The workshop on Supporting and Understanding of (multi-party) conversational Dialogues (SUD) seeks to encourage researchers to investigate automated methods to analyze and understand conversations, and also explore methodologies for proactively providing assistance to the communicating parties during conversations, ranging from summarizing the minutes of meetings to automatically keeping track of action items etc. The workshop will have (1) a regular research paper track, and a more focused (2) data challenge track, inviting papers on a specific task of contextualizing entities of interest from conversation dialogues.

This study proposes a new way of using WordNet for query expansion (QE). We choose candidate expansion terms from a set of pseudo‐relevant documents; however, the usefulness of these terms is measured based on their definitions provided in a hand‐crafted lexical resource such as WordNet. Experiments with a number of standard TREC collections WordNet‐based that this method outperforms existing WordNet‐based methods. It also compares favorably with established QE methods such as KLD and RM3. Leveraging earlier work in which a combination of QE methods was found to outperform each individual method (as well as other well‐known QE methods), we next propose a combination‐based QE method that takes into account three different aspects of a candidate expansion term's usefulness: (a) its distribution in the pseudo‐relevant documents and in the target corpus, (b) its statistical association with query terms, and (c) its semantic relation with the query, as determined by the overlap between the WordNet definitions of the term and query terms. This combination of diverse sources of information appears to work well on a number of test collections, viz., TREC123, TREC5, TREC678, TREC robust (new), and TREC910 collections, and yields significant improvements over competing methods on most of these collections.

Automatic query expansion (AQE) is a useful technique for enhancing the effectiveness of information retrieval systems. In this article, we propose a novel AQE algorithm which first adopts a systematic incremental approach to choose feedback documents from the top retrieved set and then selects the expansion terms aggregating the scores from each feedback set. We also devise a term selection measure and a number of weighting schemes based on easily computable features. A set of experiments with a large number of standard test collections reveals that the proposed incremental blind feedback algorithm outperforms a number of state-of-the-art query expansion methods with remarkable significance and consistency.

Stemming is a widely used technique in information retrieval systems to address the vocabulary mismatch problem arising out of morphological phenomena. The major shortcoming of the commonly used stemmers is that they accept the morphological variants of the query words without considering their thematic coherence with the given query, which leads to poor performance. Moreover, for many queries, such approaches also produce retrieval performance that is poorer than no stemming, thereby degrading the robustness. The main goal of this article is to present corpus-based fully automatic stemming algorithms which address these issues. A set of experiments on six TREC collections and three other non-English collections containing news and web documents shows that the proposed query-based stemming algorithms consistently and significantly outperform four state of the art strong stemmers of completely varying principles. Our experiments also confirm that the robustness of the proposed query-based stemming algorithms are remarkably better than the existing strong baselines.

We present a stemming algorithm for text retrieval. The algorithm uses the statistics collected on the basis of certain corpus analysis based on the co-occurrence between two word variants. We use a very simple co-occurrence measure that reflects how often a pair of word variants occurs in a document as well as in the whole corpus. A graph is formed where the word variants are the nodes and two word variants form an edge if they co-occur. On the basis of the co-occurrence measure, a certain edge strength is defined for each of the edges. Finally, on the basis of the edge strengths, we propose a partition algorithm that groups the word variants based on their strongest neighbors, that is, the neighbors with largest strengths.

The aim of the Forum for Information Retrieval Evaluation (FIRE) is to create an evaluation framework in the spirit of TREC (Text REtrieval Conference), CLEF (Cross-Language Evaluation Forum), and NTCIR (NII Test Collection for IR Systems), for Indian language Information Retrieval. The first evaluation exercise conducted by FIRE was completed in 2008. This article describes the test collections used at FIRE 2008, summarizes the approaches adopted by various participants, discusses the limitations of the datasets, and outlines the tasks planned for the next iteration of FIRE.

The aim of the Forum for Information Retrieval Evaluation (FIRE) is to create a Cranfield-like evaluation framework in the spirit of TREC, CLEF and NTCIR, for Indian Language Information Retrieval. For the first year, six Indian languages have been selected: Bengali, Hindi, Marathi, Punjabi, Tamil, and Telugu. This poster describes the tasks as well as the document and topic collections that are to be used at the FIRE workshop.