Paper Info
Title
Semi-automatic Parsing for Web Knowledge Extraction through Semantic Annotation.
Abstract
Parsing Web information, namely parsing content to fmd relevant documents on the basis of a user's query, represents a crucial step to guarantee fast and accurate Information Retrieval (IR). Generally, an automated approach to such task is considered faster and cheaper than manual systems. Nevertheless, results do not seem have a high level of accuracy, indeed, as also Hjorland (2007) states, using stochastic algorithms entails low precision, low recall and generic results. Usually IR systems are based on invert text index, namely an index data structure storing a mapping from content to its locations in a database file, or in a document or a set of documents. In this paper we propose a system, by means of which we will develop a search engine able to process online documents, starting from a natural language query, and to return information to users. The proposed approach, based on the Lexicon-Grammar (LG) framework and its language formalization methodologies, aims at integrating a semantic annotation process for both query analysis and document retrieval.
Year
Venue
Keywords
2016
LREC 2016 - TENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION
Knowledge Extraction,Semantic Annotation,Parsing
Field
DocType
Citations 
Semantic Web Stack,Semantic annotation,Information retrieval,Computer science,Image retrieval,Semantic Web,Natural language processing,Knowledge extraction,Artificial intelligence,Parsing,Social Semantic Web
Conference
0
PageRank 
References 
Authors
0.34
1
1
Name
Order
Citations
PageRank
Maria Pia di Buono127.15