Managed Projects

RelEx Semantic Relationship Extractor

  Analyzed about 6 hours ago

RelEx is an English-language semantic relationship extractor, built on the Carnegie-Mellon Link Grammar parser. It can identify dependency-grammar dependencies, such as subject, object, indirect object and many other relationships between words in a sentence. It can also provide part-of-speech ... [More] tagging, noun-number tagging, verb tense tagging, gender tagging, and so on. Relex includes a basic implementation of the Hobbs anaphora (pronoun) resolution algorithm. RelEx also provides semantic relationship framing, similar to that of FrameNet. [Less]

12.1K lines of code

4 current contributors

about 2 months since last commit

2 users on Open Hub

Moderate Activity
I Use This

LexAt Lexical/Corpus Statistics

  Analyzed 1 day ago

The LexAt "lexical attraction" aka the RelEx Statistical Linguistics package adds statistical algorithms to the RelEx. Corpus statistics, including mutual information, are maintained in an SQL database, and drawn on to enhance various RelEx functions, such as parse ranking and chunk ranking, and word-sense disambiguation (Mihalcea algo).

9.59K lines of code

0 current contributors

over 7 years since last commit

1 users on Open Hub

I Use This

Link Grammar

  Analyzed about 10 hours ago

The Link Grammar Parser is a syntactic parser of English, based on link grammar, an original theory of English syntax. Given a sentence, the system assigns to it a syntactic structure, which consists of a set of labeled links connecting pairs of words. The parser also produces a "constituent" (Penn ... [More] tree-bank style phrase tree) representation of a sentence (showing noun phrases, verb phrases, etc.). [Less]

65.3K lines of code

5 current contributors

11 days since last commit

1 users on Open Hub

High Activity
I Use This


  Analyzed about 17 hours ago

Meta-optimizing semantic evolutionary search (MOSES) is a new approach to program evolution, based on representation-building and probabilistic modeling. MOSES has been successfully applied to solve hard problems in domains such as computational biology, sentiment evaluation, and agent control. ... [More] Results tend to be more accurate, and require less objective function evaluations, in comparison to other program evolution systems. Best of all, the result of running MOSES is not a large nested structure or numerical vector, but a compact and comprehensible program written in a simple Lisp-like mini-language. For more information see: Interested C++ developers, please drop in at #opencog on [Less]

41.5K lines of code

6 current contributors

3 days since last commit

0 users on Open Hub

Very Low Activity
I Use This