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Resolving Pronouns for a Resource-Poor , Using Resource-Rich Language, Tamil

Sobha Devi AU-KBC Research Centre, University, [email protected]

Abstract features. If the resources available in one language (henceforth referred to as source) can be used to In this paper we give in detail how a re- facilitate the resolution, such as anaphora, for all source rich language can be used for resolv- the related to the language in question ing pronouns for a less resource language. (target), the problem of unavailability of resources The source language, which is resource rich would be alleviated. language in this study, is Tamil and the re- There exists a recent research paradigm, in which source poor language is Malayalam, both the researchers work on algorithms that can rap- belonging to the same , idly develop machine translation and other tools Dravidian. The Pronominal resolution de- for an obscure language. This work falls into this veloped for Tamil uses CRFs. Our approach paradigm, under the assumption that the language is to leverage the model to test Malayalam data and the processing re- in question has a less obscure sibling. Moreover, quired for Malayalam data is detailed. The the problem is intellectually interesting. While similarity at the syntactic level between the there has been significant research in using re- languages is exploited in identifying the sources from another language to build, for exam- features for developing the Tamil language ple, parsers, there have been very little work on model. The word form or the lexical item is utilizing the close relationship between the lan- not considered as a feature for training the guages to produce high quality tools such as CRFs. Evaluation on Malayalam anaphora resolution (Nakov, P and Tou Ng,H data shows that our approach is correct and 2012). In our work we are interested in the follow- the results, though not as good as Tamil, but ing questions: comparable. If two languages are from the same language fam- 1 Introduction ily and have similarity in syntactic structures and not in lexical form and script Natural language processing techniques of the 1. Can the language model developed for present day require large amounts of manually- one language be used for analyzing the annotated data to work. In reality, the required other language? quantity of data is available only for a few lan- 2. How the lexical form difference can be guages of major interest. In this work we show resolved in using the language model? how a resource-rich language, Tamil, can be lev- 3. How to overcome the challenges of script eraged to resolve anaphora for a related resource- variation? poor language, Malayalam. Both Tamil and Mal- In this work we develop a language model for re- ayalam belong to the same language family, Dra- solving pronominals in Tamil using CRFs and us- vidian. The methods we focus on exploits the sim- ing the language model test another language, ilarity at the syntactic level of the languages and Malayalam. As said earlier, in this work, we are anaphora resolution heavily depends on syntactic

611 Proceedings of Recent Advances in Natural Language Processing, pages 611–618, Varna, Bulgaria, Sep 2–4, 2019. https://doi.org/10.26615/978-954-452-056-4_072

focusing on Dravidian family of languages. His- retained the Proto –Dravidian verbs. For example, torically, all have branched the word for “talk” in Malayalam is “samsarik- out from a common root Proto-Dravidian. kuka” “to talk” which has root in , Among the Dravidian languages, Tamil is the old- whereas the Tamil equivalent is “pesuu” “to talk”, est language. Though there is similarity at the syn- the root in Pro-Dravidian. tactic level, there is no similarity in lexical form The Syntactic Structure Level: There is lot of sim- or at the script level among the languages. We are ilarity at the syntactic structure level between the motivated by the observation that related lan- two languages. Since antecedent to anaphor has guages tend to have similar word order and syn- dependency on the position of the , the struc- tax, but they do not have similar script or orthog- tural similarity is a positive feature for our goal. raphy. Hence words are not similar. The syntactic similarity at Sentence level, Case maker level, pronominal distribution level are ex- Tamil has the most resources at all levels of lin- plained with examples. guistics, right from morphological analyser to dis- Case marker level: Both the languages have the course parser and Malayalam has the least. About same number of cases and their distribution is the similarity of the two languages we give in de- similar. In both the languages, inflected tail in section 2. with nominative or become the subject of the sentence (Dative subject is the peculiarity The remainder of this is organized as fol- of Indian languages). denotes the lows: Section 2 provides a detailed description of object. the two languages, their linguistic similarity cred- Clausal sentences: The clause constructions in its and differences, Section 3 presents the pro- both the languages follow the same rule. The nominal resolution in Tamil. In Section 4 we in- clauses are formed by nonfinite verbs. The clauses troduce our proposed approach on how the lan- do not have free word order and they have fixed guage model for Tamil can be used for resolving positions. Order of embedding of the subordinate Malayalam pronouns. Section 5 describes the da- clause is same in both the languages. tasets used for evaluation, experiments and analy- Ex:1 sis of the results and the paper ends with the con- (Ma) [innale vanna(vbp) kutti ]/Sub-RP-cl clusion in Section 6. {sita annu}/Main cl

