%0 Journal Article %1 noauthororeditor %A Kaur, Kamaldeep %A Gupta, Vishal %D 2011 %J Computer Science & Engineering: An International Journal %K NLP Text Topic mining tracking %N 03 %P 01-13 %R 10.5121/cseij.2011.1304 %T TOPIC TRACKING FOR PUNJABI LANGUAGE %U http://airccse.org/journal/cseij/papers/0811cseij04.pdf %V 01 %X This paper introduces Topic Tracking for Punjabi language. Text mining is a field that automatically extracts previously unknown and useful information from unstructured textual data. It has strong connections with natural language processing. NLP has produced technologies that teach computers natural language so that they may analyze, understand and even generate text. Topic tracking is one of the technologies that has been developed and can be used in the text mining process. The main purpose of topic tracking is to identify and follow events presented in multiple news sources, including newswires, radio and TV broadcasts. It collects dispersed information together and makes it easy for user to get a general understanding. Not much work has been done in Topic tracking for Indian Languages in general and Punjabi in particular. First we survey various approaches available for Topic Tracking, then represent our approach for Punjabi. The experimental results are shown.

@article{noauthororeditor, abstract = { This paper introduces Topic Tracking for Punjabi language. Text mining is a field that automatically extracts previously unknown and useful information from unstructured textual data. It has strong connections with natural language processing. NLP has produced technologies that teach computers natural language so that they may analyze, understand and even generate text. Topic tracking is one of the technologies that has been developed and can be used in the text mining process. The main purpose of topic tracking is to identify and follow events presented in multiple news sources, including newswires, radio and TV broadcasts. It collects dispersed information together and makes it easy for user to get a general understanding. Not much work has been done in Topic tracking for Indian Languages in general and Punjabi in particular. First we survey various approaches available for Topic Tracking, then represent our approach for Punjabi. The experimental results are shown. }, added-at = {2018-12-22T06:41:21.000+0100}, author = {Kaur, Kamaldeep and Gupta, Vishal}, biburl = {https://www.bibsonomy.org/bibtex/2ff2e74b30c86586c22e8249adce6c9be/micaela}, doi = {10.5121/cseij.2011.1304}, interhash = {c2f7d5196b1db88866beea4c6d683084}, intrahash = {ff2e74b30c86586c22e8249adce6c9be}, issn = {2231 - 329X (Online) ; 2231 - 3583 (Print)}, journal = {Computer Science & Engineering: An International Journal }, keywords = {NLP Text Topic mining tracking}, language = {Eng}, month = aug, number = 03, pages = {01-13}, timestamp = {2018-12-22T06:41:21.000+0100}, title = {TOPIC TRACKING FOR PUNJABI LANGUAGE}, url = {http://airccse.org/journal/cseij/papers/0811cseij04.pdf}, volume = 01, year = 2011 }