A dependency parser analyzes the grammatical structure of a sentence, establishing relationships between "head" words and words which modify those heads.
Shalmaneser is a supervised learning toolbox for shallow semantic parsing, i.e. the automatic assignment of semantic classes and roles to text. The system was developed for Frame Semantics; thus we use Frame Semantics terminology and call the classes frames and the roles frame elements. However, the architecture is reasonably general, and with a certain amount of adaption, Shalmaneser should be usable for other paradigms (e.g., PropBank roles) as well. Shalmaneser caters both for end users, and for researchers.
Stanford CoreNLP provides a set of human language technology tools. It can give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases and syntactic dependencies, indicate which noun phrases refer to the same entities, indicate sentiment, extract particular or open-class relations between entity mentions, get the quotes people said, etc.
OpenNLP is an organizational center for open source projects related to natural language processing. It hosts a variety of java-based NLP tools which perform sentence detection, tokenization, pos-tagging, chunking and parsing, named-entity detection, and coreference using the OpenNLP Maxent machine learning package.
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M. de Marneffe, B. MacCartney, и C. Manning. Proceedings of the IEEE / ACL 2006 Workshop on Spoken Language Technology, The Stanford Natural Language Processing Group, (2006)
R. Snow, D. Jurafsky, и A. Ng. Proceedings of the 44 th Annual Meeting of the Association for Computational Linguistics, The Stanford Natural Language Processing Group, (2006)Received Best Paper Award.
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