1 Determining the Hierarchical Structure of Perspective and Speech Expressions Eric Breck and Claire Cardie Cornell University Department of Computer Science.

Slides:

Advertisements

Similar presentations

Exploring the Effectiveness of Lexical Ontologies for Modeling Temporal Relations with Markov Logic Eun Y. Ha, Alok Baikadi, Carlyle Licata, Bradford Mott,

Advertisements

Specialized models and ranking for coreference resolution Pascal Denis ALPAGE Project Team INRIA Rocquencourt F Le Chesnay, France Jason Baldridge.

Progress update Lin Ziheng. System overview 2 Components – Connective classifier Features from Pitler and Nenkova (2009): – Connective: because – Self.

Playing the Telephone Game: Determining the Hierarchical Structure of Perspective and Speech Expressions Eric Breck and Claire Cardie Department of Computer.

Jan Wiebe University of Pittsburgh Claire Cardie Cornell University Ellen Riloff University of Utah Opinions in Question Answering.

A Joint Model For Semantic Role Labeling Aria Haghighi, Kristina Toutanova, Christopher D. Manning Computer Science Department Stanford University.

Annotating Topics of Opinions Veselin Stoyanov Claire Cardie.

Recognizing Implicit Discourse Relations in the Penn Discourse Treebank Ziheng Lin, Min-Yen Kan, and Hwee Tou Ng Department of Computer Science National.

Shallow Parsing CS 4705 Julia Hirschberg 1. Shallow or Partial Parsing Sometimes we don’t need a complete parse tree –Information extraction –Question.

LEDIR : An Unsupervised Algorithm for Learning Directionality of Inference Rules Advisor: Hsin-His Chen Reporter: Chi-Hsin Yu Date: From EMNLP.

Robust Textual Inference via Graph Matching Aria Haghighi Andrew Ng Christopher Manning.

Multi-Perspective Question Answering Using the OpQA Corpus Veselin Stoyanov Claire Cardie Janyce Wiebe Cornell University University of Pittsburgh.

Semantic Role Chunking Combining Complementary Syntactic Views Sameer Pradhan, Kadri Hacioglu, Wayne Ward, James H. Martin, Daniel Jurafsky  Center for.

Sunita Sarawagi.  Enables richer forms of queries  Facilitates source integration and queries spanning sources “Information Extraction refers to the.

Predicting Text Quality for Scientific Articles Annie Louis University of Pennsylvania Advisor: Ani Nenkova.

Predicting Text Quality for Scientific Articles AAAI/SIGART-11 Doctoral Consortium Annie Louis : Louis A. and Nenkova A Automatically.

1 Attributions and Private States Jan Wiebe (U. Pittsburgh) Theresa Wilson (U. Pittsburgh) Claire Cardie (Cornell U.)

Learning Subjective Adjectives from Corpora Janyce M. Wiebe Presenter: Gabriel Nicolae.

Some definitions Morphemes = smallest unit of meaning in a language Phrase = set of one or more words that go together (from grammar) (e.g., subject clause,

Dept. of Computer Science & Engg. Indian Institute of Technology Kharagpur Part-of-Speech Tagging and Chunking with Maximum Entropy Model Sandipan Dandapat.

Extracting Interest Tags from Twitter User Biographies Ying Ding, Jing Jiang School of Information Systems Singapore Management University AIRS 2014, Kuching,

Text Mining: Finding Nuggets in Mountains of Textual Data Jochen Dijrre, Peter Gerstl, Roland Seiffert Presented by Huimin Ye.

Learning Table Extraction from Examples Ashwin Tengli, Yiming Yang and Nian Li Ma School of Computer Science Carnegie Mellon University Coling 04.

Extracting Opinions, Opinion Holders, and Topics Expressed in Online News Media Text Soo-Min Kim and Eduard Hovy USC Information Sciences Institute 4676.

Mining and Summarizing Customer Reviews

AQUAINT Kickoff Meeting – December 2001 Integrating Robust Semantics, Event Detection, Information Fusion, and Summarization for Multimedia Question Answering.

