7,286 research outputs found
A Conceptual Representation of Documents and Queries for Information Retrieval Systems by Using Light Ontologies
International audienceThis article presents a vector space model approach to representing documents and queries, based on concepts instead of terms and using WordNet as a light ontology. Such representation reduces information overlap with respect to classic semantic expansion techniques. Experiments carried out on the MuchMore benchmark and on the TREC-7 and TREC-8 Ad-hoc collections demonstrate the effectiveness of the proposed approach
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Local search: A guide for the information retrieval practitioner
There are a number of combinatorial optimisation problems in information retrieval in which the use of local search methods are worthwhile. The purpose of this paper is to show how local search can be used to solve some well known tasks in information retrieval (IR), how previous research in the field is piecemeal, bereft of a structure and methodologically flawed, and to suggest more rigorous ways of applying local search methods to solve IR problems. We provide a query based taxonomy for analysing the use of local search in IR tasks and an overview of issues such as fitness functions, statistical significance and test collections when conducting experiments on combinatorial optimisation problems. The paper gives a guide on the pitfalls and problems for IR practitioners who wish to use local search to solve their research issues, and gives practical advice on the use of such methods. The query based taxonomy is a novel structure which can be used by the IR practitioner in order to examine the use of local search in IR
CoMMA Corporate Memory Management through Agents Corporate Memory Management through Agents: The CoMMA project final report
This document is the final report of the CoMMA project. It gives an overview of the different search activities that have been achieved through the project. First, a description of the general requirements is proposed through the definition of two scenarios. Then it shows the different technical aspects of the projects and the solution that has been proposed and implemented
CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning
To accelerate software development, much research has been performed to help
people understand and reuse the huge amount of available code resources. Two
important tasks have been widely studied: code retrieval, which aims to
retrieve code snippets relevant to a given natural language query from a code
base, and code annotation, where the goal is to annotate a code snippet with a
natural language description. Despite their advancement in recent years, the
two tasks are mostly explored separately. In this work, we investigate a novel
perspective of Code annotation for Code retrieval (hence called `CoaCor'),
where a code annotation model is trained to generate a natural language
annotation that can represent the semantic meaning of a given code snippet and
can be leveraged by a code retrieval model to better distinguish relevant code
snippets from others. To this end, we propose an effective framework based on
reinforcement learning, which explicitly encourages the code annotation model
to generate annotations that can be used for the retrieval task. Through
extensive experiments, we show that code annotations generated by our framework
are much more detailed and more useful for code retrieval, and they can further
improve the performance of existing code retrieval models significantly.Comment: 10 pages, 2 figures. Accepted by The Web Conference (WWW) 201
From Frequency to Meaning: Vector Space Models of Semantics
Computers understand very little of the meaning of human language. This
profoundly limits our ability to give instructions to computers, the ability of
computers to explain their actions to us, and the ability of computers to
analyse and process text. Vector space models (VSMs) of semantics are beginning
to address these limits. This paper surveys the use of VSMs for semantic
processing of text. We organize the literature on VSMs according to the
structure of the matrix in a VSM. There are currently three broad classes of
VSMs, based on term-document, word-context, and pair-pattern matrices, yielding
three classes of applications. We survey a broad range of applications in these
three categories and we take a detailed look at a specific open source project
in each category. Our goal in this survey is to show the breadth of
applications of VSMs for semantics, to provide a new perspective on VSMs for
those who are already familiar with the area, and to provide pointers into the
literature for those who are less familiar with the field
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