Automatic Detection of Causal Relations for Question Answering

Slides:

Advertisements

Similar presentations

School of something FACULTY OF OTHER School of Computing FACULTY OF ENGINEERING Chunking: Shallow Parsing Eric Atwell, Language Research Group.

Advertisements

A Machine Learning Approach to Coreference Resolution of Noun Phrases By W.M.Soon, H.T.Ng, D.C.Y.Lim Presented by Iman Sen.

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

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

NLP and Speech Course Review. Morphological Analyzer Lexicon Part-of-Speech (POS) Tagging Grammar Rules Parser thethe – determiner Det NP → Det.

Empirical Methods in Information Extraction - Claire Cardie 자연어처리연구실 한 경 수

Integration of Information Extraction with an Ontology M. Vargas-Vera, J.Domingue, Y.Kalfoglou, E. Motta and S. Buckingham Sum.

1 Information Retrieval and Extraction 資訊檢索與擷取 Chia-Hui Chang, Assistant Professor Dept. of Computer Science & Information Engineering National Central.

1/17 Acquiring Selectional Preferences from Untagged Text for Prepositional Phrase Attachment Disambiguation Hiram Calvo and Alexander Gelbukh Presented.

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

Artificial Intelligence Research Centre Program Systems Institute Russian Academy of Science Pereslavl-Zalessky Russia.

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

9/8/20151 Natural Language Processing Lecture Notes 1.

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.

1 A study on automatically extracted keywords in text categorization Authors:Anette Hulth and Be´ata B. Megyesi From:ACL 2006 Reporter: 陳永祥 Date:2007/10/16.

Authors: Ting Wang, Yaoyong Li, Kalina Bontcheva, Hamish Cunningham, Ji Wang Presented by: Khalifeh Al-Jadda Automatic Extraction of Hierarchical Relations.

Natural Language Processing Introduction. 2 Natural Language Processing We’re going to study what goes into getting computers to perform useful and interesting.

A Survey for Interspeech Xavier Anguera Information Retrieval-based Dynamic TimeWarping.

Illinois-Coref: The UI System in the CoNLL-2012 Shared Task Kai-Wei Chang, Rajhans Samdani, Alla Rozovskaya, Mark Sammons, and Dan Roth Supported by ARL,

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

PAUL ALEXANDRU CHIRITA STEFANIA COSTACHE SIEGFRIED HANDSCHUH WOLFGANG NEJDL 1* L3S RESEARCH CENTER 2* NATIONAL UNIVERSITY OF IRELAND PROCEEDINGS OF THE.

Jennie Ning Zheng Linda Melchor Ferhat Omur. Contents Introduction WordNet Application – WordNet Data Structure - WordNet FrameNet Application – FrameNet.

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

Discovery of Manner Relations and their Applicability to Question Answering Roxana Girju 1,2, Manju Putcha 1, and Dan Moldovan 1 University of Texas at.

A Language Independent Method for Question Classification COLING 2004.

Mining Topic-Specific Concepts and Definitions on the Web Bing Liu, etc KDD03 CS591CXZ CS591CXZ Web mining: Lexical relationship mining.

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

Semantic web Bootstrapping & Annotation Hassan Sayyadi Semantic web research laboratory Computer department Sharif university of.

Automatic recognition of discourse relations Lecture 3.

CPSC 422, Lecture 27Slide 1 Intelligent Systems (AI-2) Computer Science cpsc422, Lecture 27 Nov, 16, 2015.

Answer Mining by Combining Extraction Techniques with Abductive Reasoning Sanda Harabagiu, Dan Moldovan, Christine Clark, Mitchell Bowden, Jown Williams.

Using Wikipedia for Hierarchical Finer Categorization of Named Entities Aasish Pappu Language Technologies Institute Carnegie Mellon University PACLIC.

Unsupervised Relation Detection using Automatic Alignment of Query Patterns extracted from Knowledge Graphs and Query Click Logs Panupong PasupatDilek.

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

Department of Computer Science The University of Texas at Austin USA Joint Entity and Relation Extraction using Card-Pyramid Parsing Rohit J. Kate Raymond.

Overview of Statistical NLP IR Group Meeting March 7, 2006.

An Ontology-based Automatic Semantic Annotation Approach for Patent Document Retrieval in Product Innovation Design Feng Wang, Lanfen Lin, Zhou Yang College.

