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Wednesday, July 29, 2020 | History

6 edition of Open-Domain Question Answering from Large Text Collections (Center for the Study of Language and Information - Lecture Notes) found in the catalog.

Open-Domain Question Answering from Large Text Collections (Center for the Study of Language and Information - Lecture Notes)

by Marius Pasca

  • 222 Want to read
  • 23 Currently reading

Published by Center for the Study of Language and Inf .
Written in English

    Subjects:
  • Language & Linguistics,
  • Questioning,
  • Language Arts & Disciplines,
  • Language Arts / Linguistics / Literacy,
  • Language,
  • Linguistics,
  • Language Arts & Disciplines / Linguistics,
  • Computational linguistics,
  • Data processing

  • The Physical Object
    FormatHardcover
    Number of Pages157
    ID Numbers
    Open LibraryOL8746464M
    ISBN 101575864274
    ISBN 109781575864273
    OCLC/WorldCa51264387

    Paşca, M.: Open-Domain Question Answering from Large Text Collections. CSLI Studies in Computational Linguistics. CSLI Publications () Google ScholarCited by: 2.   Denoising Distantly Supervised Open-Domain Question Answering. ACL • thunlp/OpenQA • Distantly supervised open-domain question answering (DS-QA) aims to find answers in collections of unlabeled text.

    " A related task is open-domain question answering (QA) where the model is not provided with this oracle context. Typically, open-domain QA systems include a mechanism to look up information in an external knowledge source. This setting is similar to an \" open-book \" exam. \n ", " \n ". ity to answer questions given large corpora. Prior datasets (such as those used in (Chen et al., )) are constructed by first selecting a passage and then constructing questions about that pas-sage. This design (intentionally) ignores some of the subproblems required to answer open-domain questions from corpora, namely searching for pas-Cited by:

    or open-domain QA systems. This paper propose d a new approach architecture for open domain question-answering system depends on the ontology and wordnet to improve answer accuracy. The result of experiments generated a score of % forWH questions and % for Yes/No questions. All Chicago e-books are on sale at 30% off with the code EBOOK About; Contact; Info & Services; Books by Marius Pasca. Open-Domain Question Answering from Large Text Collections Marius Pasca. About the Author. Free E-book Of The Month. Randall Jarrell. Pictures from an Institution. Get it for free! About E-books. Publishers We Distribute.


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Open-Domain Question Answering from Large Text Collections (Center for the Study of Language and Information - Lecture Notes) by Marius Pasca Download PDF EPUB FB2

Success in the question answering (QA) track of TREC, the major annual text retrieval con-ference. Open-domain QA involves retrieving rel-evant passages from large text collections (e.g., newswire or the WWW) in hopes of finding answers to specific factual ques-tions on arbitrary topics.

Queries are not Open-Domain Question Answering from Large Text Collections book of keywords, but rather full sentences. Better than an index, and much better than a keyword search, are the high-precision computerized question-answering systems explored in this book.

Marius Pasca presents novel and robust methods for capturing the semantics of natural language questions and for finding the most relevant portions of by: The book Open-Domain Question Answering from Large Text Collections, Marius Pasca is published by Center for the Study of Language and Information.

Open-domain question answering has recently emerged as a new field aimed at the extraction of brief, relevant answers from large text collections in response to written questions submitted by users.

Individual related fields–such as natural language processing or information retrieval–do not allow for practicable solutions to open-domain question answering. Books; Journals; Reference Works; Topics; More Information. Quarterly (March, June, September, December) pp.

per issue. 6 3/4 x ISSN. E-ISSN. Open-Domain Question Answering from Large Text Collections. Article PDF ( KB) John Fry. SRI International. US One Rogers Street Cambridge, MA UKCited by: The architecture of the virtual player consists of 1) a Question Answering (QA) module, which leverages Wikipedia and DBpedia datasources to retrieve the most relevant passages of text useful to.

Home Browse by Title Theses High-performance, open-domain question answering from large text collections High-performance, open-domain question answering from large text collections.

