NDLI: Questions to be asked & answered as to NLP's role in improving semantic annotation

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Questions to be asked & answered as to NLP's role in improving semantic annotation

Content Provider	ACM Digital Library
Author	Liddy, Elizabeth D.
Abstract	In the realm of Information Retrieval, why is Semantic Annotation needed? What has changed? Is it the users, the sources, the genres, the technologies, the applications, the queries? If there are differences, why and how can Semantic Annotation help? And more specifically, how and what is Natural Language Processing (NLP) contributing. This talk will share some practical use cases of where and how NLP-based semantic annotation has demonstrated its utility (or a solid promise of utility) in some information access tasks, by virtue of the depth and richness of annotation possible with today's more powerful NLP technologies. What is showing promise is the ability to understand how to utilize the higher levels of language processing to do Semantic Annotation. This is largely through the introduction of the Pragmatic level of language processing -- the functional perspective which provides the extra understanding that comes from the study of language in actual use. Pragmatics is concerned with the aspects of language which require context to be understood. Basically, how situational context is lexicalized and grammaticalized. In Pragmatics, the goal is to recognize the extra meaning that humans read into utterances, which other levels of language processing have not recognized as being encoded in them. Semantic Annotation can then go the next step and amplify current annotations with this additional contextual & intentional knowledge. Examples will be shown of what is being done with NLP today that couldn't be done, or simply wasn't being done in earlier days of IR. In applications of keenest interest today, there is an increased relative emphasis focus on dialogue, interaction, real-time, social, and exploratory search, where understanding the user's intent or plan in their query is key. Applications in exploratory search, eDiscovery, sentiment recognition, collaborative search, along with very, very large scale medical insurance consumer applications will be considered in terms of how and what Semantic Annotations can improve by adding more advanced levels of Natural Language Processing.
Starting Page	1
Ending Page	2
Page Count	2
File Format	PDF
ISBN	9781450303729
DOI	10.1145/1871962.1871963
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-10-30
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Retrieval Semantic Information Annotation
Content Type	Text
Resource Type	Article

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