Biomedical named entity recognition (NER) is a difficult problem in biomedical information processing due to the widespread ambiguity of terms out of context and extensive lexical ...
Seonho Kim, Juntae Yoon, Kyung-Mi Park, Hae-Chang ...
We present a syllable bigram model for segmenting a Korean sentence into words and correcting word-spacing errors in the spelling checker. We evaluated the system’s performance ...
Abstract. One of the most amazing characteristics that defines the human being is humour. Its analysis implies a set of subjective and fuzzy factors, such as the linguistic, psych...
Speech carries both linguistic content – phonemes, words, sentences – and talker information, sometimes called ‘indexical information’. While talker variability materially...
We describe our contribution to the Generation Challenge 2010 for the tasks of Named Entity Recognition and coreference detection (GREC-NER). To extract the NE and the referring e...