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Co reference resolution
This document discusses coreference resolution and Hobbs's algorithm for resolving pronouns and references. It defines anaphoric and cataphoric references, and describes how Hobbs's algorithm works by traversing the surface parse tree to find matching noun phrases. The document also discusses adapting Hobbs's algorithm for Hindi, which presents challenges due to Hindi's lack of grammatical gender in pronouns. Applications of coreference resolution include machine translation and machine learning.
Co reference resolution
- 1. Coreference Resolution USING HOBBS‟S ALGORITHM HemantKumar (10579) Babul Bhanu (10545) Pratheek Adidela (10583) Shekhar Kumar (09562)
- 2. Contents What is coreferenceresolution Anaphoric and Cataphoric references Direct and indirect anaphora Hobbs‟s algorithm Hobb‟s algorithm for Hindi Problems in implementation Applications
- 3. What is CoreferenceResolution In computational linguistics, Coreference resolution is a well-studied problem in discourse. To derive the correct interpretation of text, or even to estimate the relative importance of various mentioned subjects, pronouns and other referring expressions must be connected to the right individuals. There are two types of references: When the reader must look back to the previous context, Coreference is called anaphoric reference. When the reader must look forward, it is cataphoric reference.
- 4. Anaphoric and Cataphoric references Cataphora Inlinguistics, cataphora is used to first insert an expression or word that co-refers with a later expression in the discourse. An example of strict, sentence-internal cataphora in English is the following sentence “When he arrived home, John went to sleep.” Anaphora In linguistics, anaphora is the use of an expression the interpretation of which depends upon another expression in context (its antecedent or postcedent).In the sentence “Sally arrived, but nobody saw her” the pronoun „her‟ is anaphoric, referring back to Sally.
- 5. Hobbs‟s algorithm Hobbs‟s algorithmis mainly focused on “surface parse tree”. By this is means that the tree that exhibits the grammatical structure of the sentences (its division into subject, verb, objects, adverbials, etc.) without permuting or omitting any of the words in a sentence. The naïve algorithm traverses the surface parse tree in a particular order looking for a noun phrase of the correct gender and number.
- 7. Hobbs‟s algorithm forHindi Pronouns in Hindi exhibit a great deal of ambiguity. Pronouns in the first, second and third person do not convey any information about the gender Redefined the grammar for Hindi
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- 21. Challenges in implementation Partsof speech tagger Richness of word database In Hindi the gender of noun is identified by using verbs
- 22. Applications English – Hindimachine translation Machine learning
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