2 How similar the languages Tamil and (Ta) [neRu vanta(vbp) pon ]/Sub- RP-cl Malayalam Are? {sita aakum}/Maincl

As mentioned earlier, both the languages belong [Yesterday came girl ]/subcl {Sita is}/ Maincl to the Dravidian family and are relatively free word order languages, inflectional (rich in mor- (The girl who came yesterday is Sita) phology), agglutinative and verb final. They have Nominative and Dative subjects. The pronouns As can be seen from the above example, the basic have inherent gender marking as in English and syntactic structure is the same in both the lan- have the same lexical form “avan” “he”, “aval” guages. The above example is a two clause sen- “she” and “atu” “it” both in Tamil and Malaya- tence with a relative participial clause and a main lam. Though the pronouns have same lexical form clause. The relative participial clause is formed by and meaning, it can be said that there is no lexical the nonfinite verb (vbp). Using the same example similarity between the two languages. The simi- we can find the pronominal distribution. larity between two languages can be at three lev- Ex: 2 els, a) writing script, b) the word forms and c) the (Ma) [innale vanna(vbp) avali (PRP) syntactic structure. ]/Sub-RP-cl {sita annu}/Main cl i (Ta) [neRu vanta(vbp) avali (PRP) Script Level: The two languages have different ]/Sub-RP-cl {sita aakum}/Maincl writing form, though the base is from Grandha i [Yesterday came shei (PRP) script. Hence no similarity at the script level. ]/subcl { Sita is} / Maincl The Word Level: There is no similarity at the lex- i (The she who came yesterday is Sita) ical level between the two languages. The San- skritization of Malayalam contributed to have In the above example the pronoun “aval” “she” more Sanskrit verbs in Malayalam whereas Tamil occurs at the same position in both the languages

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and the antecedent “sita” also occurs at the same Students(N) school(N)+dat go(V)+present+3pl position as shown by co-indexing. Consider an- (Students are going to the school) other example. 4b. avarkaLi veekamaaka natakkinranar. They(PN) fast(ADV) walk(V)+present+3pl Ex:3 (They are walking fast.) (Ma). sithaai kadaikku pooyi. avali pazham Considering Ex 4a and Ex.4b, sentence Ex.4b has Vaangicchu(Vpast) 3rd person plural pronoun ‘avarkaL’ as the subject (Ta). sithaai kadaikku cenRaal. avali pazham and it refers to plural noun ‘maaNavarkaL’ which Vaangkinaal(V,past,+F,+Sg). is the subject in Ex.4a. Sita shop went. She fruit bought Ex:5 (Sitai went to the shop. Shei bought fruit.) 5a. raamuvumi giitavumj nanparkaL. In the above example there are two sentences and Raamu(N)+INC Gita(N)+INC friends(N) pronoun is in one sentence and antecedent is in (Ramu and Gita are friends.) another. Here you can see the distribution of the pronoun “aval” and where the antecedent “sita” is 5b. avani ettaam vakuppil padikkiraan. occurring. Though Tamil has number, gender and He(PN) eight(N) class(N) study(V)+present+3sm (He studies in eight standard.) person agreement between subject and verb and Malayalam does not have, this cannot be consid- 5c. avaLumj ettaam vakuppil padikkiaal. ered as a grammatical feature which can be used She(PN) eight(N) class(N) study(V)+present+3sf for identifying the antecedent of an anaphor. This (She also studies in eight standard.) grammatical variation does not have an impact on In Ex.5b, 3rd person masculine pronoun ‘avan’ oc- the identification of pronoun and antecedent rela- curs as the subject and it refers to the masculine tions. From the above examples we can see that noun ‘raamu’, subject noun in Ex.5a. Similarly, the two languages have the same syntactic struc- 3rd person feminine pronoun ‘avaL’ in Ex.5c re- ture at the clause and sentence level. We are ex- fers to feminine noun ‘giita’ in Ex.3a. ‘atu’, ploiting this similarity between the two languages which is a 3rd person neuter pronoun, will also oc- to achieve our goal. We find that using this simi- curs as genitive/possessive case marker. Consider larity between the languages, the language model the following example. of Tamil can be used to resolve pronouns in Mal- 3.1.1 Non-anaphoric Pronouns ayalam. The pronouns can also occur as generic mentions without having referent. In English it known 3 Pronoun Resolution in Tamil as‘pleonastic it’. 3.1 Pronouns in Tamil Ex:6. atu oru malaikalam. In this section, we analyse in detail the pronomi- It(PN) one(Qc) rainy_season (N) nal expressions in Tamil. Pronouns are the words (It was a rainy season.) used as a substitution to nouns, that are already mentioned or that is already known. There are In Ex.6, the 3rd person neuter pronoun ‘atu’ (it) do pronouns which do not refer. Pronouns in Tamil not have a referent. Here ‘atu’ is equivalent to ple- st nd rd have person (1 , 2 , 3 person) and number (sin- onastic ‘it’ in English gular, plural) distinction. Masculine, feminine and neuter gender distinctions are clearly marked in rd st nd 3.1.2 Corpus annotation 3 person pronouns, whereas in 1 and 2 person pronouns there is no distinction of masculine, We collected 600 News articles from various feminine and neuter gender. In this work we con- online Tamil News wires. The News articles are sider only third person pronouns. Third person from Sports, Disaster and News domains. pronouns in Tamil have inherent gender and as in The anaphoric expressions are annotated along English and they are “avan” he, “aval” she and rd with its antecedents using graphical tool, “atu” it. In this work, we resolve 3 person pro- PAlinkA, a highly customisable tool for Dis- nouns. The distribution of pronouns in various course Annotation (Orasan, 2003) which we cus- syntactic constructions is explained with exam- tomized for Tamil. We have used two tags ples below. namely, MARKABLE and COREF. The corpus Ex:4 used for training is 54,563 words which are anno- 4a. maaNavarkaLi paLLikku celkiranar. tated for anaphora –antecedent pairs and testing

