Named Entity Recognition Using Web Document Corpus

This paper introduces a named entity recognition approach in textual corpus. This Named Entity (NE) can be a named: location, person, organization, date, time, etc., characterized by instances. A NE isfound in texts accompanied by contexts: words that are left or right of the NE. The work mainly aims at identifying contexts inducing the NE’s nature. As such, The occurrence of the word "President" in atext, means that this word or context may be followed by the name of a president as President "Obama". Likewise, a word preceded by the string "footballer" induces that this is the name of afootballer. NE recognition may be viewed as a classification method, where every word is assigned to a NE class, regarding the context. The aim of this study is then to identify and classify the contexts that are most relevant to recognize aNE, those which are frequently found with the NE. A learning approach using training corpus: web documents, constructed from learning examples is then suggested. Frequency representations andmodified tf-idf representations are used to calculate the context weights associated to context frequency, learning example frequency, and document frequency in the corpus.