MontyLingua: an end-to-end natural language processor with common sense

MontyLingua packages the everyday operations of English processing into one inspectable path: sentence splitting, tokenization, part-of-speech tagging, chunking, lemmatization, semantic extraction, and later surface generation. Its jist interface emits Lisp-shaped verb-subject-object structures that applications can use without assembling a research stack of their own. The original 2.1 distribution survives with Python and Java source, documentation, data files, release archives, and licenses. Its notes report 97 percent word-level tagging accuracy after adding common sense, plus substantial speedups, but do not include the protocol needed to reproduce those figures. Subordinate-clause objects were excluded, generation was beta, and the Python 2-era code depends on companion data files. It is best understood as working software with candid edges, not a benchmark paper.