 Motivation:  Actor: [awards, height, age, weight, birthdate, birthplace, cause of death, real name]  Painter: [paintings, biography, bibliography,

Slides:

Advertisements

Similar presentations

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

Advertisements

Linking Entities in #Microposts ROMIL BANSAL, SANDEEP PANEM, PRIYA RADHAKRISHNAN, MANISH GUPTA, VASUDEVA VARMA INTERNATIONAL INSTITUTE OF INFORMATION TECHNOLOGY,

ArtEquAKT Harith Alani, Sanghee Kim, Wendy Hall, Paul Lewis, David Millard, Nigel Shadbolt, Mark Weal.

 Andisheh Keikha Ryerson University Ebrahim Bagheri Ryerson University May 7 th

Ewa Rudnicka, Wojciech Witkowski, Maciej Piasecki G4.19 Research Group Institute of Informatics, Wrocław University of Technology nlp.pwr.wroc.pl plwordnet.pwr.wroc.pl.

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

Automatic Discovery of Technology Trends from Patent Text Youngho Kim, Yingshi Tian, Yoonjae Jeong, Ryu Jihee, Sung-Hyon Myaeng School of Engineering Information.

Slide 1 EE3J2 Data Mining EE3J2 Data Mining Lecture 7 Topic Spotting & Query Expansion Martin Russell.

SLIDE 1IS 202 – FALL 2004 Lecture 13: Midterm Review Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30 am -

Sentiment Lexicon Creation from Lexical Resources BIS 2011 Bas Heerschop Erasmus School of Economics Erasmus University Rotterdam

Overall Information Extraction vs. Annotating the Data Conference proceedings by O. Etzioni, Washington U, Seattle; S. Handschuh, Uni Krlsruhe.

1 Noun Homograph Disambiguation Using Local Context in Large Text Corpora Marti A. Hearst Presented by: Heng Ji Mar. 29, 2004.

Semantic Video Classification Based on Subtitles and Domain Terminologies Polyxeni Katsiouli, Vassileios Tsetsos, Stathes Hadjiefthymiades P ervasive C.

Saarbrucken / Germany ¨

Ontology Learning and Population from Text: Algorithms, Evaluation and Applications Chapters Presented by Sole.

Feature Selection for Automatic Taxonomy Induction The Features Input: Two terms Output: A numeric score, or. Lexical-Syntactic Patterns Co-occurrence.

Webpage Understanding: an Integrated Approach

PAIRS Forming a ranked list using mined, pairwise comparisons Reed A. Coke, David C. Anastasiu, Byron J. Gao.

COMP423: Intelligent Agent Text Representation. Menu – Bag of words – Phrase – Semantics – Bag of concepts – Semantic distance between two words.

Page 1 WEB MINING by NINI P SURESH PROJECT CO-ORDINATOR Kavitha Murugeshan.

Jiuling Zhang  Why perform query expansion?  WordNet based Word Sense Disambiguation WordNet Word Sense Disambiguation  Conceptual Query.

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.

1 LiveClassifier: Creating Hierarchical Text Classifiers through Web Corpora Chien-Chung Huang Shui-Lung Chuang Lee-Feng Chien Presented by: Vu LONG.

Semantic Matching Fausto Giunchiglia work in collaboration with Pavel Shvaiko The Italian-Israeli Forum on Computer Science, Haifa, June 17-18, 2003.

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

Query Expansion By: Sean McGettrick. What is Query Expansion? Query Expansion is the term given when a search engine adding search terms to a user’s weighted.

1 Query Operations Relevance Feedback & Query Expansion.

10/22/2015ACM WIDM'20051 Semantic Similarity Methods in WordNet and Their Application to Information Retrieval on the Web Giannis Varelas Epimenidis Voutsakis.

WordNet–Based Collaborative Weighting for Ranking Web Pages Hyoungil Kim, Juntae Kim Dongguk University, Seoul, Korea Kyeonah Yu Duksung Women ’ s University,

Presenter: Shanshan Lu 03/04/2010

CMPE 493 INTRODUCTION INFORMATION RETRIEVAL PERSONALIZED QUERY EXPANSION FOR THE WEB Chirita, P.A., Firan, C.S., and Nejdl, W. SIGIR, 2007, pp Bahtiyar.

1 CSI 5180: Topics in AI: Natural Language Processing, A Statistical Approach Instructor: Nathalie Japkowicz Objectives of.

Automatic Set Instance Extraction using the Web Richard C. Wang and William W. Cohen Language Technologies Institute Carnegie Mellon University Pittsburgh,

WEB MINING. In recent years the growth of the World Wide Web exceeded all expectations. Today there are several billions of HTML documents, pictures and.

Authors: Marius Pasca and Benjamin Van Durme Presented by Bonan Min Weakly-Supervised Acquisition of Open- Domain Classes and Class Attributes from Web.

Using Semantic Relatedness for Word Sense Disambiguation

UIC at TREC 2006: Genomics Track Wei Zhou, Clement T. Yu University of Illinois at Chicago Nov. 16, 2006.

Exploiting Ontologies for Automatic Image Annotation Munirathnam Srikanth, Joshua Varner, Mitchell Bowden, Dan Moldovan Language Computer Corporation SIGIR.

An Applied Ontological Approach to Computational Semantics Sam Zhang.

