Patent Publication Number: US-10789410-B1

Title: Identification of source languages for terms

Description:
BACKGROUND 
     Despite the existence of spellchecking software, typographical errors (“typos”) made by an author, an editor, a publisher, etc. continue to exist with respect to electronic books (eBooks). To prevent these issues, quality checks are performed on electronic documents to identify portions of the document that have been misspelled, misconverted (i.e., an optical recognition system misidentified a letter in a scan of a physical document), or which are otherwise incorrect. As these types of quality checks must be performed on hundreds of thousands of English eBooks per week, time constraints necessitate that the systems that perform the quality checks must be at least partially automated. 
     However, despite using a relatively large lexicon of known words and various types of heuristics, current systems tend to identify many words/terms that are believed to be typos/misspellings, but end up being false positives (i.e., words that are not typos). One of the major sources of false positives is words from other languages that have been transliterated into the English language. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same reference numbers in different figures indicate similar or identical items. 
         FIG. 1  is an illustrative computing architecture of a quality control service that identifies transliterated words in an electronic document. 
         FIG. 2  is an illustrative environment for providing quality control services for identifying transliterated words in an electronic document. 
         FIG. 3  is a flow diagram of an illustrative process for training a model for determining whether a word is a transliterated word from a different language. 
         FIG. 4  is a flow diagram of an illustrative process for determining that a word is a transliterated word from a different language. 
         FIG. 5  is an example illustration of a process for determining that a word is a transliterated word from a different language. 
         FIG. 6  is a flow diagram of an illustrative process for using more than one feature model to determine that a word is a transliterated word from a different language. 
     
    
    
     DETAILED DESCRIPTION 
     This disclosure is generally directed to an automated system for identifying transliterated and/or transcribed words in an electronic document (collectively referred to herein as “transliterated word(s)”). The system may be trained to evaluate a set of words, and determine likelihoods that individual words in the set of words are transliterated (i.e., they are not native to the language and/or alphabet of the electronic document). In some embodiments, the model may be further configured to identify a language and/or alphabet that the individual word was transliterated from. 
     A transliterated word or term is a word that (i) traditionally occurs in a particular language or which is normally spelled using a particular alphabet, and that (ii) has been converted and/or spelled out using a different alphabet or in a different language. An example of a transliterated word is “gorakhanathi,” which is a transliteration of a Sanskrit term for the sect of yogis that follow the Nath Hindu monastic movement founded by Guru Horakhnath. Since there is not a corresponding English term for this sect, the Sanskrit term is transliterated using letters of the Latin alphabet as “gorakhanathi.” Moreover, a transcribed word may refer to a word/term that is converted to a different form, such as a word/term that is converted from a first representation (e.g., speech) to a second, different representation (e.g., text). For the purposes of this discussion, an electronic document may include any digital body of text, including eBooks, electronic magazines, blog posts, microblog entries, SMS messages, webpage texts, emails, as well as other examples of a digital body of script. 
     The automated system described herein first accesses a set of one or more words. These words may be generated by the system, received from another computing system, or may be input into the system by a user. In some embodiments, the set of words may correspond to a list of potential typos identified by a quality control system during an evaluation of an electronic document. The quality control system may then compare the set of words to a lexicon of known words, and/or apply one or more heuristics to identify and remove valid words/portions of texts that were incorrectly flagged as potential typos (e.g., a function derived via machine learning that is designed to identify false positives). For example, the quality control system may apply one or more rules to identify whether a word corresponds to a proper noun. 
     The system may then apply one or more classifiers to the set of words to identify transliterated words. In some embodiments, one of the classifiers may be a linear, binary classifier that is able to determine a likelihood that a word is of a language. For example, a classifier may be trained to break an input word into its corresponding n-grams, and then determine based on the n-grams of the input word whether the word is an English/Latin word, or whether the word has been transliterated from a different language/alphabet. An “n-gram” is a contiguous sequence of “n” items from a given sequence of text. For example, an n-gram of size 1 is a unigram (e.g., the unigrams of the word “green” are “g,” “r,” “e,” “e,” and “n”), an n-gram of size 2 is a bigram (e.g., the bigrams of the word “green” are “gr,” “re,” “ee,” and “en”), an n-gram of size 3 is a trigram (e.g., the trigrams of the word “green” are “gre,” “ree,” and “een”), and so on. Alternatively or in addition, the classifier may be trained to identify a language/alphabet that the input word has been transliterated from based on the n-grams of the input word. In some embodiments, the classifiers may also output a confidence score associated with a determination of whether a word has been transliterated, and/or the language/alphabet the word has been transliterated from. 
