15,582 research outputs found
Natural language processing
Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems
A CNL for Contract-Oriented Diagrams
We present a first step towards a framework for defining and manipulating
normative documents or contracts described as Contract-Oriented (C-O) Diagrams.
These diagrams provide a visual representation for such texts, giving the
possibility to express a signatory's obligations, permissions and prohibitions,
with or without timing constraints, as well as the penalties resulting from the
non-fulfilment of a contract. This work presents a CNL for verbalising C-O
Diagrams, a web-based tool allowing editing in this CNL, and another for
visualising and manipulating the diagrams interactively. We then show how these
proof-of-concept tools can be used by applying them to a small example
Dublin City University at QA@CLEF 2008
We describe our participation in Multilingual Question Answering at CLEF 2008 using German and English as our source and target languages respectively. The system was built using UIMA (Unstructured Information Management Architecture) as underlying framework
Universal Dependencies Parsing for Colloquial Singaporean English
Singlish can be interesting to the ACL community both linguistically as a
major creole based on English, and computationally for information extraction
and sentiment analysis of regional social media. We investigate dependency
parsing of Singlish by constructing a dependency treebank under the Universal
Dependencies scheme, and then training a neural network model by integrating
English syntactic knowledge into a state-of-the-art parser trained on the
Singlish treebank. Results show that English knowledge can lead to 25% relative
error reduction, resulting in a parser of 84.47% accuracies. To the best of our
knowledge, we are the first to use neural stacking to improve cross-lingual
dependency parsing on low-resource languages. We make both our annotation and
parser available for further research.Comment: Accepted by ACL 201
- …