July 9, 2003ACL 2003 1 An Improved Pattern Model for Automatic IE Pattern Acquisition Kiyoshi Sudo Satoshi Sekine Ralph Grishman New York University.

Slides:



Advertisements
Similar presentations
Chapter 5: Introduction to Information Retrieval
Advertisements

Problem Semi supervised sarcasm identification using SASI
The Impact of Task and Corpus on Event Extraction Systems Ralph Grishman New York University Malta, May 2010 NYU.
NYU ANLP-00 1 Automatic Discovery of Scenario-Level Patterns for Information Extraction Roman Yangarber Ralph Grishman Pasi Tapanainen Silja Huttunen.
Query Dependent Pseudo-Relevance Feedback based on Wikipedia SIGIR ‘09 Advisor: Dr. Koh Jia-Ling Speaker: Lin, Yi-Jhen Date: 2010/01/24 1.
Information Retrieval Ling573 NLP Systems and Applications April 26, 2011.
NaLIX: A Generic Natural Language Search Environment for XML Data Presented by: Erik Mathisen 02/12/2008.
Event Extraction: Learning from Corpora Prepared by Ralph Grishman Based on research and slides by Roman Yangarber NYU.
Basi di dati distribuite Prof. M.T. PAZIENZA a.a
8/13/2004NYCNLP (COLING 2004) Cross-lingual Information Extraction System Evaluation Kiyoshi Sudo Satoshi Sekine Ralph Grishman New York University.
Automatic Acquisition of Lexical Classes and Extraction Patterns for Information Extraction Kiyoshi Sudo Ph.D. Research Proposal New York University Committee:
Pre-CODIE System: Kiyoshi Sudo Satoshi Sekine Ralph Grishman New York University Crosslingual On-Demand Information Extraction IE from Japanese source.
Improving Machine Learning Approaches to Coreference Resolution Vincent Ng and Claire Cardie Cornell Univ. ACL 2002 slides prepared by Ralph Grishman.
A Framework for Named Entity Recognition in the Open Domain Richard Evans Research Group in Computational Linguistics University of Wolverhampton UK
Mining the Medical Literature Chirag Bhatt October 14 th, 2004.
Introduction to Machine Learning Approach Lecture 5.
Chapter 5: Information Retrieval and Web Search
Information Extraction with Unlabeled Data Rayid Ghani Joint work with: Rosie Jones (CMU) Tom Mitchell (CMU & WhizBang! Labs) Ellen Riloff (University.
Information Retrieval in Practice
Learning Information Extraction Patterns Using WordNet Mark Stevenson and Mark A. Greenwood Natural Language Processing Group University of Sheffield,
Copyright R. Weber Machine Learning, Data Mining ISYS370 Dr. R. Weber.
Empirical Methods in Information Extraction Claire Cardie Appeared in AI Magazine, 18:4, Summarized by Seong-Bae Park.
Opinion Mining Using Econometrics: A Case Study on Reputation Systems Anindya Ghose, Panagiotis G. Ipeirotis, and Arun Sundararajan Leonard N. Stern School.
Natural Language Processing Group Department of Computer Science University of Sheffield, UK Improving Semi-Supervised Acquisition of Relation Extraction.
Reyyan Yeniterzi Weakly-Supervised Discovery of Named Entities Using Web Search Queries Marius Pasca Google CIKM 2007.
A Semantic Approach to IE Pattern Induction Mark Stevenson and Mark Greenwood Natural Language Processing Group University of Sheffield, UK.
A Simple Unsupervised Query Categorizer for Web Search Engines Prashant Ullegaddi and Vasudeva Varma Search and Information Extraction Lab Language Technologies.
1 Technologies for (semi-) automatic metadata creation Diana Maynard.
On the Issue of Combining Anaphoricity Determination and Antecedent Identification in Anaphora Resolution Ryu Iida, Kentaro Inui, Yuji Matsumoto Nara Institute.
Péter Schönhofen – Ad Hoc Hungarian → English – CLEF Workshop 20 Sep 2007 Performing Cross-Language Retrieval with Wikipedia Participation report for Ad.
SYMPOSIUM ON SEMANTICS IN SYSTEMS FOR TEXT PROCESSING September 22-24, Venice, Italy Combining Knowledge-based Methods and Supervised Learning for.
A S URVEY ON I NFORMATION E XTRACTION FROM D OCUMENTS U SING S TRUCTURES OF S ENTENCES Chikayama Taura Lab. M1 Mitsuharu Kurita 1.
Chapter 6: Information Retrieval and Web Search
