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Anaphora Resolution
The document discusses anaphoric pronoun resolution which involves finding links between pronouns and their antecedents to enrich text by adding the resolved pronoun references. It describes the preprocessing, model representation using anaphora pairs and candidate selection, feature representation using distance and overlap features, and machine learning models used including CRF and logistic regression. Further work ideas include improving gender and animacy detection features and using a multi-pass approach.
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Anaphora Resolution
- 1. ANAPHORA RESOLUTION FINDWISE
- 2. Anaphoric Pronoun Resolution Findinglinks • Pronoun to antecedent Enriching text • Input: preprocessed document • Output: All found anaphoric pronoun references to words/phrases
- 3. Areas of use Documentsummarization • Improving sentence comparisons Ontology enrichment • Enriching results • Populating with more data. Entity level sentiment analysis Question answering • Adding more information to indata. • Extracting more RDF- tripples
- 4. Preprocessing Required Additional • Sentence splitting • Dependency parsing • Tokenization • Part of Speech-tagging • Named Entity Reconition • Gender Detection
- 5. Model representation Anaphora pairs Candidate selection/ranking • Pronoun • Find pronoun • Antecedent • Pair with antecedent candidates - Entities • Filter out improbable pairs (rules) - Nouns, cardinals, foreign words • Rank candidate pairs • Select the most probable candidate (if any)
- 6. Feature representation Distance Features Overlap Features/Filters • Sentence distance • Gender • Hobbs distance • Animacity Antecedent Features • Number • PoS-tag • Entity • Gender Pronoun Features • Animacity • Word string • Number • Gender • Entity tag • Animacity • ... • ...
- 7. Machine learning models Running the models Models • Condidtional Random Fields (CRF) • Control confiedence threshold - Mallet - Precision/Recall trade • Logistic Regression off - Liblinear Training the models • OntoNotes Conll 2012 • English • 1667 documents • Various domains
- 8. Further Work/Ideas forImprovement Full coreference/anaphora resolution Improved Features • Change model representations • Improved gender detection - Clusters - Chains • Improved animacity detection • Generalize comparisons (not only • Additional overlap features Multi pass approach pronoun - antecedent) Non referential/cataphora detection • First pass(es) rule based • Training separate models • Harder classifications with machine learning models • Rule based
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