SentiWordNet is a lexical resource for opinion mining. SentiWordNet assigns to each synset of WordNet three sentiment scores: positivity, negativity, objectivity.
Online Demo of the TreeTagger. A tool for annotating text with part-of-speech and lemma information which has been developed at the Institute for Computational Linguistics of the University of Stuttgart.
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.
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.
ASV Toolbox is a modular collection of tools for the exploration of written language data. They work either on word lists or text and solve several linguistic classification and clustering tasks. The topics covered contain language detection, POS-tagging, base form reduction, named entity recognition, and terminology extraction.
MuNPEx is a multi-lingual noun phrase (NP) extraction component developed for the GATE architecture, implemented in JAPE. It currently supports English, German, French, and Spanish (in beta).
MuNPEx requires a part-of-speech (POS) tagger to work and can additionally use detected named entities (NEs) to improve chunking performance. Please read the documentation (or source code) for more details.
The TreeTagger is a tool for annotating text with part-of-speech and lemma information which has been developed within the TC project at the Institute for Computational Linguistics of the University of Stuttgart. The TreeTagger has been successfully used to tag German, English, French, Italian, Dutch, Spanish, Bulgarian, Russian, Greek, Portuguese, Chinese and old French texts and is easily adaptable to other languages if a lexicon and a manually tagged training corpus are available.
G. Fung, J. Yu, and W. Lam. Computational Intelligence for Financial Engineering, 2003. Proceedings. 2003 IEEE International Conference on, (20-23 March 2003)
E. Riloff. Connectionist, statistical, and symbolic approaches to learning for natural language processing, 1040, page 275--289. Heidelberg, DE, Springer Verlag, (1996)
X. Wan, and J. Yang. SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, page 143--150. New York, NY, USA, ACM, (2007)
G. Siolas, and F. d'Alché Buc. IJCNN '00: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5, page 5205. Washington, DC, USA, IEEE Computer Society, (2000)
X. Li, and D. Roth. Proceedings of the 19th international conference on Computational linguistics, page 1--7. Morristown, NJ, USA, Association for Computational Linguistics, (2002)
J. Kleinberg. KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, page 91--101. New York, NY, USA, ACM, (2002)
R. Swan, and J. Allan. CIKM '99: Proceedings of the eighth international conference on Information and knowledge management, page 38--45. New York, NY, USA, ACM, (1999)