315 research outputs found
Accurate user directed summarization from existing tools
This paper describes a set of experimental
results produced from the TIPSTER
SUMMAC initiative on user directed
summaries: document summaries generated in
the context of an information need expressed
as a query. The summarizer that was
evaluated was based on a set of existing
statistical techniques that had been applied
successfully to the INQUERY retrieval system.
The techniques proved to have a wider utility,
however, as the summarizer was one of the
better performing systems in the SUMMAC
evaluation. The design of this summarizer is
presented with a range of evaluations: both
those provided by SUMMAC as well as a set of
preliminary, more informal, evaluations that
examined additional aspects of the summaries.
Amongst other conclusions, the results reveal
that users can judge the relevance of
documents from their summary almost as
accurately as if they had had access to the
document’s full text
University of Sheffield TREC-8 Q & A System
The system entered by the University of Sheffield in the question answering track of TREC-8 is the result of coupling two existing technologies - information retrieval (IR) and information extraction (IE). In essence the approach is this: the IR system treats the question as a query and returns a set of top ranked documents or passages; the IE system uses NLP techniques to parse the question, analyse the top ranked documents or passages returned by the IR system, and instantiate a query variable in the semantic representation of the question against the semantic representation of the analysed documents or passages. Thus, while the IE system by no means attempts “full text understanding", this approach is a relatively deep approach which attempts to work with meaning representations.
Since the information retrieval systems we used were not our own (AT&T and UMass) and were used more or less “off the shelf", this paper concentrates on describing the modifications made to our existing information extraction system to allow it to participate in the Q & A task
The Mirror DBMS at TREC-8
The database group at University of Twente participates in TREC8 using the Mirror DBMS, a prototype database system especially designed for multimedia and web retrieval. From a database perspective, the purpose has been to check whether we can get sufficient performance, and to prepare for the very large corpus track in which we plan to participate next year. From an IR perspective, the experiments have been designed to learn more about the effect of the global statistics on the ranking
Investigating cross-language speech retrieval for a spontaneous conversational speech collection
Cross-language retrieval of spontaneous speech combines the challenges of working with noisy automated transcription and language translation. The CLEF 2005 Cross-Language Speech Retrieval (CL-SR) task provides a standard test collection to investigate these challenges. We show that we can improve retrieval performance: by careful selection of the term weighting scheme; by decomposing automated transcripts into
phonetic substrings to help ameliorate transcription
errors; and by combining automatic transcriptions with manually-assigned metadata. We further show that topic translation with online machine translation resources
yields effective CL-SR
Probabilistic models of information retrieval based on measuring the divergence from randomness
We introduce and create a framework for deriving probabilistic models of Information Retrieval. The models are nonparametric models of IR obtained in the language model approach. We derive term-weighting models by measuring the divergence of the actual term distribution from that obtained under a random process. Among the random processes we study the binomial distribution and Bose--Einstein statistics. We define two types of term frequency normalization for tuning term weights in the document--query matching process. The first normalization assumes that documents have the same length and measures the information gain with the observed term once it has been accepted as a good descriptor of the observed document. The second normalization is related to the document length and to other statistics. These two normalization methods are applied to the basic models in succession to obtain weighting formulae. Results show that our framework produces different nonparametric models forming baseline alternatives to the standard tf-idf model
Evaluation of a Bayesian inference network for ligand-based virtual screening
Background
Bayesian inference networks enable the computation of the probability that an event will occur. They have been used previously to rank textual documents in order of decreasing relevance to a user-defined query. Here, we modify the approach to enable a Bayesian inference network to be used for chemical similarity searching, where a database is ranked in order of decreasing probability of bioactivity.
Results
Bayesian inference networks were implemented using two different types of network and four different types of belief function. Experiments with the MDDR and WOMBAT databases show that a Bayesian inference network can be used to provide effective ligand-based screening, especially when the active molecules being sought have a high degree of structural homogeneity; in such cases, the network substantially out-performs a conventional, Tanimoto-based similarity searching system. However, the effectiveness of the network is much less when structurally heterogeneous sets of actives are being sought.
Conclusion
A Bayesian inference network provides an interesting alternative to existing tools for ligand-based virtual screening
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
Search of spoken documents retrieves well recognized transcripts
This paper presents a series of analyses and experiments on spoken
document retrieval systems: search engines that retrieve transcripts produced by
speech recognizers. Results show that transcripts that match queries well tend to
be recognized more accurately than transcripts that match a query less well.
This result was described in past literature, however, no study or explanation of
the effect has been provided until now. This paper provides such an analysis
showing a relationship between word error rate and query length. The paper
expands on past research by increasing the number of recognitions systems that
are tested as well as showing the effect in an operational speech retrieval
system. Potential future lines of enquiry are also described
The relationship of word error rate to document ranking
This paper describes two experiments that examine the relationship of Word Error Rate (WER) of retrieved
spoken documents returned by a spoken document retrieval system. Previous work has demonstrated that
recognition errors do not significantly affect retrieval effectiveness but whether they will adversely affect
relevance judgement remains unclear. A user-based experiment measuring ability to judge relevance from
the recognised text presented in a retrieved result list was conducted. The results indicated that users were
capable of judging relevance accurately despite transcription errors. This lead an examination of the
relationship of WER in retrieved audio documents to their rank position when retrieved for a particular
query. Here it was shown that WER was somewhat lower for top ranked documents than it was for
documents retrieved further down the ranking, thereby indicating a possible explanation for the success of
the user experiment
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