Finding Answers to Questions, in Text Collections or Web, in Open Domain or Specialty Domains: /ch This chapter is dedicated to factual question answering, i.e., extracting precise and exact answers to question given in natural language from texts.

AAuthor: Brigitte Grau. Open Domain Question Answering Observations: • In a large text database (e.g., the Web) – Some types of answers are relatively easy to find» Stereotypical text patterns» Repetition • Architecture for a simple open-domain Q/A system – Analyze the question, produce an IR query –.

to combine the challenges of both large-scale open-domain QA and of machine comprehension of text. In order to answer any question, one must rst retrieve the few relevant articles among more than 5 million items, and then scan them care-fully to identify the answer.

We term this setting, machine reading at scale (MRS). Our work treats. ISBN: OCLC Number: Description: pages: illustrations ; 24 cm. Contents: Introduction --An approach to open-domain question answering --Question processing --Semantic constraints --Answer type determination --Passage retrieval --Answer Title.

Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text EMNLP • OceanskySun/GraftNet In this paper we look at a more practical setting, namely QA over the combination of a KB and entity-linked text, which is appropriate when an incomplete KB is available with a large text.

Open-Domain Question–Answering John Prager IBM T.J. Watson Research Center, 1S-D56, P.O. BoxYorktown Heights, NYUSA, [email protected] Abstract The top-performing Question–Answering (QA) systems have been of two types: consistent, solid, well-established and multi-faceted systems.

Using this procedure, a collection of questions and answers can be automatically generated from any text corpus. One key benefit of this automatic procedure is that question/answer sets can be easily generated from domain-specific corpora, creating training data which could be used to build a Q/A system tailored for a specific by: 4.

for open-domain question answering (Miller et al., Most of this work was done while DC was with Face-book AI Research.

), Wikipedia contains up-to-date knowledge that humans are interested in. It is designed, how-ever, for humans – not machines – to read. Using Wikipedia articles as the knowledge source causes the task of question. Advances in Open Domain Question Answering (Text, Speech and Language Technology) th Edition by Tomek Strzalkowski (Editor), Sanda Harabagiu (Editor) ISBN ISBN Why is ISBN important.

ISBN. This bar-code number lets you verify that you're getting exactly the right version or edition of a book. Format: Hardcover. erence also provides the context in which Open-Domain Question Answering from Large Text Collections is to be seen (Voorhees and Tice, ).

The author of the book, Marius Pasca, is the former R&D director at the Language Computer Corporation (LCC), a company which is a spin-off from the question answering research group at the Southern Methodist University (SMU) in Dallas, Texas.

Open-domain Question Answering DuReader. DuReader is a large-scale, open-domain Chinese machine reading comprehension (MRC) dataset, designed to address real-world MRC. Link to paper. DuReader has three advantages over other MRC datasets: (1) data sources: questions and documents are based on Baidu Search and Baidu Zhidao; answers are manually.

Open-domain question answering focuses on using diverse information resources to answer any types of question. Recent years, with the development of large-scale data set and various deep neural.

Abstract: This report describes a new open-domain answer retrieval system developed at the University of Edinburgh and gives results for the TREC question answering track.

Phrasal answers are identified by increasingly narrowing down the search space from a large text collection. Open-domain question answering. The goal of open-domain QA is to answer a question from a large collection of documents.

The annual eval-uations at the Text REtreival Conference (TREC) (Voorhees and Tice, ) led to many advances in open-domain QA, many of which were used in IBM Watson for Jeopardy!

(Ferrucci et al., ).File Size: KB. DrQA is a system for reading comprehension applied to open-domain question answering. In particular, DrQA is targeted at the task of "machine reading at scale" (MRS). In this setting, we are searching for an answer to a question in a potentially very large corpus of unstructured documents (that may not be redundant).erence also provides the context in which Open-Domain Question Answering from Large Text Collections is to be seen (Voorhees and Tice, ).

The author of the book, Marius Pasca, is the former R&D director at the Language Computer Corporation (LCC), a company which is a spin-off from the question answering research group at the Southern Author: Book Review Open-Domain.