Abstract
Relation extraction aims to extract semantic relationships between two specified named entities in a sentence. Because a sentence often contains several named entity pairs, a neural network is easily bewildered when learning a relation representation without position and semantic information about the considered entity pair. In this paper, instead of learning an abstract representation from raw inputs, task-related entity indicators are designed to enable a deep neural network to concentrate on the task-relevant information. By implanting entity indicators into a relation instance, the neural network is effective for encoding syntactic and semantic information about a relation instance. Organized, structured and unified entity indicators can make the similarity between sentences that possess the same or similar entity pair and the internal symmetry of one sentence more obviously. In the experiment, a systemic analysis was conducted to evaluate the impact of entity indicators on relation extraction. This method has achieved state-of-the-art performance, exceeding the compared methods by more than 3.7%, 5.0% and 11.2% in F1 score on the ACE Chinese corpus, ACE English corpus and Chinese literature text corpus, respectively.
Highlights
Relation extraction is one of the fundamental information extraction (IE) tasks that aims to identify the semantic relationship between two named entities in a sentence [1]
Unlike sentence classification that makes a prediction based on sentence representation, relation extraction should consider the semantic information between two named entities
Because a sentence often contains several named entities that share the same context, directly making a decision-based sentence representation learned from raw inputs is not effective for supporting relation extraction
Summary
Relation extraction is one of the fundamental information extraction (IE) tasks that aims to identify the semantic relationship between two named entities in a sentence [1]. The neural network is the most popular method to support relation extraction, where multilayer stacked architecture is adopted to support the designated feature transformation, e.g., convolutional neural network (CNN) [9], recurrent neural network (RNN) [10] and attention mechanism [11]. This approach has the advantage of extracting high-order abstract features from raw inputs, avoiding the effort required for the manual generation of designed features. Obtaining entity position information is highly important for a neural network to concentrate on the considered entity pair
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
