EMNLP
2007
13 years 6 months ago
2007
We propose a sequence-alignment based method for detecting and disambiguating coordinate conjunctions. In this method, averaged perceptron learning is used to adapt the substituti...
LREC
2010
13 years 6 months ago
2010
Named Entity Recognition is a relatively well-understood NLP task, with many publicly available training resources and software for English. Other languages tend to be underserved...
LREC
2010
13 years 6 months ago
2010
Automatic acquisition of novel compounds is notoriously difficult because most novel compounds have relatively low frequency in a corpus. The current study proposes a new method t...
COLING
2008
13 years 6 months ago
2008
This paper presents a novel approach to the task of semantic role labelling for event nominalisations, which make up a considerable fraction of predicates in running text, but are...
COLING
2008
13 years 6 months ago
2008
Most studies in statistical or machine learning based authorship attribution focus on two or a few authors. This leads to an overestimation of the importance of the features extra...
AAAI
2010
13 years 6 months ago
2010
Constructing models of mobile agents can be difficult without domain-specific knowledge. Parametric models flexible enough to capture all mobility patterns that an expert believes...
ACL
2008
13 years 6 months ago
2008
This paper addresses a new task in sentiment classification, called multi-domain sentiment classification, that aims to improve performance through fusing training data from multi...
ACL
2008
13 years 6 months ago
2008
A well-recognized limitation of research on supervised sentence compression is the dearth of available training data. We propose a new and bountiful resource for such training dat...
AUSAI
2008
Springer
13 years 6 months ago
2008
Springer
Traditional text classification algorithms are based on a basic assumption: the training and test data should hold the same distribution. However, this identical distribution assum...
FLAIRS
2008
13 years 6 months ago
2008
Building useful classification models can be a challenging endeavor, especially when training data is imbalanced. Class imbalance presents a problem when traditional classificatio...