontological rules associated with the possible ontological classes to which the current word belongs are retrieved. Possible ontological classes are eliminated based on the ontological rules. A surviving possible ontological class is determined to be an accurate ontological class for the current word. 
     In another aspect of this disclosure, a computer-implemented system and method is disclosed for generating a dictionary from a selected ontological classification for use in natural language processing. A request to generate a dictionary from a selected ontological classification is received using a computer processor. All word instances belonging to the selected ontological classification are retrieved using the computer processor and compiled into a list. 
     The foregoing has outlined rather generally the features and technical advantages of one or more embodiments of this disclosure in order that the following detailed description may be better understood. Additional features and advantages of this disclosure will be described hereinafter, which may form the subject of the claims of this application. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       This disclosure is further described in the detailed description that follows, with reference to the drawings, in which: 
         FIG. 1  is a high level representation of an illustrative natural language processing ambiguity resolution and an ontology-based dictionary generator system; 
         FIG. 2A  illustrates the effects of natural language processing ambiguity resolution on an example ambiguous word; 
         FIG. 2B  continues to illustrate the effects of natural language processing ambiguity resolution on the example ambiguous word of  FIG. 2A ; 
         FIG. 2C  continues to illustrate the effects of natural language processing ambiguity resolution on the example ambiguous word of  FIG. 2A ; 
         FIG. 3  illustrates a representative ontological library; 
         FIG. 4  illustrates a preferred sequence of steps for natural language processing ambiguity resolution; 
         FIG. 5  illustrates a continuing sequence of steps from  FIG. 4  for processing ambiguity resolution; and 
         FIG. 6  illustrates a preferred sequence of steps for ontology-based dictionary generation. 
     
    
    
     DETAILED DESCRIPTION 
     This application discloses a computer-implemented system and method for natural language processing ambiguity resolution and generating an ontology-based dictionary. The natural language processing ambiguity resolution system and method utilizes a language ontology instead of simplistic word dictionaries to understand language. Language ontologies are known in the art, but will be briefly discussed here for the sake of clarity (and with more detail in  FIG. 3  below). The ontology creates a hierarchy representing the structure of the language. Broad concepts in the language may be represented as Ontological Classes, which may be subdivided further into Ontological Sub-Classes. For example, a major Ontological Class like “words” may be divided into Sub-Classes like “nouns,” “verbs,” “adjectives,” etc., which may be further divided into further Sub-Classes, such as “pronouns,” “single-word verbs,” “multi-word verbs,” etc. The entire structure of the language may thus be represented by the ontology. Ontological Classes may be related by Ontological Relations. For example, an Ontological Sub-Class of “nouns” called “pronouns” may be related to the former as inclusive within the “nouns” class. Similarly, features or characteristics of the Ontological Classes may be stored in the ontological model as Ontological Attributes. Ontological Rules may be associated with Ontological Classes, Relations and Instances (i.e., specific words) in accordance with actual grammatical rules used by the represented language. 
     The use of an ontology conveys a benefit to natural language processing ambiguity resolution. Because the structure of the language is represented by the ontology, it is unnecessary for the system to apply every possible grammatical rule to the ambiguous word. Instead, only the rules associated with the possible Ontological Classes, Relations, etc. need be retrieved and applied to the word to resolve its grammatical ambiguity, greatly reducing the processing time necessary to resolve word ambiguity. The language ontology may also be used to generate conventional dictionaries by, for example, retrieving every word instance directly associated with a selected Ontological Class, and compiling it into a list. 
       FIG. 1  is a high level representation of an illustrative natural language processing ambiguity resolution and an ontology-based dictionary generator system  100 . The natural language processing ambiguity resolution and an ontology-based dictionary generator system  100  preferably includes a central processing unit (“CPU”)  105 , memory  120 , network device  115  and input/output device  110 . The CPU  105  receives and executes program instructions. Memory  120  may be provided for both long term and short-term memory (i.e., random access memory and hard disk storage), and provide data storage for the CPU  105 . Network device  115  may provide connectivity to a network, which may be, for example, an intranet, extranet or the Internet. Input/output device  110  may provide accessibility for human operators, including devices such as keyboards, mice, displays, touch screens, etc. 
     Software processes ambiguity resolver  130  and the dictionary generator  135  may be stored in memory  120  and are executable by the CPU  105  to operate on the natural language processing ambiguity resolution and ontology-based dictionary generator system  100 , facilitating or executing the actual processes of resolving word ambiguity and generating word dictionaries from ontological libraries. The ambiguity resolver  130  and the dictionary generator  135  may be separate software processes, or they may be implemented within the same software process. The ontological library  125  may be stored as a data structure in memory  120  (or in other storage accessible by the system  100 ), and include an ontological universe for one or more desired languages. 
