Intelligent indexing of crime scene photographs

The Scene of Crime Information System's automatic image-indexing prototype goes beyond extracting keywords and syntactic relations from captions. The semantic information it gathers gives investigators an intuitive, accurate way to search a database of cases for specific photographic evidence. Intelligent, automatic indexing and retrieval of crime scene photographs is one of the main functions of SOCIS, our research prototype developed within the Scene of Crime Information System project. The prototype, now in its final development and evaluation phase, applies advanced natural language processing techniques to text-based image indexing and retrieval to tackle crime investigation needs effectively and efficiently

An analysis of some graph theoretical cluster techniques

Graph theoretic cluster techniques for automatic generation of information retrieval systems thesaur

Embedding Web-based Statistical Translation Models in Cross-Language Information Retrieval

Although more and more language pairs are covered by machine translation services, there are still many pairs that lack translation resources. Cross-language information retrieval (CLIR) is an application which needs translation functionality of a relatively low level of sophistication since current models for information retrieval (IR) are still based on a bag-of-words. The Web provides a vast resource for the automatic construction of parallel corpora which can be used to train statistical translation models automatically. The resulting translation models can be embedded in several ways in a retrieval model. In this paper, we will investigate the problem of automatically mining parallel texts from the Web and different ways of integrating the translation models within the retrieval process. Our experiments on standard test collections for CLIR show that the Web-based translation models can surpass commercial MT systems in CLIR tasks. These results open the perspective of constructing a fully automatic query translation device for CLIR at a very low cost.Comment: 37 page

Large scale evaluations of multimedia information retrieval: the TRECVid experience

Information Retrieval is a supporting technique which underpins a broad range of content-based applications including retrieval, filtering, summarisation, browsing, classification, clustering, automatic linking, and others. Multimedia information retrieval (MMIR) represents those applications when applied to multimedia information such as image, video, music, etc. In this presentation and extended abstract we are primarily concerned with MMIR as applied to information in digital video format. We begin with a brief overview of large scale evaluations of IR tasks in areas such as text, image and music, just to illustrate that this phenomenon is not just restricted to MMIR on video. The main contribution, however, is a set of pointers and a summarisation of the work done as part of TRECVid, the annual benchmarking exercise for video retrieval tasks

Retrieval of bilingual Spanish-English information by means of a standard automatic translation system

This paper describes our participation in bilingual retrieval (queries in Spanish on documents in English), by means of an information retrieval system based on the vector model. The queries, formulated in Spanish, were translated into English by means of a commercial automatic translation system; the terms extracted from the resulting translations were filtered in order to get rid of empty words and then they were normalised by stemming. Results are poorer than those obtained through monolingual retrieval with the original queries in English slightly above 15%

Experiences in Automatic Keywording of Particle Physics Literature

Attributing keywords can assist in the classification and retrieval of documents in the particle physics literature. As information services face a future with less available manpower and more and more documents being written, the possibility of keyword attribution being assisted by automatic classification software is explored. A project being carried out at CERN (the European Laboratory for Particle Physics) for the development and integration of automatic keywording is described

Examining assessor attributes at HARD 2005

The TREC HARD (High accuracy Retrieval from Documents) track was motivated to investigate techniques for personalised retrieval of documents. Through the use of a limited dialogue with the TREC assessors, the track facilitated the gathering and exploitation of information about the assessors' personal search context (e.g. knowledge of search topic) which could be used to improve document retrieval. In this paper we describe experiments, run within the context of the 2005 HARD track, which indicate that assessor attributes such as familiarity, interest and confidence when searching a topic can help determine when the utilisation of automatic query expansion improves retrieval over the original document ranking

Dublin City University at CLEF 2004: experiments with the ImageCLEF St Andrew's collection

For the CLEF 2004 ImageCLEF St Andrew's Collection task the Dublin City University group carried out three sets of experiments: standard cross-language information retrieval (CLIR) runs using topic translation via machine translation (MT), combination of this run with image matching results from the VIPER system, and a novel document rescoring approach based on automatic MT evaluation metrics. Our standard MT-based CLIR works well on this task. Encouragingly combination with image matching lists is also observed to produce small positive changes in the retrieval output. However, rescoring using the MT evaluation metrics in their current form significantly reduced retrieval effectiveness

Automatic Genre Classification in Web Pages Applied to Web Comments

Automatic Web comment detection could significantly facilitate information retrieval systems, e.g., a focused Web crawler. In this paper, we propose a text genre classifier for Web text segments as intermediate step for Web comment detection in Web pages. Different feature types and classifiers are analyzed for this purpose. We compare the two-level approach to state-of-the-art techniques operating on the whole Web page text and show that accuracy can be improved significantly. Finally, we illustrate the applicability for information retrieval systems by evaluating our approach on Web pages achieved by a Web crawler