We describe a named-entity tagging system that requires minimal linguistic knowledge and thus may be applied to new target languages without signiicant adaptation. To maintain a language-neutral posture, the system is linguistically na ve, and in fact, reduces the tagging problem to supervised machine learning. A large number of binary features are extracted from labeled data to train classiier...

We observe that (1) how a given named entity (NE) is translated (i.e., either semantically or phonetically) depends greatly on its associated entity type, and (2) entities within an aligned pair should share the same type. Also, (3) those initially detected NEs are anchors, whose information should be used to give certainty scores when selecting candidates. From this basis, an integrated model ...

This paper outlines our collaboration on the construction of a named entity recognizer for the Thai language. This tool will support natural language processing applications in Thai such as high precision information retrieval, summarization and machine translation. Named entity recognition has been quite successfully applied to English due to the interest generated by evaluation conferences su...

Rapid improvement of electronic documents in World Wide Web has made overload to the users in accessing the information. Therefore, abstracting the primary content from numerous documents related to same topic is highly essential. Summarization of multiple documents helps in valuable decision-making in less time. This paper proposed a framework named Adept Multi-Document Summarization (AMDS) fo...