     In some embodiments, the system may have trained the classifiers using one or more language models. The language models may have been built using one or more training corpuses, where individual training corpuses may correspond to one or more electronic documents of a known language/alphabet (e.g., one or more of dictionaries of the corresponding language, electronic documents in the corresponding language, text from webpage(s) in the corresponding language, etc.). As part of the training, the system may break the words of the document into their corresponding n-grams. For example, where an Arabic dictionary is used to train the system, the system may break the word “eftar” (i.e., the Arabic term for a huge feast enjoyed during Ramazan) into the corresponding trigrams of “_ef,” “eft,” “fta,” “tar,” and “ar_.” The system may then treat each distinct character n-gram of a word as a feature of the word. 
     The system may then perform an analysis of the features of words in a training corpus to identify feature characteristics of the corresponding language of the training corpus. In some embodiments, the system may be configured to consider only n-grams that appear in at least a threshold number of different words in the training corpus in its analysis. The system may then build a feature model for the corresponding language. The feature model may identify characteristics of the language, such as a distribution of n-grams across the language, associations between n-grams in the language, feature patterns occurring in the language, etc. For example, the model may determine a frequency that the trigram “fta” is present in Arabic, and may determine a frequency that the trigram “fta” is associated with “tar” (e.g., how often are they present in the same word, at what frequency do they follow one another, etc.). The system may then perform similar evaluations for one or more other training corpuses that correspond to other languages. Based on the evaluations of each of the languages, the system may then build feature models for each of the languages. 
     The system may then train the classifiers using the feature models so that the classifiers are able to use the feature characteristics identified by the models to identify whether a word is a transliterated word and/or what language/alphabet the word was transliterated from. 
     The techniques, apparatuses, and systems described herein may be implemented in a number of ways. Example implementations are provided below with reference to the following figures. 
       FIG. 1  is an illustrative computing architecture  100  quality control service that identifies transliterated words in an electronic document. The computing architecture  100  may be used to implement the various systems, devices, and techniques discussed herein. In various embodiments, the quality control service  102  can be implemented or hosted by one or more servers, server farms, data centers, or any of the other computing devices. 
     In the illustrated implementation, the computing architecture  100  includes one or more processing units  104  coupled to a memory  106 . The computing architecture may also include a network interface  108 . The quality control service  102  can include a training module  110 , a classification module  112 , and a quality control module  114  stored in the memory  108 .  FIG. 1  further illustrates the training module  110  and the training corpus  118  as being stored on a model training service  109 . In some embodiments, the model training service  109  may be implemented on a separate computing device from quality control service  102 . Alternatively, the model training service  109  may be stored on the memory  106  of the quality control service  102 . 
     As used herein, the term “module” is intended to represent example divisions of executable instructions for purposes of discussion, and is not intended to represent any type of requirement or required method, manner or organization. Accordingly, while various “modules” are described, their functionality and/or similar functionality could be arranged differently (e.g., combined into a fewer number of modules, broken into a larger number of modules, etc.). Further, while certain functions and modules are described herein as being implemented by software and/or firmware executable on a processor, in other instances, any or all of the modules can be implemented in whole or in part by hardware (e.g., a specialized processing unit, etc.) to execute the described functions. In various implementations, the modules described herein in association with quality control service  102  can be executed across multiple devices. 
     In some embodiments, a training module  110  may generate one or more feature/classification models  116 ( 1 )- 116 (N). For example, training module  110  may generate feature/classification models  116 ( 1 )- 116 (N) using one or more training corpuses  118 . A training corpus  118  may correspond to a collection one or more electronic documents  120  that are written in a known language/alphabet, and/or features  122  that have been transformed and/or otherwise extracted from the electronic documents  120 . A feature/classification model  116  may be a computational model that uses characteristics of a corresponding language (e.g., a distribution of n-grams across the language, associations between n-grams in the language, feature patterns occurring in the language, etc.) to probabilistically determine the native language and/or alphabet of a word based on the n-grams present within the word. In some embodiments, the electronic documents  120  may include one or more electronic portions of text that are written in a particular language and/or alphabet (e.g., one or more of dictionaries of the corresponding language, electronic documents in the corresponding language, text from webpage(s) in the corresponding language, etc.). For example, for a training corpus  118  for Arabic, electronic documents  120  may include the Quran. 
     When generating the feature/classification models  116 ( 1 )- 116 (N), training module  110  may analyze the electronic documents  120 . In some embodiments, the training module  110  may extract features  122  from the electronic documents  120 . For example, training module  110  may break the words of an electronic document  120  into their corresponding n-grams. For example, where an Arabic dictionary is used to train the system, the system may break the Arabic word “alaikum” into the corresponding trigrams of “_al,” “ala,” “lai,” “aik,” “iku,” “kum,” and “um_.” Training module  110  may store the n-grams of the words of the electronic document  120  in as features  122 . For example, training module may treat each distinct character n-gram of a word as a feature of that word. 
