Mian Du


Grouping business news stories based on salience of named entities
Llorenç Escoter | Lidia Pivovarova | Mian Du | Anisia Katinskaia | Roman Yangarber
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

In news aggregation systems focused on broad news domains, certain stories may appear in multiple articles. Depending on the relative importance of the story, the number of versions can reach dozens or hundreds within a day. The text in these versions may be nearly identical or quite different. Linking multiple versions of a story into a single group brings several important benefits to the end-user–reducing the cognitive load on the reader, as well as signaling the relative importance of the story. We present a grouping algorithm, and explore several vector-based representations of input documents: from a baseline using keywords, to a method using salience–a measure of importance of named entities in the text. We demonstrate that features beyond keywords yield substantial improvements, verified on a manually-annotated corpus of business news stories.


Adapting the PULS event extraction framework to analyze Russian text
Lidia Pivovarova | Mian Du | Roman Yangarber
Proceedings of the 4th Biennial International Workshop on Balto-Slavic Natural Language Processing

Combined analysis of news and Twitter messages
Mian Du | Jussi Kangasharju | Ossi Karkulahti | Lidia Pivovarova | Roman Yangarber
Proceedings of the Joint Workshop on NLP&LOD and SWAIE: Semantic Web, Linked Open Data and Information Extraction