Coreference Resolution USING HOBBS’S ALGORITHM

Hemant Kumar (10579)Babul Bhanu (10545)

Pratheek Adidela (10583)

Shekhar Kumar (09562)

Contents

What is coreference resolution

Anaphoric and Cataphoric references

Direct and indirect anaphora

Hobbs’s algorithm

Hobb’s algorithm for Hindi

Problems in implementation

Applications

What is Coreference Resolution

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.

Anaphoric and Cataphoric references

Cataphora

In linguistics, 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.

Hobbs’s algorithm

Hobbs’s algorithm is 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.

Hobbs’s algorithm for Hindi

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

S1

NP_erg(SUB) VP2

Nbar

NounMr.Smith

Postpne

NP2_acc(DO) VP1

Nbar Postpko

NounDriver

PP_loc VP

Verbdekha

NP1(PO) postpmein

Pronoun_possuske

Nbar

Nountruck

EXAMPLE 1Non-Reflexive pronoun

S1

NP_erg(SUB) VP2

Nbar

NounMr.Smith

Postpne

NP2_acc(DO) VP1

Nbar Postpko

NounDriver

PP_loc VP

Verbdekha

NP1(PO) postpmein

Pronoun_possuske

Nbar

Nountruck

S1

NP_erg(SUB) VP2

Nbar

NounMr.Smith

Postpne

NP2_acc(DO) VP1

Nbar Postpko

NounDriver

PP_loc VP

Verbdekha

NP1(PO) postpmein

Pronoun_possuske

Nbar

Nountruck

S1

NP_erg(SUB) VP2

Nbar

NounMr.Smith

Postpne

NP2_acc(DO) VP1

Nbar Postpko

NounDriver

PP_loc VP

Verbdekha

NP1(PO) postpmein

Pronoun_possuske

Nbar

Nountruck

S1

NP_erg(SUB) VP2

Nbar

NounMr.Smith

Postpne

NP2_acc(DO) VP1

Nbar Postpko

NounDriver

PP_loc VP

Verbdekha

NP1(PO) postpmein

Pronoun_possuske

Nbar

Nountruck

S1

NP_erg(SUB) VP2

Nbar

NounMr.Smith

Postpne

NP2_acc(DO) VP1

Nbar Postpko

NounDriver

PP_loc VP

Verbdekha

NP1(PO) postpmein

Pronoun_possuske

Nbar

Nountruck

S1

NP_erg(SUB) VP2

Nbar

NounMr.Smith

Postpne

NP2_acc(DO) VP1

Nbar Postpko

NounDriver

PP_loc VP

Verbdekha

NP1(PO) postpmein

Pronoun_possuske

Nbar

Nountruck

Previous sentence

S1

NP_erg(SUB) VP2

Nbar

NounMr.Smith

Postpne

NP2_acc(DO) VP1

Nbar Postpko

NounDriver

PP_loc VP

Verbdekha

NP1(PO) postpmein

Pronoun_possapne

Nbar

Nountruck

EXAMPLE 2Reflexive pronoun

S1

NP_erg(SUB) VP2

Nbar

NounMr.Smith

Postpne

NP2_acc(DO) VP1

Nbar Postpko

NounDriver

PP_loc VP

Verbdekha

NP1(PO) postpmein

Pronoun_possapne

Nbar

Nountruck

S1

NP_erg(SUB) VP2

Nbar

NounMr.Smith

Postpne

NP2_acc(DO) VP1

Nbar Postpko

NounDriver

PP_loc VP

Verbdekha

NP1(PO) postpmein

Pronoun_possapne

Nbar

Nountruck

S1

NP_erg(SUB) VP2

Nbar

NounMr.Smith

Postpne

NP2_acc(DO) VP1

Nbar Postpko

NounDriver

PP_loc VP

Verbdekha

NP1(PO) postpmein

Pronoun_possapne

Nbar

Nountruck

S1

NP_erg(SUB) VP2

Nbar

NounMr.Smith

Postpne

NP2_acc(DO) VP1

Nbar Postpko

NounDriver

PP_loc VP

Verbdekha

NP1(PO) postpmein

Pronoun_possapne

Nbar

Nountruck

S1

NP_erg(SUB) VP2

Nbar

NounMr.Smith

Postpne

NP2_acc(DO) VP1

Nbar Postpko

NounDriver

PP_loc VP

Verbdekha

NP1(PO) postpmein

Pronoun_possapne

Nbar

Nountruck

Challenges in implementation

Parts of speech tagger

Richness of word database

In Hindi the gender of noun is identified by using verbs

Applications

English – Hindi machine translation

Machine learning

THANK YOU