BibSonomy ::  user :: hkorte ::

Source code to repeat the paper evaluation: We present the first unsupervised approach to the problem of learning a semantic parser, using Markov logic. Our USP system transforms dependency trees into quasi-logical forms, recursively induces lambda forms from these, and clusters them to abstract away syntactic variations of the same meaning. The MAP semantic parse of a sentence is obtained by recursively assigning its parts to lambda-form clusters and composing them. We evaluate our approach by using it to extract a knowledge base from biomedical abstracts and answer questions. USP substantially outperforms TextRunner, DIRT and an informed baseline on both precision and recall on this task.

ConceptNet represents data in the form of a semantic network, and makes it available to be used in natural language processing and intelligent user interfaces.

This is the project page for SecondString, an open-source Java-based package of approximate string-matching techniques. This code was developed by researchers at Carnegie Mellon University from the Center for Automated Learning and Discovery, the Department of Statistics, and the Center for Computer and Communications Security.

This is an overview of the open source NLP and machine learning tools for text mining, information extraction, text classification, clustering, approximate string matching, language parsing and tagging, and more.

Extensible Dependency Grammar (XDG) is a general framework for dependency grammar, with multiple levels of linguistic representations called dimensions, e.g. grammatical function, word order, predicate-argument structure, scope structure, information structure and prosodic structure. It is articulated around a graph description language for multi-dimensional attributed labeled graphs. An XDG grammar is a constraint that describes the valid linguistic signs as n-dimensional attributed labeled graphs, i.e. n-tuples of graphs sharing the same set of attributed nodes, but having different sets of labeled edges. All aspects of these signs are stipulated explicitly by principles: the class of models for each dimension, additional properties that they must satisfy, how one dimension must relate to another, and even lexicalization.

The objective of the ACE Program is to develop extraction technology to support automatic processing of source language data (in the form of natural text, and as text derived from ASR and OCR). This includes classification, filtering, and selection based on the language content of the source data, i.e., based on the meaning conveyed by the data. Thus the ACE program requires the development of technologies that automatically detect and characterize this meaning. The ACE research objectives are viewed as the detection and characterization of Entities, Relations, and Events.

OpenNLP is an organizational center for open source projects related to natural language processing. It hosts a variety of java-based NLP tools which perform sentence detection, tokenization, pos-tagging, chunking and parsing, named-entity detection, and coreference using the OpenNLP Maxent machine learning package.

ASV Toolbox is a modular collection of tools for the exploration of written language data. They work either on word lists or text and solve several linguistic classification and clustering tasks. The topics covered contain language detection, POS-tagging, base form reduction, named entity recognition, and terminology extraction.