Playing Biology ’ s Name Game: Identifying Protein Names In Scientific Text Daniel Hanisch, Juliane Fluck, Heinz-Theodor Mevissen and Ralf Zimmer Pac Symp.
A Semantic Approach to IE Pattern Induction Mark Stevenson and Mark A. Greenwood Natural Language Processing Group University of Sheffield, UK.
Collocations and Information Management Applications Gregor Erbach Saarland University Saarbrücken.
Bootstrapping for Text Learning Tasks Ramya Nagarajan AIML Seminar March 6, 2001.
LANGUAGE MODELS FOR RELEVANCE FEEDBACK Lee Won Hee.
A Scalable Machine Learning Approach for Semi-Structured Named Entity Recognition Utku Irmak(Yahoo! Labs) Reiner Kraft(Yahoo! Inc.) WWW 2010(Information.
Minimally Supervised Event Causality Identification Quang Do, Yee Seng, and Dan Roth University of Illinois at Urbana-Champaign 1 EMNLP-2011.
Comparing Information Extraction Pattern Models Mark Stevenson and Mark A. Greenwood Natural Language Processing Group University of Sheffield, UK.
Using Semantic Relations to Improve Passage Retrieval for Question Answering Tom Morton.
Authors: Marius Pasca and Benjamin Van Durme Presented by Bonan Min Weakly-Supervised Acquisition of Open- Domain Classes and Class Attributes from Web.
1 Automatic indexing Salton: When the assignment of content identifiers is carried out with the aid of modern computing equipment the operation becomes.
Collocations and Terminology Vasileios Hatzivassiloglou University of Texas at Dallas.
August 17, 2005Question Answering Passage Retrieval Using Dependency Parsing 1/28 Question Answering Passage Retrieval Using Dependency Parsing Hang Cui.
Semantic web Bootstrapping & Annotation Hassan Sayyadi Semantic web research laboratory Computer department Sharif university of.
Threshold Setting and Performance Monitoring for Novel Text Mining Wenyin Tang and Flora S. Tsai School of Electrical and Electronic Engineering Nanyang.
Multi-level Bootstrapping for Extracting Parallel Sentence from a Quasi-Comparable Corpus Pascale Fung and Percy Cheung Human Language Technology Center,
1 Adaptive Subjective Triggers for Opinionated Document Retrieval (WSDM 09’) Kazuhiro Seki, Kuniaki Uehara Date: 11/02/09 Speaker: Hsu, Yu-Wen Advisor:
Discovering Relations among Named Entities from Large Corpora Takaaki Hasegawa *, Satoshi Sekine 1, Ralph Grishman 1 ACL 2004 * Cyberspace Laboratories.
1 Gloss-based Semantic Similarity Metrics for Predominant Sense Acquisition Ryu Iida Nara Institute of Science and Technology Diana McCarthy and Rob Koeling.
FILTERED RANKING FOR BOOTSTRAPPING IN EVENT EXTRACTION Shasha Liao Ralph York University.
Towards Total Scene Understanding: Classification, Annotation and Segmentation in an Automatic Framework N 工科所 錢雅馨 2011/01/16 Li-Jia Li, Richard.
Exploiting Named Entity Taggers in a Second Language Thamar Solorio Computer Science Department National Institute of Astrophysics, Optics and Electronics.
Extracting and Ranking Product Features in Opinion Documents Lei Zhang #, Bing Liu #, Suk Hwan Lim *, Eamonn O’Brien-Strain * # University of Illinois.
Identifying “Best Bet” Web Search Results by Mining Past User Behavior Author: Eugene Agichtein, Zijian Zheng (Microsoft Research) Source: KDD2006 Reporter:
Using Semantic Relations to Improve Information Retrieval
Jean-Yves Le Meur - CERN Geneva Switzerland - GL'99 Conference 1.
Question Answering Passage Retrieval Using Dependency Relations (SIGIR 2005) (National University of Singapore) Hang Cui, Renxu Sun, Keya Li, Min-Yen Kan,
Relation Extraction (RE) via Supervised Classification See: Jurafsky & Martin SLP book, Chapter 22 Exploring Various Knowledge in Relation Extraction.
Automatically Labeled Data Generation for Large Scale Event Extraction
Information Organization: Overview
Using lexical chains for keyword extraction
Relation Extraction CSCI-GA.2591
Summarizing Entities: A Survey Report
Multimedia Information Retrieval
Applying Key Phrase Extraction to aid Invalidity Search
Information Organization: Overview
Unsupervised Learning of Narrative Schemas and their Participants
Presentation transcript:

