Abstract. Relation extraction is to identify the relations between pairs of named entities. In this paper, we try to solve the problem of relation extraction by discovering dependency tree patterns (a pattern is an embedded sub dependency tree indicating a relation instance). Our approach is to find an optimal rule (pattern) set automatically based on the proposed dependency tree pattern mining algorithm. The experimental results show that the extracted patterns can achieve a high precision and a reasonable recall rate when used as rules to extract relation instances. Furthermore, an additional experiment shows that other machine learning based relation extraction methods can also benefit from the extracted patterns by using them as feature...
Information Extraction (IE) has become an indispensable tool in our quest to handle the data deluge ...
Most existing methods determine relation types only after all the entities have been recognized, thu...
Dependency analysis can assist neural networks to capture semantic features within a sentence for en...
Unsupervised Relation Extraction (URE) is the task of extracting relations of a priori unknown seman...
Dependency syntax has long been recognized as a crucial source of features for relation extraction. ...
Abstract. Relation extraction is a part of Information Extraction and an established task in Natural...
This paper presents a novel approach to the semi-supervised learning of Information Extraction pat...
An important step for understanding the semantic content of text is the extraction of semantic relat...
Relation extraction has been considered as one of the most popular topics nowadays, thanks for its c...
We consider the task of KBP slot filling – extracting relation information from newswire documents f...
The recent art in relation extraction is distant supervision which generates training data by heuris...
The surge in information in the form of textual data demands automated systems to extract structured...
In this thesis, we study the importance of background knowledge in relation extraction systems. We n...
Thesis (Ph.D.)--University of Washington, 2012The ability to automatically convert natural language ...
In order for relation extraction systems to obtain human-level performance, they must be able to inc...
Information Extraction (IE) has become an indispensable tool in our quest to handle the data deluge ...
Most existing methods determine relation types only after all the entities have been recognized, thu...
Dependency analysis can assist neural networks to capture semantic features within a sentence for en...
Unsupervised Relation Extraction (URE) is the task of extracting relations of a priori unknown seman...
Dependency syntax has long been recognized as a crucial source of features for relation extraction. ...
Abstract. Relation extraction is a part of Information Extraction and an established task in Natural...
This paper presents a novel approach to the semi-supervised learning of Information Extraction pat...
An important step for understanding the semantic content of text is the extraction of semantic relat...
Relation extraction has been considered as one of the most popular topics nowadays, thanks for its c...
We consider the task of KBP slot filling – extracting relation information from newswire documents f...
The recent art in relation extraction is distant supervision which generates training data by heuris...
The surge in information in the form of textual data demands automated systems to extract structured...
In this thesis, we study the importance of background knowledge in relation extraction systems. We n...
Thesis (Ph.D.)--University of Washington, 2012The ability to automatically convert natural language ...
In order for relation extraction systems to obtain human-level performance, they must be able to inc...
Information Extraction (IE) has become an indispensable tool in our quest to handle the data deluge ...
Most existing methods determine relation types only after all the entities have been recognized, thu...
Dependency analysis can assist neural networks to capture semantic features within a sentence for en...