This paper presents a language-independent probabilistic answer ranking framework for question answering. The framework estimates the probability of an individual answer candidate given the degree of answer relevance and the amount of supporting evidence provided in the set of answer candidates for the question. Our approach was evaluated by comparing the candidate answer sets generated by Chinese and Japanese answer extractors with the re-ranked answer sets produced by the answer ranking framework. Empirical results from testing on NT-CIR factoid questions show a 40 % performance improvement in Chinese answer selection and a 45 % improvement in Japanese answer selection.

In this paper, we describe the JAVELIN Cross Language Question Answering system, which includes modules for question analysis, keyword translation, document retrieval, information extraction and answer generation. In the NTCIR6 CLQA2 evaluation, our system achieved 19 % and 13 % accuracy in the English-to-Chinese and English-to-Japanese subtasks, respectively. An overall analysis and a detailed module-by-module analysis are presented.

Automatic analysis of syntax is one of the core problems in natural language processing. Despite significant advances in syntactic parsing of written text, the application of these techniques to spontaneous spoken language has received more limited attention. The recent explosive growth of online, accessible corpora of spoken language interactions opens up new opportunities for the development of high accuracy parsing approaches to the analysis of spoken language. The availability of high accuracy parsers will in turn provide a platform for development of a wide range of new applications, as well as for advanced research on the nature of conversational interactions. One concrete field of investigation that is ripe for the application of such parsing tools is the study of child language acquisition.

Data-driven function tag assignment has been studied for English using Penn Treebank data. In this paper, we address the question of whether such method can be applied to other languages and Treebank resources. In addition to simply extend previous method from English to Chinese, we also proposed an effective way to recognize function tags directly from lexical information, which is easily scalable for languages that lack sufficient parsing resources or have inherent linguistic challenges for parsing. We investigated a supervised sequence learning method to automatically recognize function tags, which achieves an F-score of 0.938 on gold-standard POS (Part-of-Speech) tagged Chinese text – a statistically significant improvement over existing Chinese function label assignment systems. Results show that a small number of linguistically motivated lexical features are sufficient to achieve comparable performance to systems using sophisticated parse trees. 1