Keyword extraction and clustering for document recommendation in conversations.

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This document discusses a proposed system for keyword extraction and clustering from conversations to recommend relevant documents. The existing systems rely on explicit search queries, but this system aims to construct implicit queries from conversational input containing more words. It proposes algorithms to extract diverse keywords using topic modeling and cluster them into topic-specific queries. The recommendations generated using these queries are evaluated against conversation fragments from corpora, showing improved relevance over frequency-based methods.

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KEYWORD EXTRACTION AND CLUSTERING FOR
DOCUMENT RECOMMENDATION IN CONVERSATIONS
ABSTRACT
This project addresses the problem of keyword extraction from
conversations, with the goal of using these keywords to retrieve, for each short
conversation fragment, a small number of potentially relevant documents, which
can be recommended to participants. However, even a short fragment contains a
variety of words, which are potentially related to several topics; moreover, using
an automatic speech recognition (ASR) system introduces errors among them.
Therefore, it is difficult to infer precisely the information needs of the conversation
participants. We first propose an algorithm to extract keywords from the output of
an ASR system (or a manual transcript for testing), which makes use of topic
modeling techniques and of a sub modular reward function which favors diversity
in the keyword set, to match the potential diversity of topics and reduce ASR
noise. Then, we propose a method to derive multiple topically separated queries
from this keyword set, in order to maximize the chances of making at least one
relevant recommendation when using these queries to search over the English
Wikipedia.
The proposed methods are evaluated in terms of relevance with respect to
conversation fragments from the Fisher, AMI, and ELEA conversational corpora,
rated by several human judges. The scores show that our proposal improves over
previous methods that consider only word frequency or topic similarity, and
represents a promising solution for a document recommender system to be used in
conversations.

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Pondicherry-605 005.
Call: 0413-4205444, +91 9566355386, 99625 88976.
Web : www.lemenizinfotech.com / www.ieeemaster.com
Mail : projects@lemenizinfotech.com
EXISTING SYSTEM
An existing system humans are surrounded by an unprecedented wealth of
information, available as documents, databases, or multimedia resources. Access to
this information is conditioned by the availability of suitable search engines.
DRAWBACK OF EXISTING SYSTEM
 Users participate in a meeting, their information needs can be modeled as
implicit queries that are constructed in the background from the pronounced
words, obtained through real-time automatic speech recognition (ASR).
 These implicit queries are used to retrieve and recommend documents from
the Web or a local repository, which users can choose to inspect in more
detail if they find them interesting.
PROPOSED SYSTEM
In proposed system, on formulating implicit queries to a just-in-time-
retrieval system for use in meeting rooms. In contrast to explicit spoken queries
that can be made in commercial Web search engines, our just-in-time-retrieval
system must construct implicit queries from conversational input, which contains a
much larger number of words than a query.
ADVANTAGE OF PROPOSED SYSTEM
 To maintain multiple hypotheses about users information need.
 To present a small sample of recommendations based on the most likely
ones.

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Copyright © 2015 LeMeniz Infotech. All rights reserved
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LeMeniz Infotech
36, 100 Feet Road, Natesan Nagar, Near Indira Gandhi Statue,
Pondicherry-605 005.
Call: 0413-4205444, +91 9566355386, 99625 88976.
Web : www.lemenizinfotech.com / www.ieeemaster.com
Mail : projects@lemenizinfotech.com
 To extracting a relevant and diverse set of keywords, cluster them into topic-
specific queries ranked by importance, and present users a sample of results
from these queries.
SYSTEM SPECIFICATION
Hardware Requirements
System : Pentium IV 2.4 GHz
Hard Disk : 40 GB
Floppy Drive : 1.44 Mb
Monitor : 15 VGA Colour
Mouse : Logitech
Ram : 512 Mb
Software Requirements
Operating system : Windows XP
Technology Used : Microsoft .NET
Backend Used : SQL Server

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