ESAIR'13: Sixth International Workshop on
Exploiting Semantic Annotations in Information Retrieval
|Workshop Homepage||Call for Papers||Program||Organizers|
Will be published in the ACM Digital Library.
The program consists of invited keynotes, short paper presentations, breakout groups, and a report out and final discussion.
ESAIR 2013 was held on Monday October 28, 2013, in Room Salons A-E at the San Francisco Airport Marriott Waterfront hotel in Burlingame, CA, USA.
The ability to read and understand a text would seem to be a basic aspect of interacting with a rich information source like the Web, yet little is currently known about the nature of the Web, its users, and how users interact with content when seen through the lens of reading difficulty. For example, a document isn't relevant to a person's information need - at least, not immediately - if they can't understand it, yet Web search engines have traditionally ignored the problem of finding or providing content at the right level of difficulty as an aspect of relevance. I'll give an overview of recent research that shows how computing and applying metadata based on text readability at Web scale opens up new and sometimes surprising possibilities for enriching our interactions with the Web: from personalizing Web search results, to predicting user and site expertise, to estimating searcher motivation. I'll also highlight future challenges and opportunities in improving text readability analysis, particularly in light of the rapidly growing interest in large-scale applications for online education.
The organizers of this workshop have issued a call for help, stating that one challenge in using knowledge and semantic annotations in search is that "standard text search excels at shallow information needs expressed by short keyword queries, and so semantic annotation contributes very little". I suggest that the answer is right there in the question, meaning that those who work with deeper knowledge representations should also try to aid more complex information needs. I will talk about search user interfaces, current and future, that have and hopefully will successfully improve user experience by adding in more understanding of content.
Computational approaches to problems in Natural Language Understanding and Information Extraction are often modeled as structured predictions -- predictions that involve assigning values to sets of interdependent variables. Over the last few years, one of the most successful approaches to studying these problems involves Constrained Conditional Models (CCMs), an Integer Learning Programming formulation that augments probabilistic models with declarative constraints as a way to support such decisions. I will focus on exemplifying this framework in the context of developing better semantic analysis of sentences -- Extended Semantic Role Labeling -- and the task of Wikification -- identifying concepts and entities in text and disambiguating them into Wikipedia or other knowledge bases.
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|October 28, 2013||Workshop day during CIKM 2013!|
This workshop will be held as part of the 22nd ACM International Conference on Information and Knowledge Management, San Francisco, 2013. Information on San Francisco can be found in the Wikipedia.