The University of Illinois System in the CoNLL-2013 Shared Task Alla RozovskayaKai-Wei ChangMark SammonsDan Roth Cognitive Computation Group University.

Question Classification Ling573 NLP Systems and Applications April 25, 2013.

Named entities recognition Jana Kravalová. Content 1. Task 2. Data 3. Machine learning 4. SVM 5. Evaluation and results.

Automatic Ontology Extraction Miloš Husák RASLAN 2010.

Event Detection Aliaksei Antonau 1 5. Juli

Sentiment analysis algorithms and applications: A survey

Using lexical chains for keyword extraction

Chaveevan Pechsiri Dhurakij Pundij University

CRF &SVM in Medication Extraction

INAGO Project Automatic Knowledge Base Generation from Text for Interactive Question Answering.

Presented by: Hassan Sayyadi

Learning from Data.

Mining the Data Charu C. Aggarwal, ChengXiang Zhai

Summarizing Entities: A Survey Report

Discovery of Inference Rules for Question Answering

Machine Learning Week 1.

Social Knowledge Mining

Donna M. Gates Carnegie Mellon University

CSCI 5832 Natural Language Processing

Introduction Task: extracting relational facts from text

Text-to-Text Generation

Text Mining & Natural Language Processing

MACHINE LEARNING TECHNIQUES IN IMAGE PROCESSING

Text Mining & Natural Language Processing

MACHINE LEARNING TECHNIQUES IN IMAGE PROCESSING

CS246: Information Retrieval

Enriching Taxonomies With Functional Domain Knowledge

By Hossein Hematialam and Wlodek Zadrozny Presented by

Extracting Why Text Segment from Web Based on Grammar-gram

Statistical NLP: Lecture 10

Presentation transcript:

Automatic Detection of Causal Relations for Question Answering 2003 Roxana Girju Baylor University

content Background Main Task Result Analysis Application in QA

Background Causation relations expressed in English Explict (cause, lead to, kill, dry…) Implict (no keyword) Previous Work Using knowledge-based inferences

Main Task classifier A classifier based in machine learning input: sentence(<NP1, verb, NP2>) vectorization main task output: YES or NO classifier

Vectorization Training Example (entityNP1,psychological-featureNP1,abstractionNP1, stateNP1,eventNP1,actNP1,groupNP1,possessionNP1,phenomeno nNP1; verb; entityNP2,psychological-featureNP2,abstractionNP2, stateNP2,eventNP2,actNP2,groupNP2,possessionNP2,phenomeno nNP2; target)

Sentence: Earthquake generates Tsunami Vector: <f, f, f, f, f, f, f, f, t, generate, f, f, f, f, f, t, f, f, f> The complete training example： <f, f, f, f, f, f, f, f, t, generate, f, f, f, f, f, t, f, f, f, YES>

How to build the training set? Step1: find the sentences (Where did the data come from?) Step2: select features (How to vectorization?)

Find the Sentences Step1: find NP pairs contain causation relationship WordNet 1.7 contains 429 such NP pairs, the most frequent being medicine.(about 58.28%)

Step2: For each pair of causation nouns determined above, search the Internet and retain only sentences containing the pair. From these sentences,determine automatically all the parterns <NP1 verb/verb_expression NP2>

Step3: searching the text collection and retained 120 sentences containing the verb 60*120 = 7200 ( corpus A) Step4: extracting 6523 relationships of the type <NP1 verb NP2> from 7200 sentences. 2101 are causal relations, while 4422 are not (manually annotate)

Select Features Both lexical and semantic features Lexical features: verb/verb_expression Semantic features: 9 noun hierarchies in WordNet: entity, psychological feature, abstraction, state, event, act, group, possession, and phenomenon.

Training Algorithm C4.5 decision tree Inductive bias: a preference for the shorter tree that places high information gain attributes closer to the root

Result Analysis 683 relationships of the type <NP1 verb NP2> in corpus B 102/(115+38) = 66.67%

Reasons for Errors Mostly the fact that the causal pattern is very ambiguous Incorrect parsing of noun phrases The use of the rules with smaller accuracy(e.g. 63%) The lack of named entities in WordNet

Application in QA 50 questions tested, 61% precision for QA system with the causation module, and 36% precision for QA system without the module.

Thanks!