     Training module  110  may then perform an analysis of the features  122  extracted from the electronic documents  120  to identify feature characteristics of the corresponding language of the training corpus  118 . Characteristics of a language and/or alphabet corresponding to training corpus  118 , may include a distribution of n-grams across the language, associations between n-grams in the language, feature patterns that are indicative of a word being of the language, etc. In some embodiments, when performing this analysis, the training module  110  may only consider only n-grams that appear in at least a threshold number of different words in the training corpus  118 , n-grams that appear a threshold number of times in features  122 , a preset number of the highest occurring n-grams in features  122 , or a combination thereof. 
     Training module  110  may then build a feature/classification model  116 ( 1 ) for the language corresponding to the training corpus  118  based on the characteristics of a language and/or alphabet corresponding to training corpus  118 . A feature/classification model  116 ( 1 ) may be an algorithmic model that probabilistically determines the native language and/or alphabet of a word based on the n-grams present within the word. For example, for a word W whose n-gram sequence is (t 1 , t 2 , . . . , t m ), a first potential feature/classification model  116  for determining a probability of belonging to a language L j  may be given by:
 
 P ( W∈L   j )= P (&lt; t   1   ,t   2   , . . . , t   m   &gt;∈L )= P ( T   L     j     (1)   =t   1   ,T   L     j     (2)   =t   2   , . . . , T   L     j     (m)   =t   m ) P ( L   j )
 
where T L     j     (i)  denotes the i th  n-gram in the ordered sequence of n-grams occurring in a word belonging to language L j  and P(L j ) denotes the a-priori probability of the language L j (i.e. the general/unconditional probability that a randomly given word will belong to that language). Moreover, since the number of n-gram in a word depends on the word length, which is a variable, the features may identify when an n-gram occurs at the end of a word. In some embodiments, where feature/classification model  116  assumes that the probabilities of n-grams in a word are independent of each other and that all languages are equally likely to occur, the probabilistic algorithm may correspond to:
 
     
       
         
           
             
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     In another example, a feature/classification model  116  may comprise vector representations of n-gram characteristics for a corresponding language. In some embodiments, training module  110  may generate a feature/classification model  116  by passing features  122  for a training corpus  118  to a random forest classifier, which trains a feature/classification model  116  that includes vector representations of n-grams for a corresponding language. Some feature/classification models  116  may incorporate positive or negative correlations that individual n-grams and/or combinations of n-grams occur in a corresponding language. For example, some feature model may apply a negative weight for n-grams do not exist in some languages. 
     The classification module  112  can be executable by the one or more processing units  104  to determine whether a word has been transliterated from another language and/or alphabet. Alternatively or in addition, the classification module  112  may be executable to determine a language and/or alphabet that the word has been transliterated from. For example, the classification module may be able to determine that the word “datsu-sura” (i.e., a traditional Japanese dance) is a transliterated word, and/or that it has been transliterated from the Japanese language. 
     In some embodiments, classification module  112  may apply a classifier that is able to determine a confidence score that a word is a transliterated word from a different language and/or alphabet from the language/alphabet of a source document of the word. For example, classification module  112  may assign a confidence score that corresponds to a determined likelihood that the word is a transliterated word, and the confidence score can take the form of a numerical value, text, a symbol, one or more characters, and so on. In another example, classification module  112  may assign multiple confidence scores to a word, with individual confidence scores corresponding to a likelihood determined by classification module  112  that the word has been transliterated from a particular language/alphabet. For example, for the term “pratisamvits,” classification module  112  might assign confidence scores of “0.1” for English, “0.4” for Japanese, “0.9” for Sanskrit, “0.6” for Mandarin, etc. In some embodiments, classification module  110  may only determine that a word is transliterated and/or the language the word is transliterated from when the confidence score meets or exceeds a confidence score threshold, which may correspond to a numerical value. If all confidence scores are below the confidence score threshold, the classification module  112  may determine that the word is transliterated from a language and/or alphabet that it has not been trained for. 
     In some embodiments, classification module  112  use one or more feature/classification models  116 ( 1 )- 116 (N) when determining the confidence scores. For example, a confidence score may correspond to a probability of belonging to a language (L j ) determined using a feature/classification model  116 . In some embodiments, where classification module  112  uses a feature/classification model  116  that specifies vector representations of n-gram characteristics for a corresponding language, the confidence scores may be determined based on vector representations of the corresponding word. For example, classification module  112  may represent a word as a vectoral representation of its n-grams (e.g., a trigram representation of “elephant” would be “ele, lep, eph, pha, han, ant”), and then may use the vector representations of the n-gram characteristics for a language to determine one or more vector points for the word. Classification module  112  may then generate a confidence score that the word belongs to the corresponding language based on the one or more vector points. 
