Since 1970 vector-space models have been used for information retrieval from unstructured text. The initial simple vector-space models su#ered the same problems encountered today in searching the internet. These di#culties were significantly relieved by Latent Semantic Indexing (LSI), introduced

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Relation extraction Publications/documents grow dramatically and continually We need information extraction methods

Abstract — Five text mining software tools were evaluated by four undergraduate students inexperienced in the text mining field. The software was run on the Microsoft Windows XP operating system, and employed a variety of techniques to mine unstructured text for information. The considerations used

Previews and overviews of large, heterogeneous information resources help users comprehend the scope of collections and focus on particular subsets of interest. For narrative docu-ments, questions of “what happened? where? and when?” are natural points of entry. Building on our earlier work at the Perseus Project with detecting terms, place names, and dates, we have exploited co-occurrences of dates and place names to detect and describe likely events in document col-lections. We compare statistical measures for determining the relative significance of various events. We have built in-terfaces that help users preview likely regions of interest for a given range of space and time by plotting the distribution and relevance of various collocations. Users can also control the amount of collocation information in each view. Once particular collocations are selected, the system can identify key phrases associated with each possible event to organize browsing of the documents themselves.

Abstract—Clustering unstructured text documents is an important issue in data mining community and has a number of applications such as document archive filtering, document organization and topic detection and subject tracing. In the real world, some of the already clustered documents may

with UNESCO.

Abstract:-Extracting Sentiment Extraction is a relatively nascent field of research fuelled by the growing ubiquity of the Internet coupled with the huge volume of data being generated in it in the form of review sites, web logs and wikis. Extracting sentiments from unstructured text has emerged

iii Extraction of Causal-Association Networks from Unstructured Text Data by

, and other operations are often buried within unstructured feed text. Information extraction technology is a key enabler in such scenarios, using annotators to convert unstructured text into structured information that can facilitate mashup operations. Our demo presents the integration of System