704 research outputs found
Turkish factoid question answering using answer pattern matching
Ankara : The Department of Computer Engineering and Information Science and the Institute of Engineering and Science of Bilkent University, 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references.Efficiently locating information on the Web has become one of the most important
challenges in the last decade. The Web Search Engines have been used to
locate the documents containing the required information. However, in many situations
a user wants a particular piece of information rather than a document set.
Question Answering (QA) systems have addressed this problem and they return
explicit answers to questions rather than set of documents. Questions addressed
by QA systems can be categorized into five categories: factoid, list, definition,
complex, and speculative questions. A factoid question has exactly one correct
answer, and the answer is mostly a named entity like person, date, or location.
In this thesis, we develop a pattern matching approach for a Turkish Factoid QA
system. In TREC-10 QA track, most of the question answering systems used
sophisticated linguistic tools. However, the best performing system at the track
used only an extensive list of surface patterns; therefore, we decided to investigate
the potential of answer pattern matching approach for our Turkish Factoid QA
system. We try different methods for answer pattern extraction such as stemming
and named entity tagging. We also investigate query expansion by using answer
patterns. Several experiments have been performed to evaluate the performance
of the system. Compared with the results of the other factoid QA systems, our
methods have achieved good results. The results of the experiments show that
named entity tagging improves the performance of the system.Er, Nagehan PalaM.S
Finding Structured and Unstructured Features to Improve the Search Result of Complex Question
-Recently, search engine got challenge deal with such a natural language questions.
Sometimes, these questions are complex questions. A complex question is a question that
consists several clauses, several intentions or need long answer.
In this work we proposed that finding structured features and unstructured features of
questions and using structured data and unstructured data could improve the search result
of complex questions. According to those, we will use two approaches, IR approach and
structured retrieval, QA template.
Our framework consists of three parts. Question analysis, Resource Discovery and
Analysis The Relevant Answer. In Question Analysis we used a few assumptions, and
tried to find structured and unstructured features of the questions. Structured feature
refers to Structured data and unstructured feature refers to unstructured data. In the
resource discovery we integrated structured data (relational database) and unstructured
data (webpage) to take the advantaged of two kinds of data to improve and reach the
relevant answer. We will find the best top fragments from context of the webpage In the
Relevant Answer part, we made a score matching between the result from structured data
and unstructured data, then finally used QA template to reformulate the question.
In the experiment result, it shows that using structured feature and unstructured
feature and using both structured and unstructured data, using approach IR and QA
template could improve the search result of complex questions
Dublin City University at QA@CLEF 2008
We describe our participation in Multilingual Question Answering at CLEF 2008 using German and English as our source and target languages respectively. The system was built using UIMA (Unstructured Information Management Architecture) as underlying framework
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