Acquisition of Categorized Named Entities for Web Search Marius Pasca Google Inc. from Conference on Information and Knowledge Management (CIKM) ’04.

NATURAL LANGUAGE PROCESSING Zachary McNellis. Overview  Background  Areas of NLP  How it works?  Future of NLP  References.

Acceso a la información mediante exploración de sintagmas Anselmo Peñas, Julio Gonzalo y Felisa Verdejo Dpto. Lenguajes y Sistemas Informáticos UNED III.

The Development of a search engine & Comparison according to algorithms Sung-soo Kim The final report.

ENHANCING CLUSTER LABELING USING WIKIPEDIA David Carmel, Haggai Roitman, Naama Zwerdling IBM Research Lab SIGIR’09.

Toward Entity Retrieval over Structured and Text Data Mayssam Sayyadian, Azadeh Shakery, AnHai Doan, ChengXiang Zhai Department of Computer Science University.

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

Chapter 6. Inference beyond the index 2007 년 1 월 30 일 부산대학교 인공지능연구실 김민호 Text : FINDING OUT ABOUT Page. 182 ~ 251.

Semantic search-based image annotation Petra Budíková, FI MU CEMI meeting, Plzeň,

Hui Fang (ACL 2008) presentation 2009/02/04 Rick Liu.

NTNU Speech Lab 1 Topic Themes for Multi-Document Summarization Sanda Harabagiu and Finley Lacatusu Language Computer Corporation Presented by Yi-Ting.

Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 Enhancing Text Clustering by Leveraging Wikipedia Semantics.

Research Vocabulary. Research The investigation of a particular topic using a variety of reliable resources.

Query expansion COMP423. Menu Query expansion Two approaches Relevance feedback Thesaurus-based Most Slides copied from

2016/9/301 Exploiting Wikipedia as External Knowledge for Document Clustering Xiaohua Hu, Xiaodan Zhang, Caimei Lu, E. K. Park, and Xiaohua Zhou Proceeding.

Automatic Writing Evaluation

Guangbing Yang Presentation for Xerox Docushare Symposium in 2011

An Empirical Study of Learning to Rank for Entity Search

Information Retrieval and Web Search

ArtsSemNet: From Bilingual Dictionary To Bilingual Semantic Network

Information Retrieval and Web Search

What is IR? In the 70’s and 80’s, much of the research focused on document retrieval In 90’s TREC reinforced the view that IR = document retrieval Document.

Applying Key Phrase Extraction to aid Invalidity Search

Submitted By: Usha MIT-876-2K11 M.Tech(3rd Sem) Information Technology

WordNet WordNet, WSD.

Retrieval of Authentic Documents for Reader-Specific Lexical Practice

Semantic Similarity Methods in WordNet and their Application to Information Retrieval on the Web Yizhe Ge.

Giannis Varelas Epimenidis Voutsakis Paraskevi Raftopoulou

Information Retrieval and Web Design

Dynamic Word Sense Disambiguation with Semantic Similarity

Presentation transcript:

 Motivation:  Actor: [awards, height, age, weight, birthdate, birthplace, cause of death, real name]  Painter: [paintings, biography, bibliography, autobiography, artwork, self portraits, quotations, bio, quotes, life history].

 Labeled Classes of Instances: - Similarly to [9], the extraction of labeled classes of instances relies on hand-written patterns, widely used in literature on extracting conceptual hierarchies from text

 Attributes of Labeled Classes of Instances 1. word 2. construction of internal search 3. construction of a reference internal search 4. ranking

 Conceptual Hierarchies: - Manually-constructed language resources such as WordNet provide reliable, wide-coverage upper-level conceptual hierarchies, by grouping phrases with the same meaning

 Similar-Sense Selection 1. synonyms 2. siblings, or coordinate terms 3. immediate superconcepts, or hypernyms 4. immediate subconcepts, or hyponyms available in the WordNet hierarchies around that sense

 Hierarchical Propagation - As mentioned earlier, a labeled class is accompanied by a ranked list of attributes extracted from query logs.  Coverage-Based Attribute Specificity

 Hierarchical Attributes

 This paper introduces an extraction framework for exploiting labeled classes of instances acquired from a combination of documents and search query logs, to extract attributes over conceptual hierarchies.

 [1]M.BankoandO.Etzioni.Thetradeoffsbetweenopenandtraditionalrelationextr action.InProceedingsofthe46thAnnualMeetingoftheAssociationforComputati onalLinguistics(ACL-08),pages28–6, Columbus, Ohio,  [2] T. Brants. TnT - a statistical part of speech tagger. In Proceedings of the 6th Conference on Applied Natural Language Processing (ANLP-00), pages 224–31, Seattle, Washington,  [3] C. Fellbaum, editor. WordNet: An Electronic Lexical Database and Some of its Applications. MIT Press,  [4] W. Gao, C. Niu, J. Nie, M. Zhou, J. Hu, K.Wong, and H. Hon. Cross- lingual query suggestion using query logs of different languages. In Proceedings of the 30th ACM Conference on Research and Development in Information Retrieval (SIGIR-07), pages 463–70, Amsterdam, The Netherlands,  [5] M. Hearst. Automatic acquisition of hyponyms from large text corpora. In Proceedings of the 14 th InternationalConferenceonComputationalLinguistics(COLING- 92),pages539–45, Nantes, France, 1992.