Difference between revisions of "HeadEx"
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== '''HeadEx: Triple Extraction from Stream of News Headlines on Twitter using n-ary Relations''' == | == '''HeadEx: Triple Extraction from Stream of News Headlines on Twitter using n-ary Relations''' == | ||
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| + | Abstract Description: The ever-growing datasets published on Linked Data mainly contain encyclopedic information. However, there is a lack of datasets extracted from unstructured real-time sources. | ||
| + | News Headlines published on Twitter provide a real-time stream of events. | ||
| + | In this paper, we propose an approach for extracting triples, leveraging n-ary relations, | ||
| + | from News Headlines on Twitter in real-time. | ||
| + | First, we introduce a mechanism for representing n-ary relations and their arguments as a | ||
| + | background data model. | ||
| + | This representation leverages Levin's classification of English Verbs in \cite{levin_english_1993} to support the use of unstructured text for constructing the background data model and capturing mentions of | ||
| + | n-ary relations. | ||
| + | Then, we use a learning approach, employing proposed syntactic features derived from parsing, | ||
| + | to extract information respecting the data model. | ||
| + | As a proof-of-concept, we follow a case study containing three distinct n-ary relations. | ||
| + | The results of our experiments are promising and can be used to create timely and | ||
| + | structured news headlines dataset. | ||
Revision as of 16:05, 29 April 2016
HeadEx: Triple Extraction from Stream of News Headlines on Twitter using n-ary Relations
Abstract Description: The ever-growing datasets published on Linked Data mainly contain encyclopedic information. However, there is a lack of datasets extracted from unstructured real-time sources. News Headlines published on Twitter provide a real-time stream of events. In this paper, we propose an approach for extracting triples, leveraging n-ary relations, from News Headlines on Twitter in real-time. First, we introduce a mechanism for representing n-ary relations and their arguments as a background data model. This representation leverages Levin's classification of English Verbs in \cite{levin_english_1993} to support the use of unstructured text for constructing the background data model and capturing mentions of n-ary relations. Then, we use a learning approach, employing proposed syntactic features derived from parsing, to extract information respecting the data model. As a proof-of-concept, we follow a case study containing three distinct n-ary relations. The results of our experiments are promising and can be used to create timely and structured news headlines dataset.