       FIGS. 2A, 2B and 2C  are high-level overviews of the process by which natural language processing ambiguity resolution may be executed on an example ambiguous word  205  contained in a to-be-resolved phrase  200 . In  FIG. 2A , a phrase to be processed  200  is received by the CPU  105 . Then, one of the words in the received phrase is selected for processing by the ambiguity resolver  130 . In this example, the word “these”  205  is selected. In  FIG. 2B , the selected word “these”  205  is then annotated with possible Ontological Classes and Sub-Classes of which “these”  205  is directly ontologically related (based on the ontological library  125 ). In this example, a “pronoun” annotation  210  and “adjective” annotation  215  are used to annotate or otherwise mark the selected word “these”  205 . In the example shown in  FIG. 2C , the “adjective” annotation  215  is eliminated based on an Ontological Rule, associated with the “adjective” sub-class, that, for example, adjectives must be followed by nouns. When the ambiguity resolver  130  applies this rule to the phrase  200 , it determines that there is no “noun” word following the selected word “these”  205 . Therefore, based on the Ontological Rule, the ambiguity resolver  130  may determine that the selected word “these”  205  cannot be an adjective, eliminating it as depicted. With only one annotation remaining in this example, the ambiguity resolver  130  has determined that the selected word “these”  205  is a pronoun. Similarly, if there had been an adjacent word following the word “these”  205  in the phrase  200 , a determination of the classification of the adjacent word would be necessary to make a determination of whether the selected word “these”  205  is an adjective. If such a determination were made, then, based on the Ontological Rule, and the fact that the adjacent word is not a noun, a determination could be made by the ambiguity resolver  130  that the selected word “these”  205  is not an adjective, and therefore must be a pronoun. 
       FIG. 3  illustrates a representative (simplified) ontological library  300 . An actual ontological library  300  corresponding to a real language will necessarily be more complex and involved. The ontological library contains a class  305  (which may correspond to large categories of words, such as nouns, verbs, etc.), sub-classes  310   a  and  310   b  (which may correspond to more specific categories of words, such as pronouns, proper nouns, etc.) and instances  315   a ,  315   b ,  315   c  and  315   d  (which may correspond to specific instances of words belonging to the related respective class or sub-class. These classes are related via Ontological Relationships such as relationships  320   a  and  320   b , designating, for example, that sub-class  310   b  belongs to class  305 , and instance  315   d  belongs to sub-class  310   b  (and in turn, class  305 ). Certain classes, sub-classes or instances may have one or more Ontological Rules  305   a ,  305   b ,  305   c ,  305   d  and  305   e  associated with them. These may correspond to broad rules of grammar, usage-specific exceptions, norms of use, or any other rule of language that may be required to accurately represent the desired ontologically. 
       FIGS. 4 and 5  illustrate a preferred sequence of steps for natural language processing ambiguity resolution. An ontological library or universe  125  for one or more desired languages is received and stored, preferably in memory  120  of the natural language processing ambiguity resolution system  100  (step  400 ). A phrase to be processed is received by CPU  105  of the natural language processing ambiguity resolution system  100  (step  405 ). Each word in the phrase is annotated using the ambiguity resolver  130  with one or more ontological classifications to which the word belongs, as specified in the ontological library or universe  125  (step  410 ). The ambiguity resolver  130  may then determine whether there are any remaining ambiguities for any of the words in the phrase (step  415 ), an ambiguity being the presence of more than one ontological class annotation on a single word. 
     Referring to  FIG. 5 , a word having an ambiguity is selected for processing by the ambiguity resolver  130  (step  500 ). The current word may be different than a previously selected word to ensure the system  100  continuously moves on to processing a new word with new information. As ambiguity is resolved, some ontological rules that did not previously apply may then be applied. Ontological rules associated with the ontological annotations for the word are then retrieved from the ontological library  125  (step  505 ). Rules are preferably retrieved from any level of the ontological hierarchy. For example, rules associated with the word itself, with the sub-class of which the word belongs, and rules associated with a greater class inclusive of the sub-class may all be retrieved for use in resolving ambiguity. Every useable ontological rule is utilized to eliminate ontological class annotations from the current word (step  510 ). The system  100  may then return to  FIG. 4  for continued processing. If no ambiguities remain (step  415 ), then the process may end. 
     Some rules may not yet be useable because of ambiguity of surrounding words. For example, if an ontological rule states that an adjective must precede a noun, but the next word in the sentence has not yet been determined to be a noun, the ambiguity may be unresolveable until the system  100  processes the adjacent word. These ambiguities will be resolved in subsequent loops of the system  100 , since the process continues as long as there are unresolved ambiguities, and the process always selects a new word different than the previous word for processing. Naturally, the system may in some cases encounter unresolveable ambiguities. A count may be imposed on to trigger termination of the loop, and a generated notification of the problem may be sent to an administrator if the system  100  detects that it is trapped in an infinite loop with an unresolveable ambiguity. 
       FIG. 6  illustrates an illustrative sequence of steps for ontology-based dictionary generation. An ontological library or universe  125  may be stored in memory  120  of system  100  (step  600 ). The system  100  may then receive a request to generate a dictionary for an ontological classification (step  605 ). All word instances associated with the ontological classification may then be retrieved and compiled into a list by the dictionary generator  135  (step  610 ). 
     Aspects of the present invention have been described with respect to block diagrams and/or flowchart illustrations of methods, apparatus (system), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer instructions. These computer instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The aforementioned programs can be written in any combination of one or more programming languages, including low-level, high-level, object-oriented or non object-oriented languages, such as Java, Smalltalk, C, and C++. The program code may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). Alternatively, the functions of the aforementioned programs can be implemented in whole or in part by computer circuits and other hardware (not shown). 
     The foregoing description of various embodiments of the present invention has been presented for purposes of illustration and description. It is not intended to be exhaustive nor to limit the invention to the precise form disclosed. Many modifications and variations are possible. Such modifications and variations that may be apparent to a person skilled in the art of the invention are intended to be included within the scope of the invention as defined by the appended claims.