     The quality control module  114  can be executable by the one or more processing units  104  to determine a set of potential typos in an electronic document, and/or determine whether one or more of the potential typos is a transliterated word. After the quality control module  114  determines a set of potential typos in an electronic document, the quality control module  114  may then conduct a series of additional checks to identify false positives (i.e., words or other portions of text that are initially identified as typos, but are actually not typos) in the set of potential typos. For example, the quality control module  114  may perform a dictionary lookup to identify obscure words, may apply heuristics and/or hand-crafted rules to filter out proper nouns, quoted text, non-words (e.g., addresses), valid out-of-vocabulary words (e.g., compound words that may not be present in even the largest dictionary), terms for which spell-check is non-applicable (e.g., expressive interjections, onomatopoeia, etc.), or a combination thereof. In some embodiments, the series of additional checks may also cause the classification module  112  to evaluate the words in the set of potential typos to identify words that have been transliterated from another language and/or alphabet. The quality control module  114  may remove words that are identified as being transliterated words from the set of potential typos. 
     In some embodiments, after the additional is conducted, the quality control module  114  may provide the set of potential typos to an author of the electronic document, a publisher of the electronic document, a service that reviews and corrects the potential typos, or some other entity. 
     Those skilled in the art will appreciate that the architecture described in association with quality control service  102  is merely illustrative and is not intended to limit the scope of the present disclosure. In particular, the computing system and devices may include any combination of hardware or software that can perform the indicated functions, including computers, network devices, internet appliances, and/or other computing devices. The quality control service  102  may also be connected to other devices that are not illustrated, or instead may operate as a stand-alone system. In addition, the functionality provided by the illustrated components may in some implementations be combined in fewer components or distributed in additional components. Similarly, in some implementations, the functionality of some of the illustrated components may not be provided and/or other additional functionality may be available. 
     The one or more processing unit(s)  104  may be configured to execute instructions, applications, or programs stored in the memory  106 . In some examples, the one or more processing unit(s)  104  may include hardware processors that include, without limitation, a hardware central processing unit (CPU), a graphics processing unit (GPU), and so on. While in many instances the techniques are described herein as being performed by the one or more processing units  104 , in some instances the techniques may be implemented by one or more hardware logic components, such as a field programmable gate array (FPGA), a complex programmable logic device (CPLD), an application specific integrated circuit (ASIC), a system-on-chip (SoC), or a combination thereof. 
     The memory  106  is an example of computer-readable media. Computer-readable media may include two types of computer-readable media, namely computer storage media and communication media. Computer storage media may include volatile and non-volatile, removable, and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disk (DVD), or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that may be used to store the desired information and which may be accessed by a computing device. In general, computer storage media may include computer-executable instructions that, when executed by one or more processing units, cause various functions and/or operations described herein to be performed. 
     Additionally, communication media includes data stored within a modulated data signal. For example, a computer media may include computer readable instructions, data structures, program modules, modulated carrier waves, other modulated transmission mechanisms, etc. However, as defined herein, computer storage media does not include communication media. 
     Those skilled in the art will also appreciate that, while various items are illustrated as being stored in memory or storage while being used, these items or portions of them may be transferred between memory and other storage devices for purposes of memory management and data integrity. Alternatively, in other implementations, some or all of the software components may execute in memory on another device and communicate with the illustrated environment  200 . Some or all of the system components or data structures may also be stored (e.g., as instructions or structured data) on a non-transitory, computer-accessible medium or a portable article to be read by an appropriate drive, various examples of which are described above. In some implementations, instructions stored on a computer-accessible medium separate from the quality control service  102  may be transmitted to the quality control service  102  via transmission media or signals such as electrical, electromagnetic, or digital signals, conveyed via a communication medium such as a wireless link. Various implementations may further include receiving, sending or storing instructions and/or data implemented in accordance with the foregoing description upon a computer-accessible medium. 
     Additionally, the network interface  108  includes physical and/or logical interfaces for connecting the respective computing device(s) to another computing device or network. For example, the network interface  108  may enable WiFi-based communication such as via frequencies defined by the IEEE 802.11 standards, short range wireless frequencies such as Bluetooth®, or any suitable wired or wireless communications protocol that enables the respective computing device to interface with the other computing devices. 
     The architectures, systems, and individual elements described herein may include many other logical, programmatic, and physical components, of which those shown in the accompanying figures are merely examples that are related to the discussion herein. 
       FIG. 2  is a schematic diagram of an illustrative environment  200  for providing quality control services for identifying transliterated words in an electronic document. 
