1/04/2013- 2013 Website Launched
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Syllabus
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Software
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Other Links
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- Evaluating a Semantic Network Automatically Constructed from Lexical
Co-occurrence on a Word Sense Disambiguation Task , by Sean Szumlanski and Fernando Gomez (To be presented on Tuesday 26th by Deli.)
- Identifying Relations for Open Information Extraction , by Fadar, et al.
- High precision rule based PPI extraction and per-pair basis performance evaluation , by J. Lee, et al.
- Building a WordNet for Arabic , by S. Elkateb, et al.
- Unsupervised Discovery of Domain-Specific Knowledge from Text , by Hovy et al.
- Automatic detection of political opinions in
Tweets, by Diana Maynard and Adam Funk
- Sentence-Level Sentiment Analysis via Sequence Modeling, by Xiaohua Liu and Ming Zhou
- Information Search and Retrieval in Microblogs, by Miles Efron
- Extracting Protein Interactions from Text with the Unified AkaneRE Event Extraction System, by Sætr et al.
- Learning Semantic Constraints for the Automatic Discovery of Part-Whole Relations (2003), by Roxana Girju , Adriana Badulescu , Dan Moldovan
- Extraction of
Regulatory Gene/Protein Networks from Medline, Jasmin Saric, et al.
- Learning Semantic
Constraints for the Automatic Discovery of Part-Whole Relations, Roxana Girju, Adriana Badulescu,
and Dan Moldovan.
- An
Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation, Roberto Navigli
and Mirella Lapata.
- SentiWordNet: A Publicly
Available Lexical Resource for Opinion Mining, Andrea Esuli and Fabrizio Sebastiani.
- SentiWordNet 3.0: An
Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining, Stefano Baccianella, Andrea
Esuli, and Fabrizio Sebastiani.
- Extended Gloss Overlaps as a Measure
of Semantic Relatedness, Satanjeev Banerjee and Ted Pedersen.
- Acquiring Knowledge from the Web
to be Used as Selectors for Noun Sense Disambiguation, Hansen A. Schwartz and Fernando Gomez.
- Extracting Ontological Selectional
Preferences for Non-Pertainym Adjectives from the Google Corpus, John J. Tanner and Fernando Gomez.
- Automatically Acquiring a
Semantic Network of Related Concepts, Sean Szumlanski and Fernando Gomez
- ConceptNet: A
Practical Commonsense Reasoning Toolkit, Hugo Liu and Push Singh.
- An
Architecture for a Generic Dialogue Shell, James Allen et al.
- An
Architecture for More Realistic Conversational Systems, James Allen, George
Ferguson, and Amanda Stent.
- Knowledge-Rich
Word Sense Disambiguation Rivaling Supervised Systems, Simone Paolo Ponzetto and Roberto
Navigli.
- Linking Genes to Literature: Text Mining, Information
Extraction, and Retrieval Applications for Biology, Martin Krallinger, Alfonso Valencia, and Lynette
Hirschman.
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Determining the Plausibility of Answers to Questions, Troy Smith,
Thomas M Repede, and Steven L Lytinen.
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Dialogue
management for conversational case-based
reasoning, Karl Branting, James Lester, and Bradford Mott
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Automated Text Summarization Tutorial - COLING/ACL '98, Eduard Hovy and Daniel Marcu.
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Natural Language Generation: an introduction and
open-ended review of the state of the art, John
Bateman.
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Thumbs up? Sentiment Classification using Machine Learning Techniques, Bo Pang, Lillian Lee,
and Shivakumar Vaithyanathan.
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Corpus-based and Knowledge-based Measures of Text Semantic Similarity,
Rada Mihalcea, Courtney Corley, and Carlo Strapparava.
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Unsupervised Learning of Verb Argument Structures, Pardo et al.
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Comparative Experiments on Sentiment Classification for Online Product Reviews, Cui Hang,
Vibhu Mittal, and Mayur Datar.
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Structural Semantic Interconnections: a Knowledge-Based Approach to Word Sense
Disambiguation Roberto Navigli and P. Velardi
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Trec QA 2007 Papers various authors.
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A Semantic Approach to IE Pattern Induction
Mark Stevenson and Mark A. Greenwood.
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Names and
Similarities on the Web: Fact Extraction in the Fast Lane, Pasca
et al.
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Fully Automatic Lexicon Expansion for Domain-oriented Sentiment Analysis , Hiroshi
Kanayama and Tetsuya Nasukawa.
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Espresso:
Leveraging Generic Patterns for Automatically Harvesting Semantic
Relations. , Patrick Pantel and Marco Pennacchiotti.
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SenseLearner:
Minimally Supervised Word Sense Disambiguation for All Words in Open
Text, Rada Mihalcea and Ehsanul Faruque.
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High Performance Question/Answering, Pasca and Harabagiu
- Deeper
sentiment analysis using machine translation technology, Hiroshi
Kanayama, Tetsuya Nasukawa, Hideo Watanabe.
- The
Proposition Bank: An Annotated Corpus of Semantic Roles,
Palmer, Kingsbury and Gildea.
- Unsupervised
Word Sense Disambiguation Rivaling Supervised Methods, David
Yarowsky.
- Expressing Implicit Semantic Relations without
Supervision, Peter Turney.
- Thumbs Up or Thumbs Down?
Semantic Orientation Applied to Unsupervised Classification of Reviews, Peter Turney.
- Measuring praise and criticism:
Inference of semantic orientation from association (2003), Turney and Littman
- Comparative Experiments on Sentiment Classification for
Online Product Reviews, Cui Hang, Vibhu Mittal, Mayur Datar (Google).
- Sentiment Analysis: Capturing Favorability Using Natural
Language Processing, Tetsuya Nasakawa, Jeonghee Yi (IBM Research).
- Noun Homograph Disambiguation Using Local Context in
Large Text Corpora, Marti A. Hearst
- What's New in Statistical Machine
Translation, Kevin Knight and Philipp Koehn
- Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in
Natural Language, Philip Resnik
- The Role of Semantic Roles in Verb Sense
Disambiguation, Dang and Palmer
- VerbOcean: Mining the Web for Fine-Grained
Semantic Verb Relations, Chklovski and Pantel
- Automatic Acquisition of Hyponyms from Large Text
Corpora, Marti A. Hearst
- The Necessity of Parsing for Predicate Argument
Recognition, Gildea and Palmer
- Learning for Semantic Interpretation:
Scaling Up Without Dumbing Down, Raymond J. Mooney
- Papers by Eric Brill
- Automatic Labeling of Semantic Roles,
Gildea and Jurafsky
- CYC, WordNet, and EDR: critiques and
responses, Lenat, Miller, and Yokoi
- Part-of-Speech Tagging
Guidlines for the Penn Treebank Project, B. Santorini
- The St. Thomas Common Sense Symposium: Designing
Architectures for Human-Level Intelligence, Minsky, Singh, and Sloman
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