9,163 research outputs found
DCU@FIRE2010: term conflation, blind relevance feedback, and cross-language IR with manual and automatic query translation
For the first participation of Dublin City University (DCU)
in the FIRE 2010 evaluation campaign, information retrieval
(IR) experiments on English, Bengali, Hindi, and Marathi
documents were performed to investigate term conation
(different stemming approaches and indexing word prefixes),
blind relevance feedback, and manual and automatic query
translation. The experiments are based on BM25 and on
language modeling (LM) for IR. Results show that term conation always improves mean average precision (MAP)
compared to indexing unprocessed word forms, but different approaches seem to work best for different languages. For example, in monolingual Marathi experiments indexing 5-prefixes outperforms our corpus-based stemmer; in Hindi,
the corpus-based stemmer achieves a higher MAP. For Bengali, the LM retrieval model achieves a much higher MAP
than BM25 (0.4944 vs. 0.4526). In all experiments using
BM25, blind relevance feedback yields considerably higher
MAP in comparison to experiments without it. Bilingual IR experiments (English!Bengali and English!Hindi) are
based on query translations obtained from native speakers
and the Google translate web service. For the automatically
translated queries, MAP is slightly (but not significantly)
lower compared to experiments with manual query translations. The bilingual English!Bengali (English!Hindi)
experiments achieve 81.7%-83.3% (78.0%-80.6%) of the best
corresponding monolingual experiments
Adaptive Representations for Tracking Breaking News on Twitter
Twitter is often the most up-to-date source for finding and tracking breaking
news stories. Therefore, there is considerable interest in developing filters
for tweet streams in order to track and summarize stories. This is a
non-trivial text analytics task as tweets are short, and standard retrieval
methods often fail as stories evolve over time. In this paper we examine the
effectiveness of adaptive mechanisms for tracking and summarizing breaking news
stories. We evaluate the effectiveness of these mechanisms on a number of
recent news events for which manually curated timelines are available.
Assessments based on ROUGE metrics indicate that an adaptive approaches are
best suited for tracking evolving stories on Twitter.Comment: 8 Pag
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
Exploiting the similarity of non-matching terms at retrieval time
In classic information retrieval systems a relevant document will not be retrieved in response to a query if the document and query representations do not share at least one term. This problem, known as 'term mismatch', has been recognised for a long time by the information retrieval community and a number of possible solutions have been proposed. Here I present a preliminary investigation into a new class of retrieval models that attempt to solve the term mismatch problem by exploiting complete or partial knowledge of term similarity in the term space. The use of term similarity can enhance classic retrieval models by taking into account non-matching terms. The theoretical advantages and drawbacks of these models are presented and compared with other models tackling the same problem. A preliminary experimental investigation into the performance gain achieved by exploiting term similarity with the proposed models is presented and discussed
Video databases annotation enhancing using commonsense knowledgebases for indexing and retrieval
The rapidly increasing amount of video collections, especially on the web, motivated the need for intelligent automated annotation tools for searching, rating, indexing and retrieval purposes. These videos collections contain all types of manually annotated videos. As this annotation is usually incomplete and uncertain and contains misspelling words, search using some keywords almost do retrieve only a portion of videos which actually contains the desired meaning. Hence, this annotation needs filtering, expanding and validating for better indexing and retrieval.
In this paper, we present a novel framework for video annotation enhancement, based on merging two widely known commonsense knowledgebases, namely WordNet and ConceptNet. In addition to that, a comparison between these knowledgebases in video annotation domain is presented.
Experiments were performed on random wide-domain video clips, from the \emph{vimeo.com} website. Results show that searching for a video over enhanced tags, based on our proposed framework, outperforms searching using the original tags. In addition to that, the annotation enhanced by our framework outperforms both those enhanced by WordNet and ConceptNet individually, in terms of tags enrichment ability, concept diversity and most importantly retrieval performance
Glasgow University at TRECVID 2006
In the first part of this paper we describe our experiments in the automatic and interactive search tasks of TRECVID 2006. We submitted five fully automatic runs, including a text baseline, two runs based on visual features, and two runs that combine textual and visual features in a graph model. For the interactive search, we have implemented a new video search interface with relevance feedback facilities, based on both textual and visual features.
The second part is concerned with our approach to the high-level feature extraction task, based on textual information extracted from speech recogniser and machine translation outputs. They were aligned with shots and associated with high-level feature references. A list of significant words was created for each feature, and it was in turn utilised for identification of a feature during the evaluation
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