     The environment  200  may also include an author computing device  202  associated with author  204  that may provide one or more electronic documents  206  to one or more of a publisher  208  and/or the quality control service  102 . The author computing device  202  may include any type of device (e.g., a laptop computer, a tablet device, a mobile telephone, etc.), and may include one or more processor(s), computer-readable media, and a display. The electronic document(s)  206  may include any digital body of text, including eBooks, electronic magazines, blog posts, microblog entries, SMS messages, webpage texts, emails, as well as other examples of a digital body of script. 
     The publisher  208  may be any entity, server(s), platform, etc., that offers content (e.g., products, services, etc.) for acquisition to consumers. For example, the publisher  208  may be associated with an electronic or merchant marketplace (e.g., a website, electronic application, widget, etc.) that offers electronic documents  206  and/or physical versions of electronic documents  206  to search, browse, view and/or acquire (i.e., purchase, rent, lease, borrow, etc.). The publisher  208  may provide electronic document  206  to the quality control service  102  for quality review. For example, the publisher  208  may host a website for news media and the electronic document  208  may be a news article written by the author  204 . In some embodiments, the publisher  208  and the quality control service  102  may hosted by the same one or more servers, server farms, data centers, or other combination of one or more computing devices. After receiving the news article, but before publishing the news article to the website, the publisher  208  may provide the news article to the quality control service  102  to identify and/or correct typographical errors in the news article. 
     The quality control service  102  can be implemented or hosted by one or more servers, server farms, data centers, or any combination of one or more computing devices. The quality control service  102  may include the quality control module  114  that is configured to determine a set of potential typos in the electronic document  206  and/or determine whether one or more of the potential typos are a transliterated word. The quality control module  114  may compare the set of potential typos to a lexicon of known words, and/or apply one or more heuristics to identify and remove valid words/portions of texts that were incorrectly flagged as potential typos. 
     The quality control service  102  may also include the classification module  112  that is configured to determine, using the feature/classification model  116 , whether a word in the set of potential typos is a word that has been transliterated from another language and/or alphabet. Alternatively or in addition, the classification module  112  may use the feature/classification model  116  to determine a language and/or alphabet that the word has been transliterated from. For example, the classification module  112  may be able to determine that the word “Laba” (i.e., a Chinese term for a type of congee traditionally eaten during the celebration of the day Buddha attaining enlightenment) is a transliterated word, and/or that it has been transliterated from the Chinese language. The feature model/classification  116  may be an algorithmic model that probabilistically determines the native language and/or alphabet of a word based on the n-grams present within the word. Alternatively or in addition, the feature/classification model  116  may comprise vector representations of n-gram characteristics for a corresponding language. 
     In some embodiments, after the additional is conducted, the quality control service  102  remove transliterated words from the set of potential typos. The quality control service  102  may provide the set of potential typos to the author computing device  202 , the publisher  208 , or another service that reviews and corrects the potential typos, or some other entity.  FIG. 2  further illustrates each of the author computing device  202 , the publisher  208 , and the quality control service  102  being connected to a network  212 , such as a local area network or the internet. 
       FIGS. 3, 4, and 5  are flow diagrams of illustrative processes illustrated as a collection of blocks in a logical flow graph, which represent a sequence of operations that can be implemented in hardware, software, or a combination thereof. In the context of software, the blocks represent computer-executable instructions stored on one or more computer-readable storage media that, when executed by one or more processors, perform the recited operations. Generally, computer-executable instructions include routines, programs, objects, components, data structures, and the like that perform particular functions or implement particular abstract data types. The order in which the operations are described is not intended to be construed as a limitation, and any number of the described blocks can be combined in any order and/or in parallel to implement the processes. 
       FIG. 3  is a flow diagram of an illustrative process  300  for training a model for determining whether a word is a transliterated word from a different language. The process  300  may be implemented by the computing architecture  100  and in the environment  200  described above, or in other environments and architectures. 
     At  302 , the quality control service  102  accesses a training corpus associated with a language. In some embodiments, and as stated above, the training corpus may correspond to one or more electronic documents (i.e., one or more of dictionaries of the corresponding language, electronic documents in the corresponding language, text from webpage(s), etc.) that are written in a particular language. For example, a training corpus associated with Arabic may include electronic versions of Arabic novels and religious texts. 
     At  304 , the quality control service  102  determines features of the training corpus. In some embodiments, determining the features may include extracting raw data from the training corpus, and then selecting from the raw data a subset of the raw data to use as features for the language. For example, the quality control service  102  may identify the n-grams in words present within the electronic documents of the training corpus, and may select some or all of the identified n-grams to be used as features for the language. For example, where the electronic document is the Quran, the quality control service  102  may identify the trigrams of “_al,” “ala,” “lai,” “aik,” “iku,” “kum,” and “um_,” as being present within the Arabic word “alaikum.” In some embodiments, the quality control service  102  may store each distinct character n-gram of a word as a feature of that word. The quality control service  102  may also store associations between the n-grams of the words of the electronic documents. 
