Cross-language information retrieval (CLIR), where queries and documents are in different languages, has of late become one of the major topics within the information retrieval community. This paper

This paper presents a morphological classification of languages from the IR perspective. Linguistic typology research has shown that the morphological complexity of every language in the world can be described by two variables, index of synthesis and index of fusion. These variables provide a theoretical basis for IR research handling morphological issues. A common theoretical framework is needed in particular because of the increasing significance of cross-language retrieval research and CLIR systems processing different languages. The paper elaborates the linguistic morphological typology for the purposes of IR research. It studies how the indexes of synthesis and fusion could be used as practical tools in mono- and cross-lingual IR research. The need for semantic and syntactic typologies is discussed. The paper also reviews studies made in different languages on the effects of morphology and stemming in IR. 1.

Huge multilingual news articles are reported anct dissolninatod on the Internot. itow to oxlract the key inforlnation and save the reading time is a crucial issue. This paper proposes architluro of multilingual news sumlnarizer, including monolingual and multilingual clustering, similarity measure among moauingfid units, and proscntatiou of smnmarization results. Translation among news stories, idiosyncrasy among htnguages, implicit information, aud user prellronco are addressed.

The paper introduces a query translation model that re ects the structure of the cross-language information retrieval task. The model is based on a structured bilingual dictionary in which the translations of each term are clustered into groups with distinct meanings. Query translation is modeled as a two-stage process, with the system rst determining the intended meaning of a query term and then selecting translations appropriate to that meaning that might appear in the document collection. An implementation of structured translation based on automatic dictionary clustering is described and evaluated by using Chinese queries to retrieve English documents. Structured translation achieved an average precision that was statistically indistinguishable from Pirkola's technique for very short queries, but Pirkola's technique outperformed structured translation on long queries. The paper concludes with some observations on future work to improve retrieval e ectiveness and on other potential uses of structured translation in interactive cross-language retrieval applications. 1.

A WordNet-like linguistic resource is useful, but difficult to construct. This article proposes a method to integrate five linguistic resources, including English/Chinese sense-tagged corpora, English/Chinese thesauruses, and a bilingual dictionary. Chinese words are mapped into WordNet. A Chinese WordNet and a Chinese-English WordNet are derived by following the structures of WordNet. Experiments with Chinese-English information retrieval are developed to evaluate the applicability of the Chinese-English WordNet. The best model achieves 0.1010 average precision, 69.23 % of monolingual information retrieval. It also gains a 10.02 % increase relative to a model that resolves translation ambiguity and target polysemy problems together.

This paper studies cross-language cross-medium information retrieval. We introduce several approaches to unify the languages and media of queries and documents. We experiment on cross-language image retrieval via spoken query. Two approaches are proposed to recognize and translate spoken queries. We also propose a similarity-based approach to identify and backward transliterate named entities in a spoken query. 1.

This paper presents a framework of using Chinese speech to access images via English captions. The formulation and the structure mapping rules of Chinese and English named entities are extracted from an NICT foreign location name corpus. For

National Taiwan University (NTU) Natural Language Processing Laboratory (NLPL) participated in MLIR task in CLEF 2002. We submitted five official multilingual runs. In this paper, we try to resolve the collection fusion problem. We experimented with several merging strategies that merge the results of several intermediate runs. 1.

The paper introduces a query translation model that reflects the structure of the cross-language information retrieval task. The model is based on a structured bilingual dictionary in which the translations of each term are clustered into groups with distinct meanings. Query translation is modeled as a two-stage process, with the system first determining the intended meaning of a query term and then selecting translations appropriate to that meaning that might appear in the document collection. An implementation of structured translation based on automatic dictionary clustering is described and evaluated by using Chinese queries to retrieve English documents. Structured translation achieved an average precision that was statistically indistinguishable from Pirkola's technique for very short queries, but Pirkola's technique outperformed structured translation on long queries. The paper concludes with some observations on future work to improve retrieval effectiveness and on other potent...