Polarity classification of words is important for applications such as Opinion Mining and Sentiment Analysis. A number of sentiment word/sense dictionaries have been manually or ...
Eduard C. Dragut, Hong Wang, Clement T. Yu, A. Pra...
Choosing the right parameters for a word sense disambiguation task is critical to the success of the experiments. We explore this idea for prepositions, an often overlooked word c...
Sense Induction is the process of identifying the word sense given its context, often treated as a clustering task. This paper explores the use of spectral cluster method which in...
Word sense disambiguation is typically phrased as the task of labeling a word in context with the best-fitting sense from a sense inventory such as WordNet. While questions have o...
The vast majority of work on word senses has relied on predefined sense inventories and an annotation schema where each word instance is tagged with the best fitting sense. This p...
Semantic role labeling (SRL) not only needs lexical and syntactic information, but also needs word sense information. However, because of the lack of corpus annotated with both wo...
We propose a method "Interactive Paraphrasing" which enables users to interactively paraphrase words in a document by their definitions, making use of syntactic annotati...
In this paper, we present a new approach for word sense disambiguation (WSD) using an exemplar-based learning algorithm. This approach integrates a diverse set of knowledge source...
This paper presents a method to combine a set of unsupervised algorithms that can accurately disambiguate word senses in a large, completely untagged corpus. Although most of the ...
We describe an attempt to use word sense as an alternate text representation within an information retrieval system in order to enhance retrieval effectiveness. A performance comp...