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corpus is 10,912 words. We have calculated the approaches using Centering theory for . inter-annotator agreement which is the degree of Sobha et al., (2007) presented a salience factor agreement among annotators. We have used Co- based with limited shallow parsing of text. Aki- hen’s kappa as the agreement statistics. The kappa landeswari et al., (2013) used CRFs for resolution coefficient is generally regarded as the statistics of third person pronoun. et al., (2013) used of choice for measuring agreement on ratings Tree CRFs for anaphora resolution for Tamil with made on a nominal scale. We got a Kappa score features from dependency parsed text. In most of of 0.87. The difference between the annotators the published works resolution of third person were analysed and found the variation in annota- pronoun was considered and it is a non-trivial tion. It occurred in the marking of antecedents for task. pronominal. This is common in sentences with Pronoun resolution engine does the task of identi- clausal inversion, and genitive drop. fying the antecedents of the pronouns. The Pro- noun resolution is built using Conditional Ran- 3.2 Pronoun Resolution System dom Fields (CRFs) technique. Though CRFs is Early works in anaphora resolution by Hobbs notable for sequence labelling task, we used this (1978), Carbonell and Brown (1988), Rich and technique to classify the correct anaphor-anteced- Luper Foy (1988) etc. were mentioned as ent pair from the possible candidate NP pairs by knowledge intensive approach, where syntactic, presenting the features of the NP pair and by semantic information, world knowledge and case avoiding the transition probability. While training frames were used. Centering theory, a discourse we form positive pairs by pairing anaphoric pro- based approach for anaphora resolution was pre- noun and correct antecedent NP and negative sented by Grosz (1977), Joshi and Kuhn (1979). pairs by pairing anaphoric pronouns and other Salience feature based approaches were presented NPs which match in person, number and gender by Lappin and Leass (1994), Kennedy Boguraev (PNG) information with the anaphoric pronoun. (1996) and Sobha et al., (2000). Indicator based, These positive and negative pairs are fed to the knowledge poor method for anaphora resolution CRFs engine and the language model is gener- methods were presented by Mitkov (1997, 1998). ated. While testing, when an anaphoric pronoun One of the early works using machine learning occurs in the sentence, the noun phrases which technique was Dagan Itai’s (1990) unsupervised match in PNG with the pronoun, that occur in the approach based on co-occurrence words. With the preceding portion of the sentence and the four pre- use of machine learning techniques researchers ceding sentences are collected and paired with the work on anaphora resolution and noun phrase anaphoric pronoun and presented to CRFs engine anaphora resolution simultaneously. The other to identify the correct anaphor-antecedent pair. machine learning approaches for anaphora resolu- 3.2.1 Pre-processing tion were the following. Aone and Bennett (1995), The input document is processed with a sentence McCarty and Lahnert (1995), Soon et al., (2001), splitter and tokeniser to split the document into Ng and Cardia (2002) had used decision tree sentences and the sentences into individual tokens based classifier. Anaphora resolution using CRFs which include words, punctuation markers and was presented by McCallum and Wellner (2003) symbols. The sentence split and tokenized docu- for English, Li et al., (2008) for Chinese and ments are processed with Syntactic Processing Sobha et al., (2011, 2013) for English and Tamil. modules. Syntactic processing modules include In Indian languages anaphora resolution engines Morphological analyser, Part-of-Speech tagger are demonstrated only in few languages such as and Chunker. These modules are developed in Hindi, Bengali, Tamil, and Malayalam. Most of house. the Indian languages do not have parser and other a) Morphological Analyser: Morphological sophisticated pre-processing tools. The earliest analysis processes the word into component work in Indian language, ‘Vasisth’ was a rule and assigning the correct based multilingual anaphora resolution platform morpho-syntactic information. The Tamil by Sobha and Patnaik (1998, 2000, 2002), where morphological Analyser is developed using the morphological richness of Malayalam and paradigm based approach and implemented Hindi were exploited without using full-parser. using Finite State Automata (FSA) ( The case marker information is used for identify- Sundar et.al 2010). The words are classified ing subject, object, direct and in-direct object. according to their formation and are Prasad and Strube (2000), Uppalapu et al., (2009) marked as paradigms. The number of and Dekwale et al., (2013) had presented different paradigms used in this system is Noun

