Shahida Jabeen

Query expansion powered by wikipedia hyperlinks

This research introduces a new query expansion method that uses Wikipedia and its hyperlink structure to find related terms for reformulating a query. Queries are first understood better by splitting into query aspects. Further understanding is gained through measuring how well each aspect is represented in the original search results. Poorly represented aspects are found to be an excellent source of query improvement. Our main contribution is the way of using Wikipedia to identify aspects and underrepresented aspects, and to weight the expansion terms. Results have shown that our approach improves the original query and search results, and outperforms two existing query expansion methods.

Improving suffix tree clustering with new ranking and similarity measures

Retrieving relevant information from web, containing enormous amount of data, is a highly complicated research area. A landmark research that contributes to this area is web clustering which efficiently organizes a large amount of web documents into a small number of meaningful and coherent groups[1,2]. Various techniques aim at accurately categorizing the web pages into clusters automatically. Suffix Tree Clustering (STC) is a phrase-based, state-of-art algorithm for web clustering that automatically groups semantically related documents based on shared phrases. Research has shown that it has outperformed other clustering algorithms such as K-means and Buckshot due to its efficient utilization of phrases to identify the clusters. Using STC as the baseline, we introduce a new method for ranking base clusters and new similarity measures for comparing clusters. Our STHAC technique combines the Heirarchical Agglomerative clustering method with phrase based Suffix Tree clustering to improve the cluster merging process. Experimental results have shown that STHAC outperforms the original STC as well as ESTC(our precious extended version of STC) with 16% increase in F-measure. This increase in F-measure of STHAC is achieved due to its better filtering of low score clusters, better similarity measures and efficient cluster merging algorithms.

Awareness Elements In Web Based Cooperative Writing Applications

In this paper, we present our practical experience of exploring Web based Cooperative Writing Applications(CWA), a kind of groupware which supports people working in groups to achieve common tasks. While producing cooperatively, participants need structured information about activities of their colleagues and the shared production. Without these features, the cooperative production would be inconsistent and incoherent. We study awareness functionalities integrated into several CWAs on the basis of present and past elements, the kind of communication service, and coordination mechanism. The objective of this study is to investigate the trade-off concerning awareness and to provide evaluation assistance to a group or a member in choosing an application for joint production.

Directional Context Helps: Guiding Semantic Relatedness Computation by Asymmetric Word Associations

Semantic relatedness computation is the task of measuring the degree of relatedness of two concepts. It is a well known problem with applications ranging from computational linguistics to cognitive psychology. In all existing approaches, relatedness is assumed to be symmetric i.e. the relatedness of terms t i and term t j is considered the same as the relatedness of terms t j and t i . However, there are tasks such as free word association, where the association strength assumed to be asymmetric. In free word association, the given term determines the context in which the association strength must be computed. Based on this key observation, the paper presents a new approach to computing term relatedness guided by asymmetric association. The focus of this paper is on using Wikipedia for extracting directional context of each given term and computing the association of input term pair in this context. The proposed approach is generic enough to deal with both symmetric as well as asymmetric relatedness computation problems. Empirical evaluation on multiple benchmark datasets shows encouraging results when our automatically computed relatedness scores are correlated with human judgments.

Harnessing wikipedia semantics for computing contextual relatedness

This paper proposes a new method of automatically measuring semantic relatedness by exploiting Wikipedia as an external knowledge source. The main contribution of our research is to propose a relatedness measure based on Wikipedia senses and hyperlink structure for computing contextual relatedness of any two terms. We have evaluated the effectiveness of our approach using three datasets and have shown that our approach competes well with other well known existing methods.

A Hybrid Model for Learning Semantic Relatedness Using Wikipedia-Based Features

Semantic relatedness computation is the task of quantifying the degree of relatedness of two concepts. The performance of existing approaches to computing semantic relatedness is highly dependent on particular aspects of relatedness. For instance, taxonomy-based approaches aim at computing similarity, which is a special case of semantic relatedness. On the other hand, corpus-based approaches focus on the associative relations of words by taking their distributional features into account. Based on the assumption that different aspects of knowledge sources cover different kinds of semantic relations, this paper presents a hybrid model for computing semantic relatedness of words using new features extracted from various aspects of Wikipedia. The focus of this paper is on finding the optimal feature combination(s) that enhance the performance of the hybrid model. The empirical evaluation on benchmark datasets has shown that hybrid features perform better than single features by providing a complementary coverage of semantic relations, leading to improved correlation with human judgments.

Probabilistic Associations as a Proxy for Semantic Relatedness

Semantic relatedness computation is a well known problem with multidisciplinary applications. Existing approaches to computing semantic relatedness ignore the asymmetric associations of words. In the absence of an explicit topical context, these asymmetric associations can be effectively used to represent the relation of words in directional contexts. Motivated by the idea of word associations, this paper presents a new approach to computing semantic relatedness using asymmetric association based probabilities of words extracted from the directional contexts of words based on the Wikipedia corpus. The performance evaluation of the proposed approach on a variety of publicly available benchmark datasets shows that the asymmetric association based measures outperformed not only the baseline symmetric measures but also most of the state-of-art approaches.

Using Asymmetric Associations for Commonsense Causality Detection

Human actions in this world are based on exploiting knowledge of causality. Humans find it easy to connect a cause to the subsequent effect but formal reasoning about causality has proved to be a difficult task in automated NLP applications because it requires rich knowledge of all the relevant events and circumstances. Automated approaches to detecting causal connections attempt to partially capture this knowledge using commonsense reasoning based on lexical and semantics constraints. However, their performance is limited by the lack of sufficient breadth of commonsense knowledge to draw causal inferences. This paper presents a commonsense causality detection system using a new semantic measure based on asymmetric associations on the Choice Of Plausible Alternatives (COPA) task. When evaluated on three COPA benchmark datasets, the causality detection system using asymmetric association based measures demonstrates a superior performance to other symmetric measures.