Yingju Xia

Infrastructure for Standardization of Asian Language Resources

As an area of great linguistic and cultural diversity, Asian language resources have received much less attention than their western counterparts. Creating a common standard for Asian language resources that is compatible with an international standard has at least three strong advantages: to increase the competitive edge of Asian countries, to bring Asian countries to closer to their western counterparts, and to bring more cohesion among Asian countries. To achieve this goal, we have launched a two year project to create a common standard for Asian language resources. The project is comprised of four research items, (1) building a description framework of lexical entries, (2) building sample lexicons, (3) building an upper-layer ontology and (4) evaluating the proposed framework through an application. This paper outlines the project in terms of its aim and approach.

Query Expansion using LMF-Compliant Lexical Resources

This paper reports prototype multilingual query expansion system relying on LMF compliant lexical resources. The system is one of the deliverables of a three-year project aiming at establishing an international standard for language resources which is applicable to Asian languages. Our important contributions to ISO 24613, standard Lexical Markup Framework (LMF) include its robustness to deal with Asian languages, and its applicability to cross-lingual query tasks, as illustrated by the prototype introduced in this paper.

Two stages based organization name disambiguity

With the rapid growth of user generated media, Twitter has become an important information resource where users share fresh information on any subject. Pursuing on the problem of finding related tweets to a given organization, we propose two stages based organization name disambiguity. Insufficient information and the diversity of organizations are two key problems for this task. We induce multiple types of features to enrich the information of organization to solve the problem of insufficient information. The relationships between tweets and organization, the relationships among tweets are mined in two stages to solve the diversity of organization. Furthermore, we probe the distribution of organization names ambiguity and its influence to different classifiers. Our experimental results on WePS-3 prove the proposed methods are effective and promising in performing this task.

Multiple Factors-Based Opinion Retrieval and Coarse-to-Fine Sentiment Classification

Opinion mining is a growing interest task in both research and practical applications. It deals with the computational treatment of opinion, sentiment, and subjectivity in documents. This paper focuses on retrieving the opinion documents and giving their sentiment orientation. Mining and ranking the topic relevant opinion documents are implemented with a sentiment model, combining the existing knowledge and statistic information. Multi-level sentiment analysis approach is proposed to find the topic related sentiment information. Our experimental results on COAE show the effectiveness of the proposed techniques and the feasibility of classifying orientation at varying levels of granularity.