Unsupervised Word Sense Disambiguation REU, Summer, 2009.

Slides:



Advertisements
Similar presentations
Using Link Grammar and WordNet on Fact Extraction for the Travel Domain.
Advertisements

DISTRIBUTIONAL WORD SIMILARITY David Kauchak CS159 Fall 2014.
Semantic News Recommendation Using WordNet and Bing Similarities 28th Symposium On Applied Computing 2013 (SAC 2013) March 21, 2013 Michel Capelle
The Google Similarity Distance  We’ve been talking about Natural Language parsing  Understanding the meaning in a sentence requires knowing relationships.
1 Extended Gloss Overlaps as a Measure of Semantic Relatedness Satanjeev Banerjee Ted Pedersen Carnegie Mellon University University of Minnesota Duluth.
A Linguistic Approach for Semantic Web Service Discovery International Symposium on Management Intelligent Systems 2012 (IS-MiS 2012) July 13, 2012 Jordy.
Lexical Semantics and Word Senses Hongning Wang
D ETERMINING THE S ENTIMENT OF O PINIONS Presentation by Md Mustafizur Rahman (mr4xb) 1.
Data Quality Class 10. Agenda Review of Last week Cleansing Applications Guest Speaker.
USC Graduate Student DayColumbia, SCMarch 2006 Presented by: Jingshan Huang Computer Science & Engineering Department University of South Carolina PhD.
Lexical chains for summarization a summary of Silber & McCoy’s work by Keith Trnka.
Content Based Image Clustering and Image Retrieval Using Multiple Instance Learning Using Multiple Instance Learning Xin Chen Advisor: Chengcui Zhang Department.
CS Word Sense Disambiguation. 2 Overview A problem for semantic attachment approaches: what happens when a given lexeme has multiple ‘meanings’?
Collective Word Sense Disambiguation David Vickrey Ben Taskar Daphne Koller.
A UTOMATED D ISCOVERY OF T ELIC R ELATIONS FOR W ORDNET M ARCO D E B ONI S URESH M ANANDHAR.
Gimme’ The Context: Context- driven Automatic Semantic Annotation with CPANKOW Philipp Cimiano et al.
Creating a Bilingual Ontology: A Corpus-Based Approach for Aligning WordNet and HowNet Marine Carpuat Grace Ngai Pascale Fung Kenneth W.Church.
Learning syntactic patterns for automatic hypernym discovery Rion Snow, Daniel Jurafsky and Andrew Y. Ng Prepared by Ang Sun
Semantic Video Classification Based on Subtitles and Domain Terminologies Polyxeni Katsiouli, Vassileios Tsetsos, Stathes Hadjiefthymiades P ervasive C.
A Framework for Named Entity Recognition in the Open Domain Richard Evans Research Group in Computational Linguistics University of Wolverhampton UK
NATURAL LANGUAGE TOOLKIT(NLTK) April Corbet. Overview 1. What is NLTK? 2. NLTK Basic Functionalities 3. Part of Speech Tagging 4. Chunking and Trees 5.
Feature Selection for Automatic Taxonomy Induction The Features Input: Two terms Output: A numeric score, or. Lexical-Syntactic Patterns Co-occurrence.
Word Sense Disambiguation for Automatic Taxonomy Construction from Text-Based Web Corpora 12th International Conference on Web Information System Engineering.
Semantic Matching Pavel Shvaiko Stanford University, October 31, 2003 Paper with Fausto Giunchiglia Research group (alphabetically ordered): Fausto Giunchiglia,
Evaluating the Contribution of EuroWordNet and Word Sense Disambiguation to Cross-Language Information Retrieval Paul Clough 1 and Mark Stevenson 2 Department.
A Fully Unsupervised Word Sense Disambiguation Method Using Dependency Knowledge Ping Chen University of Houston-Downtown Wei Ding University of Massachusetts-Boston.
Jiuling Zhang  Why perform query expansion?  WordNet based Word Sense Disambiguation WordNet Word Sense Disambiguation  Conceptual Query.
Word Sense Disambiguation (WSD)
Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 A semantic approach for question classification using.
Annotating Words using WordNet Semantic Glosses Julian Szymański Department of Computer Systems Architecture, Faculty of Electronics, Telecommunications.
WORD SENSE DISAMBIGUATION STUDY ON WORD NET ONTOLOGY Akilan Velmurugan Computer Networks – CS 790G.
Word Sense Disambiguation in Queries Shaung Liu, Clement Yu, Weiyi Meng.
Minor Thesis A scalable schema matching framework for relational databases Student: Ahmed Saimon Adam ID: Award: MSc (Computer & Information.
SYMPOSIUM ON SEMANTICS IN SYSTEMS FOR TEXT PROCESSING September 22-24, Venice, Italy Combining Knowledge-based Methods and Supervised Learning for.
21/11/2002 The Integration of Lexical Knowledge and External Resources for QA Hui YANG, Tat-Seng Chua Pris, School of Computing.
Efficiently Computed Lexical Chains As an Intermediate Representation for Automatic Text Summarization H.G. Silber and K.F. McCoy University of Delaware.
Page 1 SenDiS Sectoral Operational Programme "Increase of Economic Competitiveness" "Investments for your future" Project co-financed by the European Regional.
Markov Logic and Deep Networks Pedro Domingos Dept. of Computer Science & Eng. University of Washington.
1 Learning Sub-structures of Document Semantic Graphs for Document Summarization 1 Jure Leskovec, 1 Marko Grobelnik, 2 Natasa Milic-Frayling 1 Jozef Stefan.
HyperLex: lexical cartography for information retrieval Jean Veronis Presented by: Siddhanth Jain( ) Samiulla Shaikh( )
Semantics-Based News Recommendation with SF-IDF+ International Conference on Web Intelligence, Mining, and Semantics (WIMS 2013) June 13, 2013 Marnix Moerland.
Element Level Semantic Matching Pavel Shvaiko Meaning Coordination and Negotiation Workshop, ISWC 8 th November 2004, Hiroshima, Japan Paper by Fausto.
Using Semantic Relatedness for Word Sense Disambiguation
CS460/IT632 Natural Language Processing/Language Technology for the Web Lecture 24 (14/04/06) Prof. Pushpak Bhattacharyya IIT Bombay Word Sense Disambiguation.
Crawling the Hidden Web Authors: Sriram Raghavan, Hector Garcia-Molina VLDB 2001 Speaker: Karthik Shekar 1.
An Applied Ontological Approach to Computational Semantics Sam Zhang.
1 Gloss-based Semantic Similarity Metrics for Predominant Sense Acquisition Ryu Iida Nara Institute of Science and Technology Diana McCarthy and Rob Koeling.
1 Fine-grained and Coarse-grained Word Sense Disambiguation Jinying Chen, Hoa Trang Dang, Martha Palmer August 22, 2003.
From Words to Senses: A Case Study of Subjectivity Recognition Author: Fangzhong Su & Katja Markert (University of Leeds, UK) Source: COLING 2008 Reporter:
Houses of Mirrors: Deeply Adaptive Designs for Machine Cognition Deborah Duong, Michael Ross.
Learning Event Durations from Event Descriptions Feng Pan, Rutu Mulkar, Jerry R. Hobbs University of Southern California ACL ’ 06.
Overview of Statistical NLP IR Group Meeting March 7, 2006.
Word Sense and Subjectivity (Coling/ACL 2006) Janyce Wiebe Rada Mihalcea University of Pittsburgh University of North Texas Acknowledgements: This slide.
Using Lexical Knowledge to Evaluate the Novelty of Rules Mined from Text Sugato Basu, Raymond J. Mooney, Krupakar V. Pasupuleti, Joydeep Ghosh Presented.
WordNet::Similarity Measuring the Relatedness of Concepts Yue Wang Department of Computer Science.
GRE SENTENCE EQUIVALENCE STRATEGY. SENTENCE EQUIVALENCE STRATEGY Read the Sentence & look for Clues Predict an Answer Select the two Choices that most.
Lexical Semantics and Word Senses Hongning Wang
Detecting and Exploiting Figurative Language in WordNet Wim Peters Department of Computer Science University of Sheffield.
COMP423: Intelligent Agent Text Representation. Menu – Bag of words – Phrase – Semantics Semantic distance between two words.
Word Sense Disambiguation Algorithms in Hindi
How to Find the Answer.
Automatic Writing Evaluation
Coarse-grained Word Sense Disambiguation
Semantic Processing with Context Analysis
Web News Sentence Searching Using Linguistic Graph Similarity
Element Level Semantic Matching
Information Organization: Clustering
WordNet WordNet, WSD.
A method for WSD on Unrestricted Text
Dynamic Word Sense Disambiguation with Semantic Similarity
Presentation transcript:

