Navigating multilingual news collections using automatically extracted information

We are presenting a text analysis tool set that allows analysts in various fields to sieve through large collections of multilingual news items quickly and to find information that is of relevance to them. For a given document collec- tion, the tool set automatically clusters the texts into groups of similar articles, extracts names of places, people and organisations, lists the user- defined specialist terms found, links clusters and entities, and generates hyperlinks. Through its daily news analysis operating on thousands of articles per day, the tool also learns relation- ships between people and other entities. The fully functional prototype system allows users to ex- plore and navigate multilingual document collec- tions across languages and time.

• Publication year

  2005

• Venue

  International Conference on Information Technology Interfaces

• Publication date

  2005-06-20

• Fields of study

  Linguistics, Computer Science

• Identifiers
  DOI 10.2498/cit.2005.04.01arXiv cs/0609053
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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