CSTParser is a multi-document discourse parser. Based on machine learning techniques and hand-crafted rules, the system identifies a set of relations predicted by CST (Cross-document Structure Theory) among sentences of different texts on the same topic.
SENTER is a SENtence splitTER for Portuguese.
GistSumm (GIST SUMMarizer) is a summarization tool for Portuguese. It uses the gist as a guideline to identify and select text segments to include in the final extract. Automatically produced extracts have been evaluated under the light of gist preservation and textuality.
Monolingual concordancer is a language independent concordancer tool. Note that the tool is also able to be used as a bilingual concordancer. Several corpora are also included in this resource.
This is a workflow that is designed especially for use in the UIMA-based U-Compare workbench (see separate META-SHARE record). The workflow is in "ucz" format (specific to U-Compare) and can be imported via the "Import Workflow" item in the "Workflows" menu of the U-Compare interface. It include...
MSTParser is a non-projective dependency parser (see McDonald et al., 2005a, 2006) that searches for maximum spanning trees over directed graphs. Models of dependency structure are based on large-margin discriminative training methods (see McDonald et al., 2005b). Projective parsing is also suppo...
FORMA is a probabilistic tool for morphological tagging and lemmatization of text. The purpose of this tool is to obtain annotated text to be processed by other NLP tools (see Gonzalez et al., 2006).
Bilingual concordancer is a language independent concordancer tool for bilingual concordancing, translation revision, post-editing, etc. Note that the tool is also able to be used as a monolingual concordancer. Several corpora are also included in this resource.
The GENIA tagger analyzes English sentences and outputs the base forms, part-of-speech tags, chunk tags, and named entity tags. The tagger is specifically tuned for biomedical text such as MEDLINE abstracts.
TinySVM is an implementation of Support Vector Machines (SVMs) (Vapnik, 1995; Vapnik, 1998) for the problem of pattern recognition.