July 9, 2003ACL An Improved Pattern Model for Automatic IE Pattern Acquisition Kiyoshi Sudo Satoshi Sekine Ralph Grishman New York University

July 9, 2003 ACL Automatic Pattern Acquisition The cost of manual construction of extraction patterns is very high. The cost of preparation of annotated data for supervised learning is still high. The recent trend of the researches on pattern acquisition is un- (semi-) supervised learning.

July 9, 2003 ACL Information Extraction Identifying entities from source text and mapping from source text to pre-defined table. “A smiling Palestinian suicide bomber triggered a massive explosion in the heavily policed heart of downtown Jerusalem today, …” Date: Location: Perpetrator: downtown Jerusalem A … suicide bomber today

July 9, 2003 ACL Local Context Local contexts provides a useful information to identify entities. “A smiling Palestinian suicide bomber triggered a massive explosion in the heavily policed heart of downtown Jerusalem today, …” Date: Location: Perpetrator: downtown Jerusalem A … suicide bomber today

July 9, 2003 ACL Extraction Pattern Generalize each instance of entity and its local context into an extraction pattern. “A smiling Palestinian suicide bomber triggered a massive explosion in the heavily policed heart of downtown Jerusalem today, …” triggered a massive explosion NE category Association Rule Perpetrator:

July 9, 2003 ACL Dependency Tree for Pattern Model Introducing syntax (dependency tree) clarify the relation of arguments with predicates. triggered a massive explosion A smiling Palestinian suicide bomber heart heavily policeddowntown Jerusalem today SBJ OBJ ADV IN

July 9, 2003 ACL Extraction Pattern models Predicate-Argument model (Yangarber et al. 2000) – Based on direct relation with a predicate Chain model (Sudo et al. 2001) – Based on a chain of modifiers of a predicate triggered explosion triggered triggered heart downtown Jerusalem

July 9, 2003 ACL Predicate-Argument model Predicate-Argument model is based on the direct relation of a predicate and its arguments. triggered a massive explosion heart heavily policeddowntown Jerusalem SBJ OBJ ADV IN

July 9, 2003 ACL Chain model Chain model can capture the chain of modifier with an arbitrary depth in the tree, regardless phrasal or clausal boundary. triggered a massive explosion heart heavily policed SBJ OBJ ADV IN (Sudo et al. 2001) reported 5% gain in recall with same level of precision over Predicate-Argument model.

