Abstract Anaphora Research Articles

Resolving abstract anaphors in Spanish discourse: Underspecification and mereological structures

Abstract Anaphoric underspecification involves multiple potential candidate antecedents for an anaphoric expression. In abstract object anaphora, where linguistic antecedents are clauses, sentences and larger fragments of discourse, the source of referential underspecification is commonly found at the propositional level. Thus, underspecified abstract anaphors have multiple antecedents of a higher-order nature (i.e., propositions and events). Following previous research on anaphoric underspecification with nominal antecedents, I propose a hypothetical three-step process toward the resolution of underspecified abstract object anaphors by hearers in discourse: (i) creation of a complex abstract object with a mereological structure that includes all potential interpretations for an anaphor, (ii) recognition of the thematic connection among propositions intended by the speaker in the form of a specific rhetorical relation, and 3) resolution of the abstract anaphor. Potential antecedents for any underspecified abstract anaphor may include atomic propositions and complex abstract referents that result from a merged interpretation of several propositions that are thematically connected. Provided that it is available, I claim that such a merged interpretation, which is part of the mereological structure, is the preferred interpretation as it is generally interpreted as part of a general purpose by the speaker, in addition to contributing to the thematic coherence of discourse.

Statistical pair pruning towards target class in learning-based anaphora resolution for Tamil

Anaphora resolution is an important task to be achieved in many natural language understanding (NLU) applications including machine translation. This paper proposes learning-based system to resolve pronouns in Tamil text built around various classification algorithms. To improve learning accuracy, the system is built in two folds. First is feature vector production where mentions are identified, characterised then a feature vectors of lexical, syntactic and semantic features are produced. Next is the pair pruning module where, number of non-target class pairs is reduced by deep statistical analysis of feature vector. Incorporating deeper pair pruning module dramatically increases the f-measure score when compared to training the same models but without the pruning module. On the tourism dataset of TDIL we trained the system with various classification algorithms and obtained encouraging results for a challenging language, Tamil. We discuss how varying the ratio of f-measure, precision and recall is between with and without the pruning module in comparative model.

Improving Anaphora Resolution by Resolving Gender and Number Agreement in Hindi Language using Rule based Approach

Anaphora resolution has been an active research area in the field of natural language processing. There are various methods to resolve the pronouns that refer to nouns but this paper presents the rule based approach to resolve the anaphora. The rules have been derived by the execution of the instruction from a set of data which consist of a specific type of knowledge of verbs in Hindi language. The Hindi language is resource poor language which has no specific structure of writing. We have proposed a model that performs pronominal anaphora resolution task based on gender and number agreement. Anaphora resolution is very complex and challenging task to handle so we have analyzed the similarities and variations between their agreement and pronouns in Hindi language. This paper exhibits the experiments that are governed on different data sets in Hindi Language along with its result based on the F-score and future directions.

A Machine Learning Approach to Anaphora Resolution in Arabic

Anaphora resolution is a commonly studied research area of Natural Language Processing (NLP). It is crucial for many application areas of Natural Language Processing including information extraction, question answering and text summarization. Most of the earlier work done in the field of anaphora resolution is for English and other European languages. Arabic language is not sufficiently studied with respect to anaphora resolution and rarely being subjected to machine learning experiments. In this paper we present a machine learning approach to resolve the pronominal anaphora in Arabic language. In this work we determine the appropriate features to be used in this task. We consider a number of classifier namely naive Bayes, K-nearest neighbors and linear logistic regression are employed as base-classifiers for each of the feature sets. In this paper, an in-depth study has been conducted on different of feature sets for exploiting effective features and investigating their effect on performance of the Anaphora resolution. Finally, a wide range of comparative experiments on Quranic datasets are conducted, The experimental results on the Arabic Quran training corpus demonstrate that the proposed method is feasible for the pronominal anaphora resolution of Arabic.

Hybrid Model of Automated Anaphora Resolution

Anaphora resolution has proven to be a very difficult problem of natural language processing, and it is useful in discourse analysis, language understanding and processing, information exaction, machine translation and many more. This paper represents a system that instead of using a monolithic architecture for resolving anaphora, use the hybrid model which combines the constraint-based and preferences-based architectures, each uses a different source of knowledge, and proves effective on theoretical and computational basis. An algorithm identifies both inter-sentential and intra-sentential antecedents of “Third person pronoun anaphors”, “Pleonastic it”, and “Lexical noun phrase anaphora”. The algorithm use Charniak parser (parser05Aug16) as an associated tool, and it relays on the output generated by it. Salience measures derived from parse tree, in order to find out accurate antecedents from the list of potential antecedents. We have tested the system extensively on 'Reuters Newspaper corpus'.

A Modular Architecture for Anaphora Resolution

Anaphora resolution attempts to determine the correct antecedent of an anaphor (the term pointing back). In what follows, we propose an algorithm for the resolution of anaphoric pronouns that relies on lexical and syntactic knowledge incorporated in a modular approach based on constraints and preferences. Our objective was to find the correct antecedent to the following subject pronouns (il, ils, elle, elles), object pronouns (l’, le, la, les) and possessive pronouns (son, sa, ses, leur, leurs) in unrestricted texts. We also identify and eliminate pleonastic pronouns and discard candidates appearing in appositions. Moreover we use a focus mechanism to determine salient entities. The algorithm, implemented in Prolog, realizes a success rate of 68%, which was considered a good performance for unrestricted French texts.

Semantic Inference for Anaphora Resolution: Toward a Framework in Machine Translation

Anaphora is a discourse-level linguistic phenomenon. There is consensus that anaphora resolution should rely on prior sentences within the context of the discourse. We propose to cast anaphora resolution as a semantic inference process in which a combination of multiple strategies, each exploiting different aspects of linguistic knowledge, is employed to provide a coherent resolution of anaphora. A framework which encompasses several salient linguistic parameters such as grammatical role, proximity, repetition, sentence recency and semantic cues is demonstrated. This work also shows how an anaphora-resolution algorithm can be embedded within a framework which captures all the above salient parameters, as well as remedies some of the inadequacies found in any monolithic resolution system. A language-neutral semantic representation characterized by semantic cues is presented in order to capture the distilled information after resolution. The effectiveness of the language-neutral representation, both for machine translation and anaphora resolution, is demonstrated through a set of simulations and evaluations.

Anaphora and Translation Discrepancies in Russian–German MT

Anaphora resolution in machine translation involves two aspects: (1) the identification of the antecedent, i.e., the determination of co-reference relations between anaphor and antecedent; and (2) the translation of the anaphor, i.e., the selection of the appropriate target-language equivalent. The identification of the antecedent is essentially a monolingual, language-pair independent problem which is usually solved during analysis. The selection of the target-language equivalent, on the other hand, can be regarded as a language-pair dependent task which has to be tackled during transfer and generation. In this paper, the problems of anaphora translation are discussed for the language pair Russian–German. Although in most cases source-language anaphoric pronouns correspond to target-language anaphoric pronouns, in some cases this straightforward equation does not hold. Two cases of such translation discrepancies are treated here: zero anaphora and pronominal PPs. The differences in the distribution of zero anaphora and pronominal PPs in Russian and German are described, and solutions to these translation problems based on the Russian–German MT system T1 are presented.