     At  306 , the quality control service  102  determines characteristics of the features. In some embodiments, the quality control service  102  may perform an analysis of the features of the training corpus to identify feature characteristics in relation to the language corresponding to the training corpus. For example, quality control service  102  may determine characteristics of the features that are correlated with the language corresponding to the training corpus, and/or how strongly these characteristics are correlated with the language corresponding to the training corpus. The characteristics of the features of the language may include a distribution of n-grams across the language, associations between n-grams in the language, feature patterns occurring in the language, etc. In some embodiments, when performing this analysis, the quality control service  102  may consider a previously selected set of n-grams, all possible n-grams, only the n-grams occurring in words of the electronic documents in the training corpus, only n-grams that appear in at least a threshold number of different words of the electronic documents in the training corpus, only n-grams that appear a threshold number of times the electronic documents of the training corpus, a preset number of the highest occurring n-grams in the electronic documents in the training corpus, or a combination thereof. 
     At  308 , the quality control service  102  generates a feature/classification model for the language. For example, the quality control service  102  may build a feature/classification model for a language corresponding to the training corpus based on the characteristics of the features. The feature/classification model may to determine, based on the n-grams within an input word, a confidence score that the input word is a transliterated word, and/or a confidence score that indicates likelihoods that the input word is a transliterated word of the language corresponding to the language corpus. Alternatively or in addition, the feature/classification models may include a vector embedding of the characteristics of the features. In some embodiments, the vector embedding may be used to translate the n-grams within an input word into one or more vector points. A random forest classifier can then be used to determine one or more confidence scores that indicate likelihoods that the input word is a transliterated word, and/or is a transliterated word of the language corresponding to the language corpus. 
     In some embodiments, the feature models may incorporate positive or negative correlations that individual n-grams and/or combinations of n-grams occur in a corresponding language. For example, some feature model may apply a negative weight for n-grams that do not exist in some languages. 
       FIG. 4  is a flow diagram of an illustrative process  400  for determining that a word is a transliterated word from a different language. The process  400  may be implemented by the computing architecture  100  and in the environment  200  described above, or in other environments and architectures. 
     At  402 , the quality control service  102  identifies a word for analysis. In some embodiments, the quality control service  102  may identify the word from a set of potential typos in an electronic document. The quality control service  102  may receive the set of potential typos, or may execute spellcheck functionality on one or more electronic documents to determine the set of potential typos. In some embodiments, before identifying a word for analysis, the quality control service  102  may additional the set of potential typos to identify false positives (i.e., words or other portions of text that are actually not typos). For example, the quality control service  102  may perform a dictionary lookup to identify obscure words, apply heuristics and/or hand-crafted rules to filter out proper nouns, quoted text, non-words (e.g., addresses), valid out-of-vocabulary words (e.g., compound words that may not be present in even the largest dictionary), terms for which spell-check is non-applicable (e.g., expressive interjections, onomatopoeia, etc.), or a combination thereof. 
     At  404 , the quality control service  102  determines one or more features of the word. For example, the quality control service  102  may break the word into its corresponding n-grams. For example, when determining features for the word “junmin” into the corresponding trigrams of “_ju,” “jum,” “unm,” “nmi,” “min,” and “in_.” In some embodiments, the quality control service  102  may include a space before the word and after the word when identifying n-grams to capture whether an n-gram begins or ends a word. The quality control service  102  may then treat each distinct character n-gram of a word as a feature of the word. Additional features determined by the quality control service  102  may include the location of a particular n-gram within a word, whether a particular n-gram starts or ends the word, n-grams that precede or follow a particular n-gram, etc. 
     At  406 , the quality control service  102  accesses one or more feature/classification models. Individual feature models may identify characteristics of a corresponding language. Characteristics identifies by a feature/classification model may include one or more of a distribution of n-grams across a corresponding language, associations between n-grams in the corresponding language, frequencies of n-gram patterns that occur in the corresponding language, etc. For example, the model may determine a frequency that the trigram “psh” is present in Sanskrit, and may determine a frequency that the trigram “psh” is associated with “sha” (e.g., how often are they present in the same word, at what frequency do they follow one another, etc.). In some embodiments, a feature model may incorporate positive or negative correlations between individual n-grams and/or combinations of n-grams and the corresponding language. For example, some feature/classification models may apply a negative weight to n-grams that do not exist in the language to which the feature model corresponds. 
     At  408 , the quality control service  102  determines a confidence score that the word is transliterated. In some embodiments, the quality control service  102  may assign a first confidence score that indicates a first likelihood that the word is a transliterated word, and a second confidence score that indicates a second likelihood that the word has been transliterated from a particular language. Alternatively or in addition, the quality control service  102  may determine multiple confidence scores, where individual confidence scores indicate a determined likelihood that the word has been transliterated from a particular language. For example, for the term “pratisamvits,” the quality control service  102  might assign confidence scores of “0.1” for English, “0.4” for Japanese, “0.9” for Sanskrit, “0.6” for Mandarin, etc. 
