Abstract
Entity relation extraction is to find entities and relations from unstructured texts, which is beneficial to the applications of knowledge graphs and question answering systems. The traditional methods handle this task in a pipelined manner which extracts the entities first and then recognizes their relations. This framework may lead to error delivery. In order to tackle this problem, this paper proposes an end-to-end method for joint extraction of Tibetan entity relations which can extract entities and relations at the same time. According to the Tibetan spelling characteristics, this paper processes the Tibetan corpus by word-level and character-level respectively. Combined with part of speech tagging, we use the end-to-end model to convert the entity relation extraction task to the tagging problem. Finally, the experimental results show that the proposed method is better than the baseline.
Highlights
The purpose of entity relation extraction is to extract the semantic relation between entity pairs in a sentence and make unstructured text into structured text
We compare the results of various algorithms in extracting Tibetan entity relations, including the traditional SVM [20] and LR [21] methods
In the method of the neural network, a comprehensive comparison of different processing on Tibetan entities and relations extraction tasks is performed, especially, we use different granularity to process Tibetan and to divide Tibetan according to word-level and character-level, and add part-of-speech tagging and optimize it in the neural network learning
Summary
The purpose of entity relation extraction is to extract the semantic relation between entity pairs in a sentence and make unstructured text into structured text. The Forbidden City is located in the center of Beijing . Entity relation extraction can automatically identify entities The Forbidden City and Beijing as location relation. The extracted result is {The Forbidden City, located in, Beijing}, which called triplet here [1]. The traditional approach, which is called pipeline method, is divided into two steps: named entity recognize (NER) and relation classification (RC)
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