Translation is a process that allows people to learn and comprehend others' ideas or cultures in their own language. A translation groupware, that allows two or more human translators to edit the output of machine translation at different times and different places, is developed in order to overcome the limitation of human translators and machine translation. However, translation groupware still not widely adopted by current human translators and the effectiveness is not known. An understanding of how multiple translators work and interact within a group is vital to develop a usable groupware. It is hypothesized that collaborative awareness can improve the translation groupware usability. Collaborative awareness components, such as interpersonal space and shared workspace, are identified as important characteristics to enhance the usability of translation groupware.

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The search that involves structured Web resources like XML data, services is still lagging of its own method and relying on contemporary search systems. This paper presents a method that learns semantics from structured information of these resources. Instead of committing the semantic meaning of resources to strict and formal vocabularies like ontology or data dictionary, we are interested to interpret the meaning based on the natural context of the resources. The semantics are used in search process, i.e. query reasoning and resource selection, to provide better answer in terms of context relevancy and clearer result description

Categories are used to organize information and knowledge in directory system, folder etc. As the amount of information increase and the types of information diversify, it is common to have more categories created. As the number of categories increases, it becomes more difficult to organize, manage and look up information from existing categories. In this paper, categories are annotated with concept features to facilitate the access, retrieval and sharing of information in the categories. We have observed that training texts is crucial in learning the concept of a category and serves as a good measure to help human to construct the category model. Hence, we present a study on training texts selection and evaluate the effectiveness of training texts, as well as its capability to complement human's knowledge in constructing the category model. Experimental evaluation shows that using training texts approach in category model construction gives promising results in both effectiveness and complement measures

Having broad coverage of search results returned by various search sources, combining and organizing these results in a meaningful way has become a common issue in the field of information retrieval. In this demo paper, we describe our meta search system, MICE, that is able to aggregate and classify search results based on user-customized categories. Categories help user to focus on search results, with respect to the categories concept customized by the user.

In this paper, we would like to present an approach to construct a huge Bilingual Knowledge Bank (BKB) from an English Malay bilingual dictionary based on the idea of synchronous Structured String-Tree Correspondence (SSTC). The SSTC is a general structure that can associate an arbitrary tree structure to string in a language as desired by the annotator to be the interpretation structure of the string, and more importantly is the facility to specify the correspondence between the string and the associated tree which can be non-projective. With this structure, we are able to match linguistic units at different inter levels of the structure (i.e. define the correspondence between substrings in the sentence, nodes in the tree, subtrees in the tree and sub-correspondences in the SSTC). This flexibility makes synchronous SSTC very well suited for the construction of a Bilingual Knowledge Bank we need for the English-Malay MT application.

In this paper, we describe an Example-Based Machine Translation (EBMT) system for English-Malay translation. Our approach is an example-based approach which relies sorely on example translations kept in a Bilingual Knowledge Bank (BKB). In our approach, a flexible annotation schema called Structured String-Tree Correspondence (SSTC) is used to annotate both the source and target sentences of a translation pair. Each SSTC describes a sentence, a representation tree as well as the correspondences between substrings in the sentence and subtrees in the representation tree. With both the source and target SSTCs established, a translation example in the BKB can then be represented effectively in terms of a pair of synchronous SSTCs. In the process of translation, we first try to build the representation tree for the source sentence (English) based on the example-based parsing algorithm as presented in [1]. By referring to the resultant source parse tree, we then proceed to synthesis the target sentence (Malay) based on the target SSTCs as pointed to by the synchronous SSTCs which encode the relationship between source and target SSTCs.