     The quality control service  102  may use the one or more feature models when determining the confidence scores. In some embodiments, the quality control service  102  determines that a word is transliterated and/or that the language the word is transliterated from a particular language when a corresponding confidence score meets or exceeds a confidence score threshold. If all confidence scores are below the confidence score threshold, the quality control service  102  may determine that the word is not a transliterated word, or that the word is a transliterated word from a language that there is no corresponding feature model. 
       FIG. 5  is an example illustration  500  of a process for determining that a word is a transliterated or transcribed word from a different language.  FIG. 5  depicts an electronic document  502 . In different embodiments, electronic document  502  may include any digital body of text, including eBooks, electronic magazines, blog posts, microblog entries, SMS messages, webpage texts, emails, as well as other examples of a digital body of script. For example,  FIG. 5  depicts electronic book  502  as an electronic eBook of The Jungle Book, by Rudyard Kipling.  FIG. 5  further depicts an unknown word  504  within the electronic document  502 . For example, the quality control service  102  may not recognize the word “Seeonee” as a valid word, and may flag the word as a potential typo. 
     The quality control service  102  may then perform one or more checks to determine whether the unknown word  504  flagged as a potential typo is a false positive (i.e., a valid word that has been erroneously flagged as a potential typo). For example, the quality control service  102  may perform a dictionary lookup to identify obscure words, apply heuristics and/or hand-crafted rules to filter out proper nouns, quoted text, non-words (e.g., addresses), valid out-of-vocabulary words (e.g., compound words that may not be present in even the largest dictionary), terms for which spell-check is non-applicable (e.g., expressive interjections, onomatopoeia, etc.), or a combination thereof. These checks may include determining whether the unknown word  504  is a transliterated word. When determining whether the word is a transliterated word, the quality control system  102  may break, truncate, or segment the word into its corresponding n-grams.  FIG. 5  depicts the component trigrams  506  of unknown word  504 . For example, the component trigrams  506  for “Seeonee” into the corresponding tri-grams of “see,” “eeo,” “eon,” “one,” and “nee.” The quality control service  102  may also identify the location of each particular n-gram within unknown word  504 , whether a particular n-gram starts or ends the word, n-grams that precede or follow a particular n-gram, etc. 
       FIG. 5  further depicts an algorithmic model  508  that probabilistically determines a probability that an unknown word  504  is a transliterated word from a language, such as the Sanskrit language. The algorithmic model  508  may determine the probability based on characteristics of a corresponding language. Characteristics of the language may include a frequency that individual n-grams a used in the language, a frequency that individual n-grams are associated with other n-grams (e.g., how often are they present in the same word, at what frequency do they follow one another, etc.). For example,  FIG. 5  shows an algorithm for determining a probability of the word “Seeonee” being Sanskrit. In  FIG. 5 , w(“EEO”) corresponds to a probabilistic weight determined by the Sanskrit language classifier model for the n-gram “EEO,” and f(X) represents a transformation function determined by the same language classifier model, a different language classifier model, or a combination thereof. Alternatively or in addition, in some embodiments the likelihood that the term “Seeonee” being Sanskrit may be determined using the algorithm:
 
 P (“SEEONEE”)= g (( W (“SEE”), W (“EEO”), W (“EON”), W (“ONE”), W (“NEE”))
 
     Where P(“SEEONEE”) corresponds to a likelihood of “Seeonee” being Sanskrit, W(“SEE”) represents a model-dependent weight associated with the Sanskrit language model for the n-gram “SEE,” and g(X 1 , X 2 , . . . , X m ) represents a transformation function of weights determined by a same language classifier model. In some embodiments, the algorithmic model  508  may incorporate positive or negative correlations between individual n-grams and/or combinations of n-grams and the corresponding language. For example, the algorithmic model  508  may apply a negative weight to n-grams that do not exist in the language corresponding to the feature model. 
       FIG. 5  further depicts a confidence score  510  that the unknown word  504  is transliterated from a different language, such as Sanskrit. The confidence score  510  may correspond to, or be based at least in part on, the likelihood of the unknown word  504  being of a particular language that is determined using one or more algorithmic models  508 . The quality control service  102  may determine the confidence score  510  based at least in part on the algorithmic probability. In some embodiments, confidence score  510  indicates a likelihood that the unknown word  504  is a transliterated word. Alternatively or in addition, the confidence score  510  may indicate a likelihood that the word has been transliterated from a particular language, such as Sanskrit. 
