Anaphora resolution is a procedure of replacing pronouns with its referring nouns which may be available in the same sentence or in different sentences within the same document. Even multiple approaches are available for anaphora resolution; there may be some space for semantic approaches which provides solution better than syntactic or corpus based approaches. In syntactic approaches, all noun phrases from the previous or current sentences are checked for getting constrains agreement with the anaphor and score value is calculated for a pair of Noun-Pronoun based on these constrains. The pair getting the highest positive score is treated as the result. In some cases the sentence structure is not changed, hence the score corresponding to the feature values and syntactic structures are same. Here the actual replacement of pronoun with noun depends on the meaning of the sentence especially the meaning of the verb. To resolve such situations, here we are proposing a method which is a combination of syntactic and semantic approaches based on ThemeSets or thematic sets. Using ThemeSets we are exploiting the role of verbal lexemes associated with the noun or pronoun for the resolution of anaphora. Anaphora resolution in semantic way has great importance in the modern era of artificial intelligence which enhance multi-dimensional research in the area of natural language processing in a better way.
As the availability of data increase in the form of web pages, there arises a challenge of effective processing of data in a timely manner. Millions of documents were added day by day in World Wide Web, and for a manual processing it will take thousands of years and hence the term automatic data extraction comes to the context. On these gigantic volumes, textual data holds a major portion and effective text processing algorithms are needed for the processing and acquisition of data. Even though a number of techniques are available for sentence extraction no one can perform well as that of a human expert. But semantic approaches can perform better than other existing approaches, since they are considering the meanings rather that its form. Here the similarity calculations are more accurate than other approaches and the level of accuracy depends on the semantic tool they have used and the efficiency of the logic used for extracting the meaning, calculating similarity etc. In this proposal we are presenting a combinational approach of statistical and semantic procedures for sentence extraction which mainly differs from the previous approaches in the use of the semantic tool ThemeSets.
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