Digital Camera and Computer Vision Laboratory Department of Computer Science and Information Engineering National Taiwan University, Taipei, Taiwan, R.O.C.

“DIALOGUE” REVISIONS RULES FOR ADDING DIALOGUE. WHY DIALOGUE? Dialogue is what keeps the story interesting and moving quickly for the reader. Think about.

Andreea Bodnari, 1 Peter Szolovits, 1 Ozlem Uzuner 2 1 MIT, CSAIL, Cambridge, MA, USA 2 Department of Information Studies, University at Albany SUNY, Albany,

Automatic Extraction of Opinion Propositions and their Holders Steven Bethard, Hong Yu, Ashley Thornton, Vasileios Hatzivassiloglou and Dan Jurafsky Department.

Empirical Methods in Information Extraction Claire Cardie Appeared in AI Magazine, 18:4, Summarized by Seong-Bae Park.

Probabilistic Parsing Reading: Chap 14, Jurafsky & Martin This slide set was adapted from J. Martin, U. Colorado Instructor: Paul Tarau, based on Rada.

Some Thoughts on HPC in Natural Language Engineering Steven Bird University of Melbourne & University of Pennsylvania.

Digital Camera and Computer Vision Laboratory Department of Computer Science and Information Engineering National Taiwan University, Taipei, Taiwan, R.O.C.

A Compositional Context Sensitive Multi-document Summarizer: Exploring the Factors That Influence Summarization Ani Nenkova, Stanford University Lucy Vanderwende,

2007. Software Engineering Laboratory, School of Computer Science S E Towards Answering Opinion Questions: Separating Facts from Opinions and Identifying.

The CoNLL-2013 Shared Task on Grammatical Error Correction Hwee Tou Ng, Yuanbin Wu, and Christian Hadiwinoto 1 Siew.

Scott Duvall, Brett South, Stéphane Meystre A Hands-on Introduction to Natural Language Processing in Healthcare Annotation as a Central Task for Development.

Automatic Detection of Tags for Political Blogs Khairun-nisa Hassanali Vasileios Hatzivassiloglou The University.

Day 3. Standards Reading: 1.0 Word Analysis, Fluency, and Systematic Vocabulary Development- Students apply their knowledge of word origins to determine.

This work is supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior National Business Center contract number.

A Language Independent Method for Question Classification COLING 2004.

Experiments of Opinion Analysis On MPQA and NTCIR-6 Yaoyong Li, Kalina Bontcheva, Hamish Cunningham Department of Computer Science University of Sheffield.

AQUAINT Workshop – June 2003 Improved Semantic Role Parsing Kadri Hacioglu, Sameer Pradhan, Valerie Krugler, Steven Bethard, Ashley Thornton, Wayne Ward,

A Cascaded Finite-State Parser for German Michael Schiehlen Institut für Maschinelle Sprachverarbeitung Universität Stuttgart

Opinion Holders in Opinion Text from Online Newspapers Youngho Kim, Yuchul Jung and Sung-Hyon Myaeng Reporter: Chia-Ying Lee Advisor: Prof. Hsin-Hsi Chen.

1 Multi-Perspective Question Answering Using the OpQA Corpus (HLT/EMNLP 2005) Veselin Stoyanov Claire Cardie Janyce Wiebe Cornell University University.

1 Toward Opinion Summarization: Linking the Sources Veselin Stoyanov and Claire Cardie Department of Computer Science Cornell University Ithaca, NY 14850,

1/21 Automatic Discovery of Intentions in Text and its Application to Question Answering (ACL 2005 Student Research Workshop )

Digital Camera and Computer Vision Laboratory Department of Computer Science and Information Engineering National Taiwan University, Taipei, Taiwan, R.O.C.

Multilingual Opinion Holder Identification Using Author and Authority Viewpoints Yohei Seki, Noriko Kando,Masaki Aono Toyohashi University of Technology.

