Web content mining is related but different from data mining and text mining. It is related to data mining because many data mining techniques can be applied in Web content mining. It is related to text mining because much of the web contents are texts. H
Step Towards Disease Outbreak Information Extraction: Automatic ...
http://naist.cpe.ku.ac.th/SlideSNLP2007/131207/A%20Step%20Towards%20Disease%20Outbreak%20Information%20Extraction%20Automatic%20Entity%20Role%20Recognition%20for%20Named%20Entities.pdf
What would be a good way to extract headlines, dates, and authors from news articles? It seems easy to write a scraper using xpath or similar to extract this information from a single site, but I'm not sure of a more scalable solution if you're extracting from say 10,000 sites.
TeSSI® (Terminology Supported Semantic Indexing) is a state-of-the-art tool that improves upon the existing search and retrieval tools by extracting the meaning out of medical free text and placing the resulting medical ‘concepts’ in the document ind
TeSSI® (Terminology Supported Semantic Indexing) is a state-of-the-art tool that improves upon the existing search and retrieval tools by extracting the meaning out of medical free text and placing the resulting medical ‘concepts’ in the document...
Although term extraction has been researched for more than 20 years, only a few studies focus on under-resourced languages. Moreover, bilingual term mapping from comparable corpora for these languages has attracted researchers only recently. This paper presents methods for term extraction, term tagging in documents, and bilingual term mapping from comparable corpora for four under-resourced languages: Croatian, Latvian, Lithuanian, and Romanian. Methods described in this paper are language independent as long as language specific parameter data is provided by the user and the user has access to a part of speech or a morpho-syntactic tagger.
Text mining and web scraping involves chunk parsing and recognition of named entities (institutions, dates, titles)...The extraction of named entities is mostly based on a strategy that combines look up in gazetteers (lists of companies, cities, etc.) wit
Text mining and web scraping involves chunk parsing and recognition of named entities (institutions, dates, titles)...The extraction of named entities is mostly based on a strategy that combines look up in gazetteers (lists of companies, cities, etc.) wit
This is the project page for SecondString, an open-source Java-based package of approximate string-matching techniques. This code was developed by researchers at Carnegie Mellon University from the Center for Automated Learning and Discovery, the Department of Statistics, and the Center for Computer and Communications Security.
SecondString is intended primarily for researchers in information integration and other scientists. It does or will include a range of string-matching methods from a variety of communities, including statistics, artificial intelligence, information retrieval, and databases. It also includes tools for systematically evaluating performance on test data. It is not designed for use on very large data sets.
The main task of the GenIELex project is the development of a biochemistry specific lexicon as well as of an annotated corpus for the evaluation of the system. The need for the construction of such a lexicon is illustrated by the following figures, based
The main task of the GenIELex project is the development of a biochemistry specific lexicon as well as of an annotated corpus for the evaluation of the system. The need for the construction of such a lexicon is illustrated by the following figures, based
A technique for studying disorder in quantum systems is able to spot significant patterns in large data sets such as web pages, and may be adaptable to
This project aims to develop an efficient rule based extractor of entries of references, located in scientific articles in English language. The application takes a pdf file or a directory of pdf and then returns an html file, containing the list of all entries with their respective title. Moreover the title of the article cited is searched through Google Web Service to get the URL that identifying the article on the web. If the URL provides on the page a Bibtex entry, this will appear in the html output under the relative entries, stolen from some typical site like citeseer, ieeexlpore etc. The application does not make search over pdf file based on images.
Neil Ireson, Fabio Ciravegna, Marie Elaine Califf, Dayne Freitag, Nicholas Kushmerick, Alberto Lavelli: Evaluating Machine Learning for Information Extraction, 22nd International Conference on Machine Learning (ICML 2005), Bonn, Germany, 7-11 August, 2005
Neil Ireson, Fabio Ciravegna, Marie Elaine Califf, Dayne Freitag, Nicholas Kushmerick, Alberto Lavelli: Evaluating Machine Learning for Information Extraction, 22nd International Conference on Machine Learning (ICML 2005), Bonn, Germany, 7-11 August, 2005
This is the home page of the ParsCit project, which performs reference string parsing, sometimes also called citation parsing or citation extraction. It is architected as a supervised machine learning procedure that uses Conditional Random Fields as its learning mechanism. You can download the code below, parse strings online, or send batch jobs to our web service (coming soon!). The code contains both the training data, feature generator and shell scripts to connect the system to a web service (used here too).
J. Wermter, and U. Hahn. 44th Annual Meeting of the Association for Computational Linguistics, page 785--792. Sydney, Australia, Association for Computational Linguistics, (July 2006)
R. Mihalcea, and A. Csomai. Proceedings of the sixteenth ACM Conference on information and knowledge management, page 233--242. New York, NY, USA, ACM, (2007)
M. Romanello, M. Berti, A. Babeu, and G. Crane. HT '09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia, New York, NY, USA, ACM, (July 2009)
S. Auer, and J. Lehmann. ESWC '07: Proceedings of the 4th European conference on The Semantic Web, page 503--517. Berlin, Heidelberg, Springer-Verlag, (2007)
T. Tezuka, R. Lee, Y. Kambayashi, and H. Takakura. Proceedings of the Second International Conference on Web Information Systems Engineering, 2, page 14--21. (December 2001)