1,213 research outputs found
The State-of-the-arts in Focused Search
The continuous influx of various text data on the Web requires search engines to improve their retrieval abilities for more specific information. The need for relevant results to a user’s topic of interest has gone beyond search for domain or type specific documents to more focused result (e.g. document fragments or answers to a query). The introduction of XML provides a format standard for data representation, storage, and exchange. It helps focused search to be carried out at different granularities of a structured document with XML markups. This report aims at reviewing the state-of-the-arts in focused search, particularly techniques for topic-specific document retrieval, passage retrieval, XML retrieval, and entity ranking. It is concluded with highlight of open problems
DCU and ISI@INEX 2010: Ad-hoc and data-centric tracks
We describe the participation of Dublin City University (DCU)and the Indian Statistical Institute (ISI) in INEX 2010. The main contributions of this paper are: i) a simplified version of Hierarchical Language Model (HLM) which involves scoring XML elements with a combined probability of generating the given query from itself and the top level article node, is shown to outperform the baselines of Language Model (LM) and Vector Space Model (VSM) scoring of XML elements; ii) the Expectation Maximization (EM) feedback in LM is shown to be the most effective on the domain specic collection of IMDB; iii) automated removal of sentences indicating aspects of irrelevance from the narratives
of INEX ad-hoc topics is shown to improve retrieval eectiveness
Enhanced information retrieval using domain-specific recommender models
The objective of an information retrieval (IR) system is to retrieve relevant items which meet a user information need. There is currently significant interest in personalized IR which seeks to improve IR effectiveness by incorporating a model of the user’s interests. However, in some situations
there may be no opportunity to learn about the interests of a specific user on a certain topic. In our work, we propose an IR approach which combines a recommender algorithm with IR methods to improve retrieval for domains where the system has no opportunity to learn prior information about the user’s knowledge of a domain for which they have not previously entered a query. We use search data from other previous users interested in the same topic to build a
recommender model for this topic. When a user enters a query on a topic, new to this user, an appropriate recommender model is selected and used to predict a ranking which the user may find interesting based on the behaviour of previous
users with similar queries. The recommender output is integrated with a standard IR method in a weighted linear combination to provide a final result for the user. Experiments using the INEX 2009 data collection with a simulated recommender training set show that our approach can improve on a baseline IR system
Exploiting Query Structure and Document Structure to Improve Document Retrieval Effectiveness
In this paper we present a systematic analysis of document
retrieval using unstructured and structured queries within
the score region algebra (SRA) structured retrieval framework. The behavior of di®erent retrieval models, namely
Boolean, tf.idf, GPX, language models, and Okapi, is tested
using the transparent SRA framework in our three-level structured retrieval system called TIJAH. The retrieval models are implemented along four elementary retrieval aspects: element and term selection, element score computation, score combination, and score propagation.
The analysis is performed on a numerous experiments
evaluated on TREC and CLEF collections, using manually
generated unstructured and structured queries. Unstructured queries range from the short title queries to long title
+ description + narrative queries. For generating structured
queries we exploit the knowledge of the document structure
and the content used to semantically describe or classify
documents. We show that such structured information can
be utilized in retrieval engines to give more precise answers to user queries then when using unstructured queries
Entity Query Feature Expansion Using Knowledge Base Links
Recent advances in automatic entity linking and knowledge base
construction have resulted in entity annotations for document and
query collections. For example, annotations of entities from large
general purpose knowledge bases, such as Freebase and the Google
Knowledge Graph. Understanding how to leverage these entity
annotations of text to improve ad hoc document retrieval is an open
research area. Query expansion is a commonly used technique to
improve retrieval effectiveness. Most previous query expansion
approaches focus on text, mainly using unigram concepts. In this
paper, we propose a new technique, called entity query feature
expansion (EQFE) which enriches the query with features from
entities and their links to knowledge bases, including structured
attributes and text. We experiment using both explicit query entity
annotations and latent entities. We evaluate our technique on TREC
text collections automatically annotated with knowledge base entity
links, including the Google Freebase Annotations (FACC1) data.
We find that entity-based feature expansion results in significant
improvements in retrieval effectiveness over state-of-the-art text
expansion approaches
PFTijah: text search in an XML database system
This paper introduces the PFTijah system, a text search system that is integrated with an XML/XQuery database management system. We present examples of its use, we explain some of the system internals, and discuss plans for future work. PFTijah is part of the open source release of MonetDB/XQuery
Document expansion for image retrieval
Successful information retrieval requires e�ective matching
between the user's search request and the contents of relevant
documents. Often the request entered by a user may
not use the same topic relevant terms as the authors' of the
documents. One potential approach to address problems
of query-document term mismatch is document expansion
to include additional topically relevant indexing terms in a
document which may encourage its retrieval when relevant
to queries which do not match its original contents well. We
propose and evaluate a new document expansion method
using external resources. While results of previous research
have been inconclusive in determining the impact of document
expansion on retrieval e�ectiveness, our method is
shown to work e�ectively for text-based image retrieval of
short image annotation documents. Our approach uses the
Okapi query expansion algorithm as a method for document
expansion. We further show improved performance can be
achieved by using a \document reduction" approach to include
only the signi�cant terms in a document in the expansion
process. Our experiments on the WikipediaMM task at
ImageCLEF 2008 show an increase of 16.5% in mean average
precision (MAP) compared to a variation of Okapi BM25 retrieval
model. To compare document expansion with query
expansion, we also test query expansion from an external resource
which leads an improvement by 9.84% in MAP over
our baseline. Our conclusion is that the document expansion
with document reduction and in combination with query expansion
produces the overall best retrieval results for shortlength
document retrieval. For this image retrieval task, we
also concluded that query expansion from external resource
does not outperform the document expansion method
Structural features in content oriented XML retrieval
The structural features of XML components are an extra source of information that should be used in a content-oriented retrieval task on this type of documents. This paper explores three different structural features from the INEX collection that could be used in content-oriented search. We analyse the gain this knowledge could add to the performance of an information retrieval system, and present a first approach on how this structural information could be extracted from a relevance feedback process to be used as priors in a language modelling framewor
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