1st Insight Workshop on Distributional Semantics
On May 1st we will have the 1st Insight Workshop on Distributional Semantics where Insighters and guests could learn about and present their work on distributional semantics. Distributional semantics is a quickly evolving research area which focuses on extracting meaning from the statistical analysis of large text collections. These semantic models can be very effective over heterogeneous data and may be applied in different areas including semantic search, sentiment analysis, question answering, knowledge discovery.
The preliminary program is available at the website: https://sites.google.com/site/insightdistsemws/
The workshop will be held at Insight at NUIGalway, and will cover the basic principles and recent advances in theoretical and applied distributional semantics.
We would like to invite all people who are interested in attending the workshop to reply to get in contact. We still have space for new presentations (we have both long (30 min) and short (15 min) presentation slots).
On Distributional Semantics: Efficient means for capturing and representing computational semantics of data are critical for coping with current limitations of information systems, especially if one wants to make sense of large amounts of information coming from heterogeneous and/or poorly structured resources. Efforts aimed at representing meaning of data in a machine-readable way (i.e., Semantic Web or deductive databases) have achieved some level of success. There are alternatives to the top-down, assertional approaches to semantics, which can work even without (too much) expensive human involvement. Distributional semantic models, based on the distributional hypothesis , provide a bottom-up approach to the computational representation of meaning, where the statistical co-occurrence of terms in unstructured corpora can provide a basis for the construction of simplified but comprehensive and extensible models of semantic content. Distributional models can provide flexible models of meaning which work over large-scale and heterogeneous data. Their applications include semantic search, sentiment analysis, question answering, knowledge discovery, among others.
If you have any questions or suggestions contact Andre Freitas. andre.freitas@DERI.ORG