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Joint Model of Morphological Analysis and Named Entity Recognition Using Shared Layer

Hongjin Kim, Seongsik Park, Harksoo Kim

http://doi.org/10.5626/JOK.2021.48.2.167

Named entity recognition is a natural language processing technology that finds words with unique meanings such as human names, place names, organization names, dates, and time in sentences and attaches them. Morphological analysis in Korean is generally divided into morphological analysis and part-of-speech tagging. In general, named entity recognition and morphological analysis studies conducted in independently. However, in this architecture, the error of morphological analysis propagates to named entity recognition. To alleviate the error propagation problem, we propose an integrated model using Label Attention Network (LAN). As a result of the experiment, our model shows better performance than the single model of named entity recognition and morphological analysis. Our model also demonstrates better performance than previous integration models.

Relation Extraction among Multiple Entities using Dual-Pointer Network

Seongsik Park, Harksoo Kim

http://doi.org/10.5626/JOK.2019.46.11.1186

Information Extraction is the process of automatically extracting structured information from unstructured machine-readable texts. The rapid increase in large-scale unstructured texts in recent years has led to many studies investigating information extraction. Information extraction consists of two sub-tasks: an entity linking task and a relation extraction task. Most previous studies examining relation extraction have assumed that a single sentence contains a single entity pair mention. They have also focused on extracting a single entity pair (i.e., Subject-Relation-Object triple) per sentence. However, sentences can also contain multiple entity pairs. Therefore, in this paper, we propose a Dual-pointer network model that can entirely extract all possible entity pairs from a given text. In relation extraction experiments with two kinds of representative English datasets, NYT and ACE-2005, the proposed model achieved state-of-the-art performances with an F1-score of 0.8050 in ACE-2005 and an F1-score of 0.7834 in NYT.


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