Using a Named Entity Tagger to Generalise Surface Matching Text Patterns for Question Answering Mark A. Greenwood and Robert Gaizauskas Natural Language.

Creating Subjective and Objective Sentence Classifier from Unannotated Texts Janyce Wiebe and Ellen Riloff Department of Computer Science University of.

Evaluating an Opinion Annotation Scheme Using a New Multi- perspective Question and Answer Corpus (AAAI 2004 Spring) Veselin Stoyanov Claire Cardie Diane.

Number Sense Disambiguation Stuart Moore Supervised by: Anna Korhonen (Computer Lab)‏ Sabine Buchholz (Toshiba CRL)‏

UWMS Data Mining Workshop Content Analysis: Automated Summarizing Prof. Marti Hearst SIMS 202, Lecture 16.

Subjectivity Recognition on Word Senses via Semi-supervised Mincuts Fangzhong Su and Katja Markert School of Computing, University of Leeds Human Language.

Object-Oriented Parsing and Transformation Kenneth Baclawski Northeastern University Scott A. DeLoach Air Force Institute of Technology Mieczyslaw Kokar.

Event-Based Extractive Summarization E. Filatova and V. Hatzivassiloglou Department of Computer Science Columbia University (ACL 2004)

The P YTHY Summarization System: Microsoft Research at DUC 2007 Kristina Toutanova, Chris Brockett, Michael Gamon, Jagadeesh Jagarlamudi, Hisami Suzuki,

Learning Event Durations from Event Descriptions Feng Pan, Rutu Mulkar, Jerry R. Hobbs University of Southern California ACL ’ 06.

Annotating and measuring Temporal relations in texts Philippe Muller and Xavier Tannier IRIT,Université Paul Sabatier COLING 2004.

Narrative Style in The Doll’s House. What is a narrator? The narrator is the person telling the story. They could be involved in the story as a character.

A classifier-based approach to preposition and determiner error correction in L2 English Rachele De Felice, Stephen G. Pulman Oxford University Computing.

AQUAINT Mid-Year PI Meeting – June 2002 Integrating Robust Semantics, Event Detection, Information Fusion, and Summarization for Multimedia Question Answering.

Twitter as a Corpus for Sentiment Analysis and Opinion Mining

Identifying Expressions of Opinion in Context Eric Breck and Yejin Choi and Claire Cardie IJCAI 2007.

Improving a Pipeline Architecture for Shallow Discourse Parsing

Aspect-based sentiment analysis

Automatic Detection of Causal Relations for Question Answering

Presentation transcript:

1 Determining the Hierarchical Structure of Perspective and Speech Expressions Eric Breck and Claire Cardie Cornell University Department of Computer Science

Cornell University Computer Science COLING Events in the News

Cornell University Computer Science COLING Reporting events

Cornell University Computer Science COLING Reporting in text Clapp sums up the environmental movement’s reaction: “The polluters are unreasonable’’ Charlie was angry at Alice’s claim that Bob was unhappy

Cornell University Computer Science COLING Perspective and Speech Expressions (pse’s) A perspective expression is text denoting an explicit opinion, belief, sentiment, etc. The actor was elated that … John’s firm belief in … A speech expression is text denoting spoken or written communication … argued the attorney... … the 9/11 Commission’s final report …

Cornell University Computer Science COLING Grand Vision angryclaim (implicit) unhappy Charlie was angry at Alice’s claim that Bob was unhappy writer CharlieAlice Bob that Bob was unhappy

Cornell University Computer Science COLING This Work angryclaim (implicit) unhappy

Cornell University Computer Science COLING System Output: Pse Hierarchy Charlie was angry at Alice’s claim that Bob was unhappy 78% accurate! angryclaim (implicit) unhappy

Cornell University Computer Science COLING Related Work: Abstract Bergler, 1993 Lexical semantics of reporting verbs Gerard, 2000 Abstract model of news reader

Cornell University Computer Science COLING Related Work: Concrete Bethard et al., 2004 Extract propositional opinions & holders Wiebe, 1994 Tracks “point of view” in narrative text Wiebe et al., 2003 Preliminary results on pse identification Gildea and Jurafsky, 2002 Semantic Role ID - use for finding sources?

