Cornell AMR Parser The Abstract Meaning Representation (AMR) parser maps English sentences to AMR, a broad-coverage meaning representation. Sentence meaning is represented using directed graphs, where nodes are events and entities, and edges represent binary relations.
Cornell Semantic Parsing Framework (SPF) Cornell SPF is a learning and inference framework for mapping natural language to formal representation of its meaning. SPF uses Combinatory Categorial Grammars (CCG) to map sentences to lambda calculus logical-form meaning representations. The framework includes semantic parsing algorithms, a flexible higher-order typed representation language, and algorithms for supervised and weakly supervised learning. SPF is implemented in Java.
Mallet MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text. MALLET includes sophisticated tools for document classification: efficient routines for converting text to features, a wide variety of algorithms (including Naïve Bayes, Maximum Entropy, and Decision Trees), and code for evaluating classifier performance using several commonly used metrics. In addition to classification, MALLET includes tools for sequence tagging for applications such as named-entity extraction from text. Algorithms include Hidden Markov Models, Maximum Entropy Markov Models, and Conditional Random Fields.
The Cornell Conditional Probability Calculator (CCPC) The CCPC is a tool for computing information-theoretical complexity metrics from formal grammars.