Unsupervised Word Sense Disambiguation REU, Summer, 2009

Word Sense Disambiguation E.g., “The soldiers drove the tank.” armored combat vehicle large vessel for holding gases or liquids

hire Context Knowledge Base company programmer many computer “Many companies hire computer programmers” write programmer software “Computer programmers write software” + computer

Context Knowledge Base hire company programmer many computer write software Result of merging dependency trees Weights are number of dependency relation instances found

WSD Algorithm Parse original sentence using Minipar, get weighted dependency tree. hire company programmer software computer “A large software company hires computer programmers.” To-be-disambiguated word large 11 Weights are distances from to-be-disambiguated word

Parse each gloss of to-be-disambiguated word, get weighted dependency trees. WSD Algorithm Gloss 1: an institution created to conduct business create institution business unit smallmilitary Gloss 2: a small military unit conduct

For each word in a gloss tree, find that word’s dependent words in the context knowledge base. We are looking for words in the knowledge base that match words in the original sentence. In other words, we are looking for context clues to disambiguate a word. A score is generated based on the weights of those dependency relations in the knowledge base, and the dependent words of the to-be-disambiguated word in the original sentence. The more matches we find, the higher the generated score will be. The gloss with the highest generated score will be selected as the correct sense of the word. WSD Algorithm

Synonym Matching If no direct matches are found between a gloss word and dependency relations in context knowledge base, we can replace the gloss word with one of its synonyms, since synonyms are semantically equivalent words.

Hypernym/hyponym Matching E.g., animal mammal dog poodle Extract hypernyms and hyponyms of words from WordNet database. Store these in a data structure. Strategies:use all “levels” use only levels close to the original word apply the above strategies to synonym matching, as well

Word Similarity Use WordNet::Similarity Perl module to calculate “similarity score” between gloss word and dependent words in knowledge base. The most similar word found will be considered the closest to an actual match. doganimal dogdesk WordNet::Similarity similarity scores