Search Results for "lexical"

...The system mines those traces for repeated patterns and turns them into reusable skills that can be shared across a team. It supports agents such as Claude Code, OpenClaw, Codex, Cursor, Hermes, and pi. Hivemind uses hybrid lexical and semantic retrieval so agents can recall prior decisions, solved bugs, migrations, and team-specific workflows. It is useful for engineering teams that want agent behavior to improve over time instead of starting from zero in every session.

...NLTK was originally developed to support research and teaching in computational linguistics and artificial intelligence, and it has become one of the most influential educational platforms for learning NLP in Python. The project also includes access to numerous linguistic corpora and lexical resources that can be downloaded and used directly in experiments and applications.

WQuery is a domain-specific query language designed to process WordNet-like lexical databases. It may be used as a standalone application or as an API to a lexical database in Java based systems.

This is a suite of several software agents to provide a complete architecture of lexical base as proposed in Didier Schwab's PhD. thesis. It will be used for automatic translation, information retrieval and other natural language processing tasks.

Casper, Cascading Parser, is a unification-based natural language parsing library. It uses a syntax based on Lexical Functional Grammar, and is developed in C++. Requires Libxml and STL.

A Python interface to the Wordnet database of word meanings and lexical relationships. allows the user to type expressions such as N['dog'], hyponyms(N['dog'][0]), and closure(ADJ['red'], SYNONYM) to query the database for lexical relationships.

Supertagging is a process of statistical lexical disambiguation, preprocessing step to parsing, which assigns LTAG tree categories to the lexical items present in the input sentence. Thus, if the input sentence is in the form of a dependency tree, the task of the supertagger is to assign the most probable TAG family to each node and edge in the dependency tree.