Dublin City University at CLEF 2004: experiments in monolingual, bilingual and multilingual retrieval

The Dublin City University group participated in the monolingual, bilingual and multilingual retrieval tasks this year. The main focus of our investigation this year was extending our retrieval system to document languages other than English, and completing the multilingual task comprising four languages: English, French, Russian and Finnish. Results from our French monolingual experiments indicate that working in French is more effective for retrieval than adopting document and topic translation to English. However, comparison of our multilingual retrieval results using different topic and document translation reveals that this result does not extend to retrieved list merging for the multilingual task in a simple predictable way

Sub-word indexing and blind relevance feedback for English, Bengali, Hindi, and Marathi IR

The Forum for Information Retrieval Evaluation (FIRE) provides document collections, topics, and relevance assessments for information retrieval (IR) experiments on Indian languages. Several research questions are explored in this paper: 1. how to create create a simple, languageindependent corpus-based stemmer, 2. how to identify sub-words and which types of sub-words are suitable as indexing units, and 3. how to apply blind relevance feedback on sub-words and how feedback term selection is affected by the type of the indexing unit. More than 140 IR experiments are conducted using the BM25 retrieval model on the topic titles and descriptions (TD) for the FIRE 2008 English, Bengali, Hindi, and Marathi document collections. The major findings are: The corpus-based stemming approach is effective as a knowledge-light term conation step and useful in case of few language-specific resources. For English, the corpusbased stemmer performs nearly as well as the Porter stemmer and significantly better than the baseline of indexing words when combined with query expansion. In combination with blind relevance feedback, it also performs significantly better than the baseline for Bengali and Marathi IR. Sub-words such as consonant-vowel sequences and word prefixes can yield similar or better performance in comparison to word indexing. There is no best performing method for all languages. For English, indexing using the Porter stemmer performs best, for Bengali and Marathi, overlapping 3-grams obtain the best result, and for Hindi, 4-prefixes yield the highest MAP. However, in combination with blind relevance feedback using 10 documents and 20 terms, 6-prefixes for English and 4-prefixes for Bengali, Hindi, and Marathi IR yield the highest MAP. Sub-word identification is a general case of decompounding. It results in one or more index terms for a single word form and increases the number of index terms but decreases their average length. The corresponding retrieval experiments show that relevance feedback on sub-words benefits from selecting a larger number of index terms in comparison with retrieval on word forms. Similarly, selecting the number of relevance feedback terms depending on the ratio of word vocabulary size to sub-word vocabulary size almost always slightly increases information retrieval effectiveness compared to using a fixed number of terms for different languages

Detecting missing content queries in an SMS-Based HIV/AIDS FAQ retrieval system

Automated Frequently Asked Question (FAQ) answering systems use pre-stored sets of question-answer pairs as an information source to answer natural language questions posed by the users. The main problem with this kind of information source is that there is no guarantee that there will be a relevant question-answer pair for all user queries. In this paper, we propose to deploy a binary classifier in an existing SMS-Based HIV/AIDS FAQ retrieval system to detect user queries that do not have the relevant question-answer pair in the FAQ document collection. Before deploying such a classifier, we first evaluate different feature sets for training in order to determine the sets of features that can build a model that yields the best classification accuracy. We carry out our evaluation using seven different feature sets generated from a query log before and after retrieval by the FAQ retrieval system. Our results suggest that, combining different feature sets markedly improves the classification accuracy

Searching strategies for the Bulgarian language

This paper reports on the underlying IR problems encountered when indexing and searching with the Bulgarian language. For this language we propose a general light stemmer and demonstrate that it can be quite effective, producing significantly better MAP (around + 34%) than an approach not applying stemming. We implement the GL2 model derived from the Divergence from Randomness paradigm and find its retrieval effectiveness better than other probabilistic, vector-space and language models. The resulting MAP is found to be about 50% better than the classical tf idf approach. Moreover, increasing the query size enhances the MAP by around 10% (from T to TD). In order to compare the retrieval effectiveness of our suggested stopword list and the light stemmer developed for the Bulgarian language, we conduct a set of experiments on another stopword list and also a more complex and aggressive stemmer. Results tend to indicate that there is no statistically significant difference between these variants and our suggested approach. This paper evaluates other indexing strategies such as 4-gram indexing and indexing based on the automatic decompounding of compound words. Finally, we analyze certain queries to discover why we obtained poor results, when indexing Bulgarian documents using the suggested word-based approac

A Systematic Review on Stopword Removal Algorithms

Stopwords, also known as noise words, are the words that contain a little information which is not usually required. Stopwords were discovered by H.P. Luhn in 1958. In the domain of information retrieval, an effective indexing can be achieved by removing the stopwords. Indexing is a technique of connecting or tagging documents with different search terms or criteria. The main motive behind the elimination of stopwords is to increase the execution speed and the accuracy. It not only decreases the vector space but also helps to improve overall performance. It also helps in reducing the size of text. Till now, techniques for automatic stopwords removal have been developed for languages such as English, Sanskrit, Arabic, Chinese, etc. In this paper, we discuss the different techniques which have been used by the researchers to construct automated stopword lists in different languages