%0 Journal Article %1 noauthororeditor %A Karaa, Wahiba Ben Abdessalem %D 2011 %J International Journal of Managing Information Technology (IJMIT) %K Information Learning Named Web document. entity extraction tf-idf %N 1 %P 46 to 55 %T Named Entity Recognition Using Web Document Corpus %U http://airccse.org/journal/ijmit/papers/3111ijmit04.pdf %V 3 %X 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 is found 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 a text, means that this word or context may be followed by the name of a president as President Öbama". Likewise, a word preceded by the string "footballer" induces that this is the name of a footballer. 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 a NE, 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 and modified tf-idf representations are used to calculate the context weights associated to context frequency, learning example frequency, and document frequency in the corpus.

@article{noauthororeditor, abstract = {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 is found 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 a text, 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 a footballer. 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 a NE, 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 and modified tf-idf representations are used to calculate the context weights associated to context frequency, learning example frequency, and document frequency in the corpus.}, added-at = {2020-06-05T06:07:10.000+0200}, author = {Karaa, Wahiba Ben Abdessalem}, biburl = {https://www.bibsonomy.org/bibtex/24cfe7bdda83efec14d12d7cf19d54fc5/ijmit_journal}, interhash = {175aedf9e2892e4286bdd67b3c5f5a6b}, intrahash = {4cfe7bdda83efec14d12d7cf19d54fc5}, journal = {International Journal of Managing Information Technology (IJMIT)}, keywords = {Information Learning Named Web document. entity extraction tf-idf}, language = {English}, month = {February}, number = 1, pages = {46 to 55}, timestamp = {2020-06-05T06:07:10.000+0200}, title = {Named Entity Recognition Using Web Document Corpus }, url = {http://airccse.org/journal/ijmit/papers/3111ijmit04.pdf}, volume = 3, year = 2011 }