Apache Tika is a toolkit for detecting and extracting metadata and structured text content from various documents using existing parser libraries. For more information about Tika, please see the list of supported document formats and the available documentation . You can find the latest release on the download page . See the Getting Started guide for instructions on how to start using Tika.
Tika is a subproject of Apache Lucene . Lucene is a project of the Apache Software Foundation .
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
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
The cb2Bib is a tool for rapidly extracting unformatted, or unstandardized bibliographic references from email alerts, journal Web pages, and PDF files.
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.
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
Todays feature of the week post will point you to one of the hidden features of the system. As most of you certainly know one way to acquire the meta data of a publication is to use the screen scraping facility of BibSonomy.
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
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...
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
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
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
M. Schwab, R. Jäschke, and F. Fischer. Proceedings of the 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, page 110--115. Association for Computational Linguistics, (2023)
M. Schwab, R. Jäschke, and F. Fischer. Proceedings of the 5th International Conference on Natural Language and Speech Processing, page 282--287. Association for Computational Linguistics, (2022)
F. Arnold, and R. Jäschke. Proceedings of the Workshop Understanding LIterature references in academic full TExt at JCDL 2022, volume 3220 of ULITE-ws '22, page 7--15. CEUR Workshop Proceedings, (2022)