Ahmad El Sayed

A multisource context-dependent semantic distance between concepts

A major lack in the existing semantic similarity methods is that no one takes into account the context or the considered domain. However, two concepts similar in one context may appear completely unrelated in another context. In this paper, our first-level approach is context-dependent. We present a new method that computes semantic similarity in taxonomies by considering the context pattern of the text corpus. In addition, since taxonomies and corpora are interesting resources and each one has its strengths and weaknesses, we propose to combine similarity methods in our second-level multi-source approach. The performed experiments showed that our approach outperforms all the existing approaches.

Exploring Validity Indices for Clustering Textual Data

The goal of any clustering algorithm producing flat partitions of data, is to find both the optimal clustering solution and the optimal number of clusters. One natural way to reach this goal without the need for parameters, is to involve a validity index in a clustering process, which can lead to an objective selection of the optimal number of clusters. In this chapter, we provide two main contributions. Firstly, since validity indices have been mostly studied in a two or three-dimensionnal datasets, we have chosen to evaluate them in a real-world applications, document and word clustering. Secondly, we propose a new context-aware method that aims at enhancing the validity indices usage as stopping criteria in agglomerative algorithms. Experimental results show that the method is a step-forward in using, with more reliability, validity indices as stopping criteria.

A new framework for taxonomy discovery from text

Ontology learning from text is considered as an appealing and a challenging approach to address the shortcomings of the handcrafted ontologies. In this paper, we present OLEA, a new framework for ontology learning from text. The proposal is a hybrid approach combining the pattern-based and the distributional approaches. It addresses key issues in the area of ontology learning: low recall of the pattern-based approach, low precision of the distributional approach, and finally ontology evolution. Preliminary experiments performed at each stage of the learning process show the pros and cons of the proposal.

Using semantic distance in a content-based heterogeneous information retrieval system

This paper brings two contributions in relation with the semantic heterogeneous (documents composed of texts and images) information retrieval: (1) A new context-based semantic distance measure for textual data, and (2) an IR system providing a conceptual and an automatic indexing of documents by considering their heterogeneous content using a domain specific ontology. The proposed semantic distance measure is used in order to automatically fuzzify our domain ontology. The two proposals are evaluated and very interesting results were obtained. Using our semantic distance measure, we obtained a correlation ratio of 0.89 with human judgments on a set of words pairs which led our measure to outperform all the other measures. Preliminary combination results obtained on a specialized corpus of web pages are also reported.

A Hybrid Approach for Taxonomy Learning from Text

Ontology learning from text is considered as an appealing and challeging alternative to address the shortcomings of the hand-crafted ontologies. In this paper, we present OLea, a new framework for ontology learning from text. The proposal is a hybrid approach combining the pattern-based and the distributionnal approaches. It addresses key issues in the area of ontology learning: context-dependency, low recall of the pattern-based approach, low precision of the distributionnal approach, and finally ontology evolution. Experiments performed at each stage of the learning process show the advantages and drawbacks of the proposal.

On determining the optimal partition in agglomerative clustering of documents

The goal of any clustering algorithm producing flat partitions of data, is to find both the optimal clustering solution and the optimal number of clusters. One natural way to reach this goal without the need for parameters, is to involve a validity index in a clustering process, which can lead to an objective selection of the optimal number of clusters. In this paper, we provide an evaluation of the major relative indices involving them in an agglomerative clustering algorithm for documents. The evaluation seeks the indices ability to identify both the optimal solution and the optimal number of clusters. Then, we propose a new context-aware method that aims at enhancing the validity indices usage as stopping criteria in agglomerative algorithms. Experimental results show that the method is a step-forward in using, with more reliability, validity indices as stopping criteria.

Word clustering with validity indices

The goal of any clustering algorithm producing flat partitions of data is to find the optimal clustering solution and the optimal number of clusters. One natural way to reach this goal without the need for parameters, is to involve a validity index in the clustering process, which can lead to an objective selection of the optimal number of clusters. In this paper, we provide two main contributions. Firstly, since validity indices have been mostly studied in small dimensional datasets, we have chosen to evaluate them in a real-world task: agglomerative clustering of words. Secondly, we propose a new context-aware method that aims at enhancing the validity indices usage as stopping criteria in agglomerative algorithms. Experimental results show that the method is a step-forward in using, with more reliability, validity indices as stopping criteria.