This book offers a highly accessible introduction to Natural Language Processing, the field that underpins a variety of language technologies ranging from predictive text and email filtering to automatic summarization and translation. You'll learn how to write Python programs to analyze the structure and meaning of texts, drawing on techniques from the fields of linguistics and artificial intelligence.
a self-contained specification of a lexical analyzer that uses automated parsing techniques to handle SGML document types limited to a tractable set of SGML features. An implementation is available as well.
TextSTAT is a concordance program which was designed to be user friendly and provide simple Internet functionality. Texts can be combined to form corpora (which can also be stored as such). The program analyses these text corpora and displays word frequency lists and concordances to search terms. The program is written in Python and offered here as a Windows program. TextSTAT is freeware.
Programs listed here can be divided into more subtle groups:
* pure information retrievers: searching and displaying texts, indexers
* concordancers: programs providing concordances
The scripts presented here are not intended to teach programming; I assume you have at least a vague idea about that already. Nor are they intended to exemplify fine coding style. The point is to show how easy things can be, if you pick the right tools.
OpenNLP is an organizational center for open source projects related to natural language processing. Its primary role is to encourage and facilitate the collaboration of researchers and developers on such projects.
MontyLingua is a free*, commonsense-enriched, end-to-end natural language understander for English. Feed raw English text into MontyLingua, and the output will be a semantic interpretation of that text. Perfect for information retrieval and extraction, request processing, and question answering. From English sentences, it extracts subject/verb/object tuples, extracts adjectives, noun phrases and verb phrases, and extracts people's names, places, events, dates and times, and other semantic information.
Latent Semantic Analysis (LSA) is a mathematical method that tries to bring out latent relationships within a collection of documents. Rather than looking at each document isolated from the others it looks at all the documents as a whole and the terms within them to identify relationships.
Open source Python modules, linguistic data and documentation for research and development in natural language processing, supporting dozens of NLP tasks
The Universal Text Parser enables you to link an external data source with the Meta-Directory join engine. With the Universal Text Parser, you can synchronize a wide variety of text-based data with your Meta-Directory views.