Cornell University Computer Science COLING Baseline 1: Only filter through writer Only 66% correct angryclaim (implicit) unhappy

Cornell University Computer Science COLING Baseline 2: Dependency Tree 72% correct angry (implicit) claim unhappy claim unhappy claim unhappy

Cornell University Computer Science COLING A Learning Approach How do we cast the recovery of hierarchical structure as a learning problem? Simplest solution Learn pairwise attachment decisions Is pse parent the parent of pse target ? Combine decisions to form tree Other solutions are possible (n-ary decisions, tree-modeling, etc.)

Cornell University Computer Science COLING Training instances angryclaim (implicit) unhappy

Cornell University Computer Science COLING Training instances angryclaim (implicit) unhappy

Cornell University Computer Science COLING Training instances angryclaim (implicit) unhappy

Cornell University Computer Science COLING Training instances angryclaim (implicit) unhappy

Cornell University Computer Science COLING Training instances angryclaim (implicit) unhappy

Cornell University Computer Science COLING Training instances angryclaim (implicit) unhappy

Cornell University Computer Science COLING Training instances angryclaim (implicit) unhappy

Cornell University Computer Science COLING Decision Combination (implicit) angry claim unhappy

Cornell University Computer Science COLING Decision Combination angry (implicit) angry claim unhappy

Cornell University Computer Science COLING Decision Combination angry (implicit) claim unhappy

Cornell University Computer Science COLING Decision Combination angry claim (implicit) claim unhappy

Cornell University Computer Science COLING Decision Combination angryclaim (implicit) unhappy

Cornell University Computer Science COLING Decision Combination angryclaim (implicit) unhappy

Cornell University Computer Science COLING Decision Combination angryclaim (implicit) unhappy

Cornell University Computer Science COLING Features(1) All features based on error analysis Parse-based features Domination+ variants Positional features Relative position of pse parent and pse target

Cornell University Computer Science COLING Features(2) Lexical features writer’s implicit pse “said” “according to” part of speech Genre-specific features Charlie, she noted, dislikes Chinese food. “Alice disagrees with me,” Bob said.

Cornell University Computer Science COLING Resources GATE toolkit (Cunningham et al, 2002) - part-of-speech, tokenization, sentence boundaries Collins parser (1999) - extracted dependency parses CASS partial parser (Abney, 1997) IND decision trees (Buntine, 1993)

Cornell University Computer Science COLING Data From the NRRC Multi-Perspective Question Answering workshop (Wiebe, 2002) 535 newswire documents (66 for development, 469 for evaluation) All pse’s annotated, along with sources and other information Hierarchical pse structure annotated for each sentence*

Cornell University Computer Science COLING Example (truncated) model One learned tree, truncated to depth 3: pse 0 is parent of pse 1 iff pse 0 is (implicit) And pse 1 is not in quotes OR pse 0 is said Typical trees on development data: Depth ~20, ~700 leaves

Cornell University Computer Science COLING Evaluation Dependency-based metric (Lin, 1995) Percentage of pse’s whose parents are identified correctly Percentage of sentences with perfectly identified structure Performance of binary classifier

Cornell University Computer Science COLING Results

Cornell University Computer Science COLING Error Analysis Pairwise decisions prevent the model from learning larger structure Speech events and perspective expressions behave differently Treebank-style parses don’t always have the structure we need

Cornell University Computer Science COLING Future Work Identify pse’s Identify sources Evaluate alternative structure-learning methods Use the structure to generate perspective-oriented summaries

Cornell University Computer Science COLING Conclusions Understanding pse structure is important for understanding text Automated analysis of pse structure is possible

Cornell University Computer Science COLING Thank you!