       FIG. 6  is a flow diagram of an illustrative process  400  for using more than one feature models to determine that a word is a transliterated word from a different language. The process  600  may be implemented by the computing architecture  100  and in the environment  200  described above, or in other environments and architectures. 
     At  602 , the quality control service  102  identifies a word for analysis. In some embodiments, the quality control service  102  may identify the word from a set of potential typos in an electronic document. The quality control service  102  may receive the set of potential typos, or may execute spellcheck functionality on one or more electronic documents to determine the set of potential typos. In some embodiments, before identifying a word for analysis, the quality control service  102  may additional the set of potential typos to identify false positives (i.e., words or other portions of text that are actually not typos). For example, the quality control service  102  may perform a dictionary lookup to identify obscure words, apply heuristics and/or hand-crafted rules to filter out proper nouns, quoted text, non-words (e.g., addresses), valid out-of-vocabulary words (e.g., compound words that may not be present in even the largest dictionary), terms for which spell-check is non-applicable (e.g., expressive interjections, onomatopoeia, etc.), or a combination thereof. 
     At  604 , the quality control service  102  determines one or more features of the word. For example, the quality control service  102  may break the word into its corresponding n-grams. For example, when determining features for the word “gongzhi” into the corresponding trigrams of“gon,” “ong,” “ngz,” “gzh,” and “zhi.” In some embodiments, the quality control service  102  may include a space before the word and after the word when identifying n-grams to capture whether an n-gram begins or ends a word. The quality control service  102  may then treat each distinct character n-gram of a word as a feature of the word. Additional features determined by the quality control service  102  may include the location of a particular n-gram within a word, whether a particular n-gram starts or ends the word, n-grams that precede or follow a particular n-gram, etc. 
     At  606 , the quality control service  102  accesses a feature model associated with a corresponding language. Individual feature models may identify characteristics of a corresponding language. Characteristics identified by a feature model may include one or more of a distribution of n-grams across a corresponding language, associations between n-grams in the corresponding language, frequencies of n-gram patterns that occur in the corresponding language, etc. For example, the feature model may determine a frequency that the trigram “ngz” is present in Chinese, and may determine a frequency that the trigram “ngz” is associated with “gzh” (e.g., how often are they present in the same word, at what frequency do they follow one another, etc.). In some embodiments, the feature model may incorporate positive or negative correlations between individual n-grams and/or combinations of n-grams and the corresponding language. For example, some feature models may apply a negative weight to n-grams that do not exist in the language corresponding to the feature model. 
     At  608 , the quality control service  102  determines a confidence score that the word is transliterated. The quality control service  102  may use the one or more feature models when determining the confidence scores. In some embodiments, the quality control service  102  may assign a first confidence score that indicates to a determined first likelihood that the word is a transliterated word, and a second confidence score that indicates a second likelihood that the word has been transliterated from a particular language. 
     At operation  610 , the quality control service  102  determines whether there is another feature model to apply to the word. If the answer at operation  610  is “yes” (it is determined that there is another feature model to apply to the word), then the process  600  moves to an operation  608  and the quality control service  102  accesses a new feature model associated with a new language. If the answer at operation  610  is “no” (it is determined that there are no other feature models to apply to the word), then the process  600  moves to an operation  612  where the quality control service  102  determines whether a confidence score exceeds a confidence score threshold. The confidence score threshold may be a preset value, or it may be determined dynamically by the quality control service  102 . In some embodiments, there may be multiple confidence score thresholds, where individual confidence score thresholds are associated with a corresponding feature model. 
     If the answer at operation  612  is “yes” (it is determined that there is a confidence score that exceeds the confidence score threshold), then the process  600  moves to an operation  614  and the quality control service  102  determines that the word is a transliterated or transcribed word. In some embodiments, where a confidence score exceeds the confidence score threshold, the quality control service  102  may determine that the word is a transliterated word from the language that corresponds to the feature model used to determine the confidence score. Where two or more confidence scores exceed the confidence score threshold, the quality control service  102  may determine the language that the word was most likely transliterated from based on a comparison between the two or more confidence scores. Alternatively, the quality control service  102  may determine that the word is a transliterated word from one of the two or more languages associated with the feature models used to determine the confidence scores. For example, the quality control service  102  may present each of the languages associated with the two or more confidence scores, along with a corresponding likelihood that the word has been transliterated from each of the languages. 
     If the answer at operation  612  is “no” (it is determined that no confidence scores exceed the confidence score threshold), then the process  600  moves to an operation  616  where the quality control service  102  determines that the word is not a transliterated or transcribed word. Alternatively, when no confidence scores exceed the confidence score threshold, the quality control service  102  may determine that the word is a transliterated word from a language and/or alphabet that it has not been trained for. 
     Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described. Rather, the specific features and acts are disclosed as illustrative forms of implementing the claims.