3 research outputs found
Can Subcategorisation Probabilities Help a Statistical Parser?
Research into the automatic acquisition of lexical information from corpora
is starting to produce large-scale computational lexicons containing data on
the relative frequencies of subcategorisation alternatives for individual
verbal predicates. However, the empirical question of whether this type of
frequency information can in practice improve the accuracy of a statistical
parser has not yet been answered. In this paper we describe an experiment with
a wide-coverage statistical grammar and parser for English and
subcategorisation frequencies acquired from ten million words of text which
shows that this information can significantly improve parse accuracy.Comment: 9 pages, uses colacl.st
D7.1. Criteria for evaluation of resources, technology and integration.
This deliverable defines how evaluation is carried out at each integration cycle in the PANACEA project. As PANACEA aims at producing large scale resources, evaluation becomes a critical and challenging issue. Critical because it is important to assess the quality of the results that should be delivered to users. Challenging because we prospect rather new areas, and through a technical platform: some new methodologies will have to be explored or old ones to be adapted
D6.1: Technologies and Tools for Lexical Acquisition
This report describes the technologies and tools to be used for Lexical Acquisition in PANACEA. It includes descriptions of existing technologies and tools which can be built on and improved within PANACEA, as well as of new technologies and tools to be developed and integrated in PANACEA platform. The report also specifies the Lexical Resources to be produced. Four main areas of lexical acquisition are included: Subcategorization frames (SCFs), Selectional Preferences (SPs), Lexical-semantic Classes (LCs), for both nouns and verbs, and Multi-Word Expressions (MWEs)