REAH: Resolution Engine for Anaphora in Hindi dialogue

Natural language processing needs cognitive study of human brain, mind, and intelligence to come up with new intricacies and theories and convert them to build computational strategies and machine learning based applications. One such phenomenon of natural language processing is anaphora resolution (AR) that assures the presence of pro-forms or pronouns in the context and calls an antecedently expression that recover the same meaning and can be an noun phrase (NP) or non-NP. Therefore AR is the task of resolving a pronoun that has been introduced after its referent and depict same implication. A reader or speaker rejects the formal linguistic concept to understand the utterance and link the pro-forms appeared in dialects to the actual referent as per the cognition. The productivity and performance of many natural languages processing applications such as tutoring system, essay summarization system, question answering system, and machine translation had been affected by the unresolved hidden facts in pro-forms, which must be sort out. AR is a nontrivial task as the human brain is smart enough to comprehend different writing and speaking styles, references, coreferences, idiomatic terms, etc., but computer lacks real-world knowledge, and hence analyzing anaphora and its referent is problematic. The machine learning techniques had made possible to understand and handle such exigent task like AR. The paper presents a knowledge-poor machine learning technique that employs heuristics procedures on shallow morphological features for finding equivalence class for Hindi anaphora. The authors believe that it is a novel procedure as no dictionaries and named entity recognizer have been implemented, and investigations have been carried out solely utilizing shallow parsing where there is no information available about the animated entity. The paper describes the probabilistic method that integrates the filtration rules for mention detection and the algorithms for identifying actual antecedents of intrasentential and intersentential anaphora. The filtration rules check the potency of mentions for being candidate for antecedent and remove them if irrelevant. The resolution algorithms for pronominal and nonpronominal anaphors in Hindi texts gives approximate solution as based on heuristics. The antecedents for mentions may be multiple, distributive or phrase in nature and could span across the utterance in the discourse. Hence, prior to resolution such mentions are remarked and categorized. The paper discusses the ambiguities in Hindi spoken dialogues and the role of case markers (CMs) to resolve anaphors. The limited attributes of noun and pronouns are strongly considered as constraint features that have been incorporated in the algorithms, which are CMs, number, and distance between the anaphor and the candidate NP. The approach had defined its own module to categorize inanimate NP without using any semantic knowledge. Further the authors have discussed the contribution of these features in overall result. The final evaluation of the system has been conducted using standard metrics and also comparison of the results with other resolution approaches using the same datasets has been made. Since the approach is not domain based, it is feasible to easily apply to new domains that contain dialogues. The effectiveness of the results has been promoted by generating the mapping table for each utterance that satisfactorily depicts human cognition.