International Journal of Computer Processing of Languages

This paper develops a Natural Language Processing method for extracting temporal information of events from news articles. The extraction process is based on the result of a morpho-syntactic analysis. The obtained results, which are a translation of morpho-syntactic sentences, are skimmed in order to identify temporal markers.

In our approach, we consider a specific description of events. Our approach is based on a methodology of temporal markers classes and on a contextual exploration method.

Our experimental results indicate that our approach is significant for extracting relevant event information. We have also achieved a system, called EXEV, on the basis of the proposed approach and have empirically verified its effectiveness. More specifically, EXEV system automatically extracts all information about events (paragraphs or sentences) from news articles and specifies more information about events: associations, locations, temporal settings, etc.

The system was tested on press articles and gives recall and precision more than 80%.

In cross-language information retrieval (CLIR), queries in a source language are used to retrieve relevant documents in a target language. This paper describes the automatic construction of a translation matrix for English-Arabic CLIR applications. English-Arabic parallel sentences are translated using an adaptation of the expected mutual information measure (EMIM) technique and a matrix of translation terms is created. We evaluate our translation matrix as part of a retrieval system by comparing the effectiveness of cross-language retrieval and monolingual retrieval. The results show that the performance of English-Arabic CLIR using the translation matrix for query translation is either comparable to or better than that of monolingual Arabic retrieval depending on the number of translation terms used and the type of indexing method chosen.

Centering Theory assumes that entities realized in an utterance can be ranked according to their relative salience degree. This ranking called Cf-ranking (ranking of forward-looking centers) determines the likelihood that the entities realized in an utterance will be the center of the subsequent utterance, and it is one of the central issues in Centering literatures. This paper deals with these Cf-ranking issues in Korean at the level of text structuring and pronominalization for coherent text generation. For text structuring, we compare several Cf-ranking methods by examining various Centering-based metrics to evaluate local coherence of text, and for pronominalization, we compare them by examining previous rules for Centering-based pronoun generation rules and our pronominalization model. In almost all previous works, surface word order was not solely employed for Cf-ranking, instead it was additionally considered to supplement the main ranking scheme based on the fact that linear order does not perform well alone. However, this study shows that due to the characteristics of the Korean language, ranking by surface word order is better than any other ranking method in most Centering-based Metrics which depend on Cf-ranking, and it is also reliable in terms of pronominalization accuracy. Additionally we found that based on the Cf-ranking by surface word order, it is the most effective way for text structuring to maximize simply the number of utterance pairs whose first realized nominal entity in adjacent utterances is identical.

Machine transliteration is automatic generation of the phonetic equivalents in a target language given a source language term, which is useful in many cross language applications. Transliteration between far distant languages, e.g. English and Chinese, is challenging because their phonological dissimilarities are significant. Existing techniques are typically rule-based or statistically noisy channel-based. Their accuracies are very low due to their intrinsic limitations on modeling transcription details. We propose direct statistical approaches on transliterating phoneme sequences for English–Chinese name translation. Aiming to improve performance, we propose two direct models: First, we adopt Finite State Automata on a process of direct mapping from English phonemes to a set of rudimentary Chinese phonetic symbols plus mapping units dynamically discovered from training. An effective algorithm for aligning phoneme chunks is proposed. Second, contextual features of each phoneme are taken into consideration by means of Maximum Entropy formalism, and the model is further refined with the precise alignment scheme using phoneme chunks. We compare our approaches with the noisy channel baseline that applies IBM SMT model, and demonstrate their superiority.