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paradigms: 32; Verb Paradigms: 37; The features for all possible candidate antecedent Paradigms: 4; Paradigms: and pronoun pairs are obtained by pre-processing 1. A root word with 1, 52,590 root the input sentences with morphological analyser, words is used for developing the POS tagger, and chunker. The features identified morphological anlyser. The morphological can be classified as positional and syntactic fea- analyser is tested with 12,923 words and the tures system processed 12,596 words out of which Positional Features: The occurrence of the can- it correctly tagged 12,305. The Precision is didate antecedent is noted in the same sentence 97.69% and Recall = 97.46%. MA returns all where the pronoun occurs or in the prior sentences possible parse for a given word. or in prior four sentences from the current sen- tence. b) Tagger (POS tagger): Part Syntactic Features: The syntactic arguments of of Speech tagger disambiguates the multiple the candidate noun phrases in the sentence are a parse given by the morphological analyser, key feature. The arguments of the noun phrases using the context in which a word occurs. The such as subject, object, indirect object, are ob- Part of speech tagger is developed using the tained from the case suffix affixed with the noun machine learning technique Conditional phrase. As mentioned in section 2 the subject of a Random fields (CRF++) (Sobha L, et.al sentence can be identified by the case marker it 2010). The features used for machine learning takes. We use morphological marking for the is a set of linguistic suffix features along with above. statistical and uses a window of 3 a) PoS tag and chunk tag of Candidate NP, case words. We have used 4, 50,000 words, which marker tags of the noun. are tagged using BIS POS tags. The system b) The suffixes which show the gender which performs with recall 100% and Average gets attached to the verb. Precision of 95.16%. c) Noun and Verb Phrase Chunker: Chunking 3.3.2 Development of Tamil Language Model is the task of grouping grammatically related words into chunks such as noun phrase, verb We used 600 Tamil Newspaper articles for build- phrase, adjectival phrase etc. The system is ing the language model. The preparation of the developed using the machine learning training data is described below. The raw corpus technique, Conditional Random is processed with sentence splitter and tokeniser. fields(CRF++) (Sobha L et.al 2010). The fea- The tokenized corpus is then preprocessed with tures used are the POS tag, Word and window shallow processing modules, namely, morpholog- of 5 words. Training Corpus is 74,000 words. ical analyser, part-of-speech tagger and chunker. The recall is 100%. Average Precision of The training data is prepared from this processed 92.00%. corpus. For each pronoun, the Noun phrase (NP) preceding the pronouns and in the NPs in preced- 3.3 Pronoun Resolution Engine ing sentence till correct antecedent NP, which match in Person, number and Gender (PNG) are In both training and testing phase, the noun selected for training. The above features are used phrases (NP) which match with the PNG of the for CRF for learning. The system was evaluated pronoun are considered. The features are ex- with data from the web and the result is given be- tracted from these NPs. In the training phase the low. positive and negative pairs are marked and fed to Domain Testing Precision Recall the ML engine for generating a language model. Corpus (%) (%) In the testing phase these NPs with its features are (Words) input to the language model to identify the ante- News 10,912 86.2 66.67 cedent of a pronoun. Here we have not taken the data lexical item or the word as a feature. We have used only the grammatical tags as feature. The Table 1: Pronominal Resolution (CRFs engine) features selected represent the syntactic position of the anaphor –antecedent occurrence. 4. Resolution of Pronouns in Malayalam Cor- 3.3.1 Features Selection pus using Tamil Language Model The features required for machine learning are identified from shallow parsed input sentences.