July 9, 2003 ACL Problem Chain model contains only one node at each level of the tree. triggered a massive explosion heart heavily policeddowntown Jerusalem SBJ OBJ ADV IN

July 9, 2003 ACL Problem Lack of the context can make a pattern too general, causing a false match on irrelevant text. triggered a national financial crisis the Mexican peso last week SBJ OBJ ADV “ The Mexican peso was devalued and triggered a national financial crisis last week. ”

July 9, 2003 ACL Subtree model Generalization of Predicate-Argument and Chain model – Any connected subtree of a dependency tree will be considered as a candidate of extraction pattern. – Give reliable contexts as Predicate-Argument model does – Capable to capture long-distance relationship in dependency tree

July 9, 2003 ACL Subtree model Subtree model can provide more relevant contexts, as well as have a flexibility in traversing arbitrary depth in the tree. triggered a massive explosion heart heavily policeddowntown Jerusalem SBJ OBJ ADV IN

July 9, 2003 ACL Experiment Entity Extraction task – Identify if an NE instance is involved in scenario or not Management Succession – Person, Organization, Post (Position_Title) Murder Arrest – Arresting Agency (Organization), Suspect (Person), Charge – Source: Japanese newspaper 117,109 articles (Mainichi 1995) – Test: accumulated from Mainichi 1994 Succession 148 documents Arrest 205 documents

July 9, 2003 ACL Acquisition Method The target scenario is specified by TREC-like narrative description – “ Management Succession at the level of executives of a company. The topic of interest should not be limited to the promotion inside the company mentioned, but also includes hiring executives from outside the company of their resignation. ” [Translated from Japanese] Preprocessing – Dependency Analysis, NE-tagging Document Retrieval R

July 9, 2003 ACL Acquisition Method Count all possible subtrees in R – subtree-mining algorithm (Zaki et al. 2002) – make a Pattern List of those that conform the pattern model Rank each subtree R For each subtree i, number of times subtree i occurred in the documents in R

July 9, 2003 ACL Acquisition Method Count all possible subtrees in R – subtree-mining algorithm (Zaki et al. 2002) – make a Pattern List of those that conform the pattern model Rank each subtree R For each subtree i, number of documents in the source which contain subtree i

July 9, 2003 ACL Overlapping patterns Pattern List contains many overlapping patterns – (19) ( report) (( -wa) Happyo_suru) – (480) ( report that … be appointed) (( -wa) (Shunin_suru-to) Happyo-suru)  works as a weight on patterns with more relevant context [Translated from Japanese]

July 9, 2003 ACL  comparison

July 9, 2003 ACL Unsupervised Parameter Tuning Unsupervised text classification task by pattern matching – retrieved … 300 documents retrieved – random … 300 randomly selected – For each precision-recall curve for , calculate the area that the curve covers. Pearson correlation coefficient – r p = 0.80 with 2% confidence

July 9, 2003 ACL Extraction Performance

July 9, 2003 ACL Lessons learned Subtree vs. Chain – Too-general patterns got more penalized for Subtree model Penalize by Inversed Document Frequency (Subtree, Chain) More scenario-specific patterns got promoted (Subtree)

July 9, 2003 ACL Lessons learned Subtree vs. Predicate-Argument – Patterns with nominalized predicates Extraction patterns for headlines e.g. (promotion of ) (( -no) Shokaku) – Noun phrase patterns with chain of modifiers e.g. ( with ministerial authority) (((Daihyoken-no (Aru-  ( ))) [Translated from Japanese]

July 9, 2003 ACL Lessons to be learned Enhanced scoring function by modern IR technique. – Some techniques directly helps pattern acquisition e.g. relevance feedback – However, note the crucial difference between Pattern acquisition and IR Same pattern does not appear twice in a document. Generic variable instead of sticking to Named Entity categories as place holder. – How robust can a pattern be without semantic restriction?

July 9, 2003 ACL Conclusion We proposed Subtree model as a generalization of – Predicate-Argument model – Chain model Subtree model patterns overly performed better than other models in Entity Extraction tasks. Scoring function needs a special consideration for overlapping patterns. Unsupervised parameter tuning by text classification task.