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Long-distant Coreference Resolution by Clustering-extended BERT for Korean and English Document
Cheolhun Heo, Kuntae Kim, Key-sun Choi
http://doi.org/10.5626/JOK.2020.47.12.1126
Coreference resolution is a natural language processing task of identifying all mentions that refer to the same denotation in the given natural language document. It contributes to improving the performance of various natural language processing tasks by resolving the co-referents caused by linguistically replaceable realizations by using the referencible forms such as pronouns, indicative adjectives, and abbreviations but preventing co-referencing of homonyms (i.e., same form but different meaning). We propose a novel approach to coreference resolution particulary to identify the long-distant co-referents by applying long-distance clustering for surface forms under a BERT-based model performing well in English. We compare the performance of the proposed model and other models over the Korean and English datasets. Results demonstrated that our model has a better grasp of contextual elements compared to the other models.
Knowledge Base Population Model Using Non-Negative Matrix Factorization
Jiho Kim, Sangha Nam, Key-Sun Choi
http://doi.org/10.5626/JOK.2018.45.9.918
The purpose of a knowledge base is to incorporate all the knowledge in the world in a format that machines can understand. In order for a knowledge base to be useful, it must continuously acquire and add new knowledge. However, it cannot if it lacks knowledge-acquisition ability. Knowledge is mainly acquired by analyzing natural language sentences. However, studies on internal knowledge acquisition are being neglected. In this paper, we introduce a non-negative matrix factorization method for knowledge base population. The model introduced in this paper transforms a knowledge base into a matrix and then learns the latent feature vector of each entity tuple and relation by decomposing the matrix and reassembling the vectors to score the reliability of the new knowledge. In order to demonstrate the effectiveness and superiority of our method, we present results of experiments and analysis performed with Korean DBpedia.
Multi-sense Word Embedding to Improve Performance of a CNN-based Relation Extraction Model
Sangha Nam, Kijong Han, Eun-kyung Kim, Sunggoo Kwon, Yoosung Jung, Key-Sun Choi
http://doi.org/10.5626/JOK.2018.45.8.816
The relation extraction task is to classify a relation between two entities in an input sentence and is important in natural language processing and knowledge extraction. Many studies have designed a relation extraction model using a distant supervision method. Recently the deep-learning based relation extraction model became mainstream such as CNN or RNN. However, the existing studies do not solve the homograph problem of word embedding used as an input of the model. Therefore, model learning proceeds with a single embedding value of homogeneous terms having different meanings; that is, the relation extraction model is learned without grasping the meaning of a word accurately. In this paper, we propose a relation extraction model using multi-sense word embedding. In order to learn multi-sense word embedding, we used a word sense disambiguation module based on the CoreNet concept, and the relation extraction model used CNN and PCNN models to learn key words in sentences.
Linking Korean Predicates to Knowledge Base Properties
Yousung Won, Jongseong Woo, Jiseong Kim, YoungGyun Hahm, Key-Sun Choi
Relation extraction plays a role in for the process of transforming a sentence into a form of knowledge base. In this paper, we focus on predicates in a sentence and aim to identify the relevant knowledge base properties required to elucidate the relationship between entities, which enables a computer to understand the meaning of a sentence more clearly. Distant Supervision is a well-known approach for relation extraction, and it performs lexicalization tasks for knowledge base properties by generating a large amount of labeled data automatically. In other words, the predicate in a sentence will be linked or mapped to the possible properties which are defined by some ontologies in the knowledge base. This lexical and ontological linking of information provides us with a way of generating structured information and a basis for enrichment of the knowledge base.
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