2,974 research outputs found
Two selfless contributions to web search evaluation
We present our results for the Web Search track and the Federated Web Search track for the 23rd Text Retrieval Conference TREC
Ontology-Based MEDLINE Document Classification
An increasing and overwhelming amount of biomedical information is available in the research literature mainly in the form of free-text. Biologists need tools that automate their information search and deal with the high volume and ambiguity of free-text. Ontologies can help automatic information processing by providing standard concepts and information about the relationships between concepts. The Medical Subject Headings (MeSH) ontology is already available and used by MEDLINE indexers to annotate the conceptual content of biomedical articles. This paper presents a domain-independent method that uses the MeSH ontology inter-concept relationships to extend the existing MeSH-based representation of MEDLINE documents. The extension method is evaluated within a document triage task organized by the Genomics track of the 2005 Text REtrieval Conference (TREC). Our method for extending the representation of documents leads to an improvement of 17% over a non-extended baseline in terms of normalized utility, the metric defined for the task. The SVMlight software is used to classify documents
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
Web Page Retrieval by Combining Evidence
The participation of the REINA Research Group in WebCLEF 2005 focused in the monolingual mixed task. Queries or topics are of two types: named and home pages. For both, we first perform a search by thematic contents; for the same query, we do a search in several elements of information from every page (title, some meta tags, anchor text) and then we combine the results. For queries about home pages, we try to detect using a method based in some keywords and their patterns of use. After, a re-rank of the results of the thematic contents retrieval is performed, based on Page-Rank and Centrality coeficients
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
Dublin City University at the TREC 2006 terabyte track
For the 2006 Terabyte track in TREC, Dublin City Universityās participation was focussed on the ad hoc search task. As per the pervious two years [7, 4], our experiments on the Terabyte track have concentrated on the evaluation of a sorted inverted index, the aim of which is to sort the postings within each posting list in such a way, that allows only a limited number of postings to be processed from each list, while at the same time minimising the loss of effectiveness in terms of query precision. This is done using the FĆsrĆ©al search system, developed at Dublin City University [4, 8]
Teaching a New Dog Old Tricks: Resurrecting Multilingual Retrieval Using Zero-shot Learning
While billions of non-English speaking users rely on search engines every
day, the problem of ad-hoc information retrieval is rarely studied for
non-English languages. This is primarily due to a lack of data set that are
suitable to train ranking algorithms. In this paper, we tackle the lack of data
by leveraging pre-trained multilingual language models to transfer a retrieval
system trained on English collections to non-English queries and documents. Our
model is evaluated in a zero-shot setting, meaning that we use them to predict
relevance scores for query-document pairs in languages never seen during
training. Our results show that the proposed approach can significantly
outperform unsupervised retrieval techniques for Arabic, Chinese Mandarin, and
Spanish. We also show that augmenting the English training collection with some
examples from the target language can sometimes improve performance.Comment: ECIR 2020 (short
Finding Support Documents with a Logistic Regression Approach
Entity retrieval finds the relevant results for a userās information needs at a finer unit called āentityā. To retrieve such entity, people usually first locate a small set of support documents which contain answer entities, and then further detect the answer entities in this set. In the literature, people view the support documents as relevant documents, and their findings as a conventional document retrieval problem. In this paper, we will state that finding support documents and that of relevant documents, although sounds similar, have important differences. Further, we propose a logistic regression approach to find support documents. Our experiment results show that the logistic regression method performs significantly better than a baseline system that treat the support document finding as a conventional document retrieval problem
EveTAR: Building a Large-Scale Multi-Task Test Collection over Arabic Tweets
This article introduces a new language-independent approach for creating a
large-scale high-quality test collection of tweets that supports multiple
information retrieval (IR) tasks without running a shared-task campaign. The
adopted approach (demonstrated over Arabic tweets) designs the collection
around significant (i.e., popular) events, which enables the development of
topics that represent frequent information needs of Twitter users for which
rich content exists. That inherently facilitates the support of multiple tasks
that generally revolve around events, namely event detection, ad-hoc search,
timeline generation, and real-time summarization. The key highlights of the
approach include diversifying the judgment pool via interactive search and
multiple manually-crafted queries per topic, collecting high-quality
annotations via crowd-workers for relevancy and in-house annotators for
novelty, filtering out low-agreement topics and inaccessible tweets, and
providing multiple subsets of the collection for better availability. Applying
our methodology on Arabic tweets resulted in EveTAR , the first
freely-available tweet test collection for multiple IR tasks. EveTAR includes a
crawl of 355M Arabic tweets and covers 50 significant events for which about
62K tweets were judged with substantial average inter-annotator agreement
(Kappa value of 0.71). We demonstrate the usability of EveTAR by evaluating
existing algorithms in the respective tasks. Results indicate that the new
collection can support reliable ranking of IR systems that is comparable to
similar TREC collections, while providing strong baseline results for future
studies over Arabic tweets
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