Xiwen Cheng


Annotating Opinions in German Political News
Hong Li | Xiwen Cheng | Kristina Adson | Tal Kirshboim | Feiyu Xu
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

This paper presents an approach to construction of an annotated corpus for German political news for the opinion mining task. The annotated corpus has been applied to learn relation extraction rules for extraction of opinion holders, opinion content and classification of polarities. An adapted annotated schema has been developed on top of the state-of-the-art research. Furthermore, a general tool for annotating relations has been utilized for the annotation task. An evaluation of the inter-annotator agreement has been conducted. The rule learning is realized with the help of a minimally supervised machine learning framework DARE.


Talking NPCs in a Virtual Game World
Tina Klüwer | Peter Adolphs | Feiyu Xu | Hans Uszkoreit | Xiwen Cheng
Proceedings of the ACL 2010 System Demonstrations

Question Answering Biographic Information and Social Network Powered by the Semantic Web
Peter Adolphs | Xiwen Cheng | Tina Klüwer | Hans Uszkoreit | Feiyu Xu
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

After several years of development, the vision of the Semantic Web is gradually becoming reality. Large data repositories have been created and offer semantic information in a machine-processable form for various domains. Semantic Web data can be published on the Web, gathered automatically, and reasoned about. All these developments open interesting perspectives for building a new class of domain-specific, broad-coverage information systems that overcome a long-standing bottleneck of AI systems, the notoriously incomplete knowledge base. We present a system that shows how the wealth of information in the Semantic Web can be interfaced with humans once again, using natural language for querying and answering rather than technical formalisms. Whereas current Question Answering systems typically select snippets from Web documents retrieved by a search engine, we utilize Semantic Web data, which allows us to provide natural-language answers that are tailored to the current dialog context. Furthermore, we show how to use natural language processing technologies to acquire new data and enrich existing data in a Semantic Web framework. Our system has acquired a rich biographic data resource by combining existing Semantic Web resources, which are discovered from semi-structured textual data in Web pages, with information extracted from free natural language texts.


Gossip Galore – A Self-Learning Agent for Exchanging Pop Trivia
Xiwen Cheng | Peter Adolphs | Feiyu Xu | Hans Uszkoreit | Hong Li
Proceedings of the Demonstrations Session at EACL 2009


Fine-grained Opinion Topic and Polarity Identification
Xiwen Cheng | Feiyu Xu
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

This paper presents OMINE, an opinion mining system which aims to identify concepts such as products and their attributes, and analyze their corresponding polarities. Our work pioneers at linking extracted topic terms with domain-specific concepts. Compared with previous work, taking advantage of ontological techniques, OMINE achieves 10% higher recall with the same level precision on the topic extraction task. In addition, making use of opinion patterns for sentiment analysis, OMINE improves the performance of the backup system (NGram) around 6% for positive reviews and 8% for negative ones.