'Question answering' diaporamas de présentation

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1.27k views • 113 slides

Question Processing: Formulation & Expansion

Question Processing: Formulation & Expansion. Ling573 NLP Systems and Applications May 2, 2013. Deeper Processing for Query Formulation. MULDER (Kwok, Etzioni , & Weld) Converts question to multiple search queries Forms which match target Vary specificity of query

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Exploring PropBanks for English and Hindi

Exploring PropBanks for English and Hindi. Ashwini Vaidya Dept of Linguistics University of Colorado, Boulder. Why is semantic information important?. Imagine an automatic question answering system Who created the first effective polio vaccine? Two possible choices:

860 views • 72 slides

Information Extractions from Texts

Information Extractions from Texts. ??????? ?????? , ?.?.?., ?.?.? . ???????? ??????? ??????????? ???. Content. Motivation Applications Information Extraction Steps Source Selection and Preparation Entity Recognition Named Entity Annotation Entity Disambiguation. Motivation.

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Towards overcoming the knowledge acquisition bottleneck in declarative logic programming applications: embracing natural

Towards overcoming the knowledge acquisition bottleneck in declarative logic programming applications: embracing natural language inputs. Chitta Baral Department of Computer Sc. & Eng. Arizona State University Tempe, AZ 85287.

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2ID10: Information Retrieval Lecture 2: IR Evaluation & Queries

2ID10: Information Retrieval Lecture 2: IR Evaluation & Queries. Lora Aroyo 4 April 2006. User Query. Lecture 1 Summary. Compare the information need with the information. generate a ranking which reflects relevance. Information Need. Ranked list of documents. IR System. feedback.

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Natural Language Processing for the Web

Natural Language Processing for the Web. Prof. Kathleen McKeown 722 CEPSR, 939-7118 Office Hours: Wed, 1-2; Tues 4-5 TA: Yves Petinot 719 CEPSR, 939-7116 Office Hours: Thurs 12-1, 8-9. Projects. Proposal due today Hand in via courseworks (by midnight). Today.

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Question Answering

Question Answering. What is Question Answering?. Question Answering. One of the oldest NLP tasks (punched card systems in 1961). Simmons, Klein, McConlogue . 1964. Indexing and Dependency Logic for Answering English Questions. American Documentation 15:30, 196-204.

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Natural Language Questions for the Web of Data

Natural Language Questions for the Web of Data. Mohamed Yahya 1 , Klaus Berberich 1 , Shady Elbassuoni 2 Maya Ramanath 3 , Volker Tresp 4 , Gerhard Weikum 1 1 Max Planck Institute for Informatics, Germany 2 Qatar Computing Research Institute 3 Dept. of CSE, IIT-Delhi, India

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Natural Language Questions for the Web of Data

Natural Language Questions for the Web of Data. Mohamed Yahya , Klaus Berberich , Gerhard Weikum Max Planck Institute for Informatics, Germany Shady Elbassuoni Qatar Computing Research Institute Maya Ramanath Dept. of CSE, IIT-Delhi, India Volker Tresp

602 views • 47 slides

Automatic Text Summarization

By : asef poormasoomi autumn 2009. Automatic Text Summarization. Introduction. summary : brief but accurate representation of the contents of a document. Motivation. Abstracts for Scientific and other articles News summarization (mostly Multiple document summarization) ?

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Mondeca Smart Content Factory

Mondeca Smart Content Factory. December 2012. INTRODUCTION. Value proposition. H igher value for end users. Efficient access through better retrieval and personalization. Clarity and insight through better contextualization, discovery, and analytics.

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An Extended Model of Natural Logic

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D 3 : Passage Retrieval

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Online Learning of Semantic Relations

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Semantics and Context in Natural Language Processing (NLP)

Semantics and Context in Natural Language Processing (NLP). Ari Rappoport The Hebrew University. Form vs. Meaning. Solid NLP progress with statistical learning OK for classification, search, prediction Prediction: language models in speech to text

361 views • 14 slides

For Wednesday

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Natural Language Processing for the Web

Natural Language Processing for the Web. Prof. Kathleen McKeown 722 CEPSR, 939-7118 Office Hours: Wed, 1-2; Tues 4-5 TA: Yves Petinot 719 CEPSR, 939-7116 Office Hours: Thurs 12-1, 8-9. Today.

195 views • 9 slides

CS4705 Natural Language Processing Fall 2009

CS4705 Natural Language Processing Fall 2009. What will we study in this course?. How can machines recognize and generate text and speech? Human language phenomena Theories, often drawn from linguistics, psychology Algorithms Applications. Syntax. Word Order John hit Bill

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Introduction

The Jikitou Biomedical Question Answering System: Using High-Performance Computing to Preprocess Possible Answers Michael A. Bauer 1,2 , Daniel Berleant 1 , Robert E. Belford 1 , and Roger A. Hall 1 1 University of Arkansas at Little Rock 2 University of Arkansas for Medical Sciences.

148 views • 1 slides