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In this section, we present in detail how Malaya- 1. [determiner] [quantifier][intensifier] [classi- lam is tested using Tamil language model. Here fier][adjective] {Head Noun}. Here Head Malayalam data is pre-processed as per the re- noun is obligatory and others are all optional quirement of the Tamil language model test data. 2. NN+NN combination The test data required four grammatical infor- mation, i) POS, ii) the case marker, iii) the number 3. NN with no suffix+NN with no suffix…… gender and person and iv)chunk information. In +NN with suffix or without suffix. the introduction we have asked three questions on how to use a language model in source language Using the above rules we identified the noun be used for testing a target language. The three chunks in Malayalam. The above discussed pre- questions are dealt one by one below. processing gives an accuracy of 66% for POS and 1. Can the language model developed for suffix tagging and 63% for chunking. one language be used for analyzing the 2. How the lexical form difference can be other language? resolved in using the language model? In this study we have used the language model of The second question we asked is about the lexical the source language Tamil. The features used to form or words which are not similar in both the develop this language model are POS tag, Chunk languages and how this can be resolved. The anal- tag and the case/ suffix tags. The word form was ysis of Tamil has shown that the syntactic struc- not considered as a feature. Since these are the ture of the language has more prominence over the features used for learning, the test data also should words in the resolution of anaphors. Hence we have these information. As said earlier, Malaya- have taken the syntactic features and did not take lam is not a resource rich language and it does not word as a feature. The system learned only the have pre-processing engines such as POS tagger, structure patterns. Chunker and morphological analysers with high 3. How to overcome the challenges of script accuracy. Hence we developed a very rudimen- variation? tary preprocessing systems which can give the Since word feature is not considered the script do POS tag, case/suffix tags and chunk tags. not pose any challenges. Still to have the same The POS information: The POS and suffix infor- script we converted the two languages into one mation are assigned to the corpus using a root form the WX notation. This helped in having the word dictionary with part of speech information same representation of the languages. and a suffix dictionary. The dictionary has the root words which include 4. Testing with Malayalam Data all types of pronouns and contains nearly 66,000 We selected 300 articles from Malayalam Wik- root words. The grammatical information (POS) ipedia, which were on different genre and size. such as noun, verb, adjective, pronoun and num- The 300 Malayalam documents from Wikipedia ber gender person (PNG) information are given has 7600 sentences with 3660 3rd person pro- for a word in the dictionary. nouns. The distribution of the pronouns is pre- The suffix dictionary contains all possible suf- sented in Table 2. fixes which a root word can take. The suffix in- Number of cludes the changes in when added to the Pronoun Occurrences with root word. The suffixes are of two types, i) that its Inflected forms avan (3rd person masculine which gets attached to nouns called the case suf- 1120 fixes and 2) that which gets attached to verbs singular) aval (3rd person feminine called the TAM (Tense, Aspect, and Modal). Us- 840 ing this suffix dictionary we can identify the POS singular) avar (3rd person ) 420 of the word even if the word is not present in the athu (3rd person nuetor 1280 root word dictionary. The suffix dictionary has singular) 1,00,000 unique entries. Total 3660 The noun chunk information is given by a rule Table 2. Distribution of 3rd person pronouns in the based chunker which works on three linguistic corpus rules. The noun phrases alone are required for As discussed in the earlier section, the prepro- anaphora resolution. Chunks are identified using cessing done for Tamil using syntactic module are the basic linguistic rule for Noun phrases as given morphological information, POS and Chunking below information. Hence the same pre-processing in- formation is necessary for Malayalam data as

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well. The documents are initially preprocessed Precisi Recal Type of pronoun with a sentence splitter and tokenizer. The to- on (%) l (%) kenized documents are pre-processed using the avan (3rd person masculine 70.83 69.52 pos, suffix and chunking systems discussed above singular) to enrich the text with syntactic information. avaL (3rd person feminine 69.34 68.56 For each pronoun, we identify the possible candi- singular) avar/avarkal (3rd person date antecedents. Those noun phrases that occur 65.45 70.34 preceding the pronoun in the current sentences plural/honorofic) and preceding four sentences, which match in the atu (3rd person nuetor singular) 56.67 65.67 person, number gender (PNG) with the select pro- Total 68.45 67.34 noun are identified as possible candidates. For Table 3: Evaluation Results these possible candidates we extract the features 6. Conclusion required for CRFs techniques as explained in the In this paper we explained a method to use high previous section. After extraction of features for resource language to resolve anaphors in less re- selected candidate antecedents, the antecedent is source language. In this experiment the high re- identified by using language model built using source language is Tamil and the less resource Tamil data. The results are encouraging with 67% language is Malayalam. The results are encourag- accuracy which is a respectable score. The errors ing. The model needs to be tested with more data and evaluation is given in detail in the next sec- as future work. tion. References 5. Experiment and Discussion Carbonell J. G., and Brown R. D. 1988. Anaphora res-

olution: A multi- strategy approach. In: 12th The experiment showed that the resolution of the International Conference on Computational Lin- pronouns “avan” he and “aval” she is similar to guistics, 1988, pp. 96-101. that of Tamil documents. The issues related to split antecedent is not addressed and hence pro- Dagan I., and Itai. A. 1990. Automatic processing of nouns which are referring to coordinated nouns large corpora for the resolution of anaphora refer- were not resolved. The pronoun which is less re- ences. In: 13th conference on Computational lin- guistics, Vol. 3, Helsinki, Finland, pp.330-332. solved is the third person neuter pronouns “atu” compared to other pronouns. The third person Dakwale. P., Mujadia. V., Sharma. D.M. 2013. A Hy- neuter pronoun usually has more number of pos- brid Approach for Anaphora Resolution in Hindi. sible candidates, which leads to poor resolution. In: Proc of International Joint Conference on Natu- Consider the following example. ral Language Processing, Nagoya, Japan, pp.977– Ex:7 981. avan joli ceytha jolikkarkku Li., F., Shi., S., Chen., Y., and Lv, X. 2008. Chinese He(PRP) work(N) do(V)+past+RP worker(N)+pl+dat Pronominal Anaphora Resolution Based on Condi- vellam kotuthu. tional Random Fields. In: International water(N) give(V)+past Conference on Computer Science and Software En- (He gave water to the workers who did the work.) gineering, Washington, DC, USA, pp. 731-734. atu nallatu aayirunnu Hobbs J. 1978. Resolving pronoun references. Lingua It(PRP) good(N) is ( V) +past. 44, pp. 339-352. (It was good.) Grosz, B. J. 1977. The representation and use of focus in dialogue understanding. Technical Report 151, In this example, the pronoun 'atu' refers to SRI International, 333 Ravenswood Ave, Menlo 'vellam” ‘water’; in the previous sentence. In the Park, Ca. 94025. previous sentence there are two possible candi- rd Joshi A. K., and Kuhn S. 1979. Centered logic: The date antecedents 'vellam' and 'joli 'which are 3 role of entity centered sentence representation in person nouns. The ML engine chooses 'joli', natural language inferencing. In: International Joint which is in the initial position of the sentence and Conference on Artificial Intelligence. in the subject position. When the antecedent is in Kennedy, C., Boguraev, B. 1996 Anaphora for Every- the object position the engine has not identified it one: Pronominal Anaphora Resolution without a properly. The following table gives the results of pronouns.

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