Results 31 to 40 of about 3,127 (217)

Revisiting Unsupervised Relation Extraction [PDF]

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020
Unsupervised relation extraction (URE) extracts relations between named entities from raw text without manually-labelled data and existing knowledge bases (KBs). URE methods can be categorised into generative and discriminative approaches, which rely either on hand-crafted features or surface form.
Thy Thy Tran, Phong Le, Sophia Ananiadou
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An Attention-Based Model Using Character Composition of Entities in Chinese Relation Extraction

Information, 2020
Relation extraction is a vital task in natural language processing. It aims to identify the relationship between two specified entities in a sentence. Besides information contained in the sentence, additional information about the entities is verified to
Xiaoyu Han, Yue Zhang, Wenkai Zhang, Tinglei Huang   +3 more
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Latent Relational Model for Relation Extraction

, 2019
Analogy is a fundamental component of the way we think and process thought. Solving a word analogy problem, such as mason is to stone as carpenter is to wood, requires capabilities in recognizing the implicit relations between the two word pairs. In this paper, we describe the analogy problem from a computational linguistics point of view and explore ...
Gaetano Rossiello, Alfio Gliozzo, Nicolas R. Fauceglia, Giovanni Semeraro   +3 more
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Research on the Chinese Named-Entity–Relation-Extraction Method for Crop Diseases Based on BERT

Agronomy, 2022
In order to integrate fragmented text data of crop disease knowledge to solve the current problems of disordered knowledge management, weak correlation and difficulty in knowledge sharing, a Chinese named-entity–relation-extraction model for crop ...
Wenhao Zhang, Chunshan Wang, Huarui Wu, Chunjiang Zhao, Guifa Teng, Sufang Huang, Zhen Liu   +6 more
doaj   +1 more source

Label-Guided relation prototype generation for Continual Relation Extraction [PDF]

PeerJ Computer Science
Continual relation extraction (CRE) aims to extract relations towards the continuous and iterative arrival of new data. To address the problem of catastrophic forgetting, some existing research endeavors have focused on exploring memory replay methods by
Shuang Liu, XunQin Chen, Peng Chen, Simon Kolmanič   +3 more
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Application of Public Knowledge Discovery Tool (PKDE4J) to Represent Biomedical Scientific Knowledge

Frontiers in Research Metrics and Analytics, 2018
In today’s era of information explosion, extracting entities and their relations in large-scale, unstructured collections of text to better represent knowledge has emerged as a daunting challenge in biomedical text mining.
Min Song, Munui Kim, Keunyoung Kang, Yong Hwan Kim, Sieun Jeon   