Different Sense Granularities For Different Applications

Learning to Merge Word Senses

It has been widely observed that different NLP applications require different sense granularities in order to best exploit word sense distinctions, and that for many applications WordNet senses are too fine-grained. In contrast to previously proposed automatic methods for sense clustering, we formulate sense merging as a supervised learning problem, exploiting human-labeled sense clusterings as...

The web as a resource for WSD

Why general Word Sense Disambiguation (WSD) systems are not (to the best of our knowledge) being used in real-world applications? In our opinion, some plausible reasons are: • General Word Sense Disambiguation, as an intermediate task, is frequently harder than the final application. This is probably the case of the most commonly mentioned WSD applications: mono and multilingual Information Ret...

Web services for time granularity reasoning ∗ Extended

We first briefly illustrate the concept of time granularity and review the emerging approaches for modeling and reasoning with it. We then advocate the need for a set of web services that distributed applications can use to define and manipulate time granularities. As an example of these services we discuss in detail the GSTP service, a constraint solver for networks of temporal constraints wit...

Sense Clusters For Information Retrieval: Evidence From Semcor And The EuroWordNet InterLingual Index

We examine three different types of sense clustering criteria with an Information Retrieval application in mind: methods based on the wordnet structure (such as generMization, cousins, sisters...); eooccurrence of senses obtained from Serecot; and equivalent translations of senses in other languages via the EuroWordNet InterLingual Index (ILI). We conclude that a) different NLP applications dem...

Reasoning With Different Time Granularities in Industrial Applications: A Case Study Using CP-logic