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Abstract

For the sake of national security, very large volumes of data and information are generated and gathered daily. Much of this data and information is written in different languages, stored in different locations, and may be seemingly unconnected. Crosslingual semantic interoperability is a major challenge to generate an overview of this disparate data and information so that it can be analyzed, shared, searched, and summarized. The recent terrorist attacks and the tragic events of September 11, 2001 have prompted increased attention an national security and criminal analysis. Many Asian countries and cities, such as Japan, Taiwan, and Singapore, have been advised that they may become the next targets of terrorist attacks. Semantic interoperability has been a focus in digital library research. Traditional information retrieval (IR) approaches normally require a document to share some common keywords with the query. Generating the associations for the related terms between the two term spaces of users and documents is an important issue. The problem can be viewed as the creation of a thesaurus. Apart from this, terrorists and criminals may communicate through letters, e-mails, and faxes in languages other than English. The translation ambiguity significantly exacerbates the retrieval problem. The problem is expanded to crosslingual semantic interoperability. In this paper, we focus an the English/Chinese crosslingual semantic interoperability problem. However, the developed techniques are not limited to English and Chinese languages but can be applied to many other languages. English and Chinese are popular languages in the Asian region. Much information about national security or crime is communicated in these languages. An efficient automatically generated thesaurus between these languages is important to crosslingual information retrieval between English and Chinese languages. To facilitate crosslingual information retrieval, a corpus-based approach uses the term co-occurrence statistics in parallel or comparable corpora to construct a statistical translation model to cross the language boundary. In this paper, the text based approach to align English/Chinese Hong Kong Police press release documents from the Web is first presented. We also introduce an algorithmic approach to generate a robust knowledge base based an statistical correlation analysis of the semantics (knowledge) embedded in the bilingual press release corpus. The research output consisted of a thesaurus-like, semantic network knowledge base, which can aid in semanticsbased crosslingual information management and retrieval.

Source

Journal of the American Society for Information Science and Technology. 56(2005) no.3, S.272-281

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Abstract

As illustrated by the World Wide Web, the volume of information in languages other than English has grown significantly in recent years. This highlights the importance of multilingual corpora. Much effort has been devoted to the compilation of multilingual corpora for the purpose of cross-lingual information retrieval and machine translation. Existing parallel corpora mostly involve European languages, such as English-French and English-Spanish. There is still a lack of parallel corpora between European languages and Asian. languages. In the authors' previous work, an alignment method to identify one-to-one Chinese and English title pairs was developed to construct an English-Chinese parallel corpus that works automatically from the World Wide Web, and a 100% precision and 87% recall were obtained. Careful analysis of these results has helped the authors to understand how the alignment method can be improved. A conceptual analysis was conducted, which includes the analysis of conceptual equivalent and conceptual information alternation in the aligned and nonaligned English-Chinese title pairs that are obtained by the alignment method. The result of the analysis not only reflects the characteristics of parallel corpora, but also gives insight into the strengths and weaknesses of the alignment method. In particular, conceptual alternation, such as omission and addition, is found to have a significant impact on the performance of the alignment method.

Footnote

Beitrag einer special topic section on multilingual information systems

Source

Journal of the American Society for Information Science and Technology. 57(2006) no.5, S.632-644

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Abstract

The authors propose a heuristic method for Chinese automatic text segmentation based an a statistical approach. This method is developed based an statistical information about the association among adjacent characters in Chinese text. Mutual information of bi-grams and significant estimation of tri-grams are utilized. A heuristic method with six rules is then proposed to determine the segmentation points in a Chinese sentence. No dictionary is required in this method. Chinese text segmentation is important in Chinese text indexing and thus greatly affects the performance of Chinese information retrieval. Due to the lack of delimiters of words in Chinese text, Chinese text segmentation is more difficult than English text segmentation. Besides, segmentation ambiguities and occurrences of out-of-vocabulary words (i.e., unknown words) are the major challenges in Chinese segmentation. Many research studies dealing with the problem of word segmentation have focused an the resolution of segmentation ambiguities. The problem of unknown word identification has not drawn much attention. The experimental result Shows that the proposed heuristic method is promising to segment the unknown words as weIl as the known words. The authors further investigated the distribution of the errors of commission and the errors of omission caused by the proposed heuristic method and benchmarked the proposed heuristic method with a previous proposed technique, boundary detection. It is found that the heuristic method outperformed the boundary detection method.

Source

Journal of the American Society for Information Science and Technology. 56(2005) no.13, S.1438-1447

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Abstract

As the demand for global information increases significantly, multilingual corpora has become a valuable linguistic resource for applications to cross-lingual information retrieval and natural language processing. In order to cross the boundaries that exist between different languages, dictionaries are the most typical tools. However, the general-purpose dictionary is less sensitive in both genre and domain. It is also impractical to manually construct tailored bilingual dictionaries or sophisticated multilingual thesauri for large applications. Corpusbased approaches, which do not have the limitation of dictionaries, provide a statistical translation model with which to cross the language boundary. There are many domain-specific parallel or comparable corpora that are employed in machine translation and cross-lingual information retrieval. Most of these are corpora between Indo-European languages, such as English/French and English/Spanish. The Asian/Indo-European corpus, especially English/Chinese corpus, is relatively sparse. The objective of the present research is to construct English/ Chinese parallel corpus automatically from the World Wide Web. In this paper, an alignment method is presented which is based an dynamic programming to identify the one-to-one Chinese and English title pairs. The method includes alignment at title level, word level and character level. The longest common subsequence (LCS) is applied to find the most reliabie Chinese translation of an English word. As one word for a language may translate into two or more words repetitively in another language, the edit operation, deletion, is used to resolve redundancy. A score function is then proposed to determine the optimal title pairs. Experiments have been conducted to investigate the performance of the proposed method using the daily press release articles by the Hong Kong SAR government as the test bed. The precision of the result is 0.998 while the recall is 0.806. The release articles and speech articles, published by Hongkong & Shanghai Banking Corporation Limited, are also used to test our method, the precision is 1.00, and the recall is 0.948.

Source

Journal of the American Society for Information Science and technology. 54(2003) no.8, S.730-742