Terrier Information Retrieval Platform

Ounis, Iadh; Amati, Giambattista; Plachouras, Vassilis; He, Ben; Macdonald, Craig; Johnson, Douglas A.

doi:10.1007/978-3-540-31865-1_37

Cited by 246 publications

(100 citation statements)

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“…For the retrieval part, the SMART [2,11] IR system and the Terrier [1,9] IR system were tested with many different weighting schemes for indexing the collection and the queries.…”

Section: Introductionmentioning

confidence: 99%

“…It is based on Divergence from Randomness models (DFR) where IR is seen as a probabilistic process [1,9]. We experimented with the In(exp)C2 weighting model, one of Terrier's DFRbased document weighting models.…”

Section: Introductionmentioning

confidence: 99%

“…In our experiments, we applied the Kullback-Leibler (KL) model for query expansion [3,9]. Table 1 shows the results of the submitted results on the test data (33 queries).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Experiments for the Cross Language Speech Retrieval Task at CLEF 2006

Alzghool

Inkpen

2007

Evaluation of Multilingual and Multi-Modal Information Retrieval

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Abstract. This paper presents the second participation of the University of Ottawa group in the Cross-Language Speech Retrieval (CL-SR) task at CLEF 2006. We present the results of the submitted runs for the English collection and very briefly for the Czech collection, followed by many additional experiments. We have used two Information Retrieval systems in our experiments: SMART and Terrier, with several query expansion techniques (including a new method based on log-likelihood scores for collocations). Our experiments showed that query expansion methods do not help much for this collection. We tested different Automatic Speech Recognition transcripts and combinations. The retrieval results did not improve, probably because the speech recognition errors happened for the words that are important in retrieval. We present cross-language experiments, where the queries are automatically translated by combining the results of several online machine translation tools. Our experiments showed that high quality automatic translations (for French) led to results comparable with monolingual English, while the performance decreased for the other languages. Experiments on indexing the manual summaries and keywords gave the best retrieval results.

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“…For the retrieval part, the SMART [2,11] IR system and the Terrier [1,9] IR system were tested with many different weighting schemes for indexing the collection and the queries.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Experiments for the Cross Language Speech Retrieval Task at CLEF 2006

Alzghool

Inkpen

2007

Evaluation of Multilingual and Multi-Modal Information Retrieval

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“…Besides, external sources such as WordNet [2] can be used to model correlation of the text. The Terrier Retrieval System [6] can be used for indexing the subtitles. Terrier is a Java based framework for the rapid development of large-scale information retrieval applications and provides indexing and retrieval functionalities.…”

Section: Capturing and Indexing Processmentioning

confidence: 99%

A News Video Retrieval Framework for the Study of Implicit Relevance Feedback

Hopfgartner¹

2007

Second International Workshop on Semantic Media Adaptation and Personalization (SMAP 2007)

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In this paper, we propose a framework for recording, analysing, indexing and retrieving news videos such as the BBC One O'Clock News. We believe that such a framework will be useful to identify implicit indicators of relevance, a nearly untouched area in adaptive multimedia retrieval. Due to its advantages as a web application and its up-todate content, it can be a promising approach to motivate a broad quantity of users to interact with the system.

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“…Depending on the method used to identify the language of documents and queries, we test various approaches for performing stemming in a robust and appropriate way on the tested multilingual setting. We use our Terrier IR platform [10] to conduct all the experiments.…”

Section: Introductionmentioning

confidence: 99%

University of Glasgow at WebCLEF 2005: Experiments in Per-Field Normalisation and Language Specific Stemming

Macdonald

Plachouras

et al. 2006

Accessing Multilingual Information Repositories

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Abstract. We participated in the WebCLEF 2005 monolingual task. In this task, a search system aims to retrieve relevant documents from a multilingual corpus of Web documents from Web sites of European governments. Both the documents and the queries are written in a wide range of European languages. A challenge in this setting is to detect the language of documents and topics, and to process them appropriately. We develop a language specific technique for applying the correct stemming approach, as well as for removing the correct stopwords from the queries. We represent documents using three fields, namely content, title, and anchor text of incoming hyperlinks. We use a technique called per-field normalisation, which extends the Divergence From Randomness (DFR) framework, to normalise the term frequencies, and to combine them across the three fields. We also employ the length of the URL path of Web documents. The ranking is based on combinations of both the language specific stemming, if applied, and the per-field normalisation. We use our Terrier platform for all our experiments. The overall performance of our techniques is outstanding, achieving the overall top four performing runs, as well as the top performing run without metadata in the monolingual task. The best run only uses per-field normalisation, without applying stemming.

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Terrier Information Retrieval Platform

Cited by 246 publications

References 0 publications

Experiments for the Cross Language Speech Retrieval Task at CLEF 2006

Experiments for the Cross Language Speech Retrieval Task at CLEF 2006

A News Video Retrieval Framework for the Study of Implicit Relevance Feedback

University of Glasgow at WebCLEF 2005: Experiments in Per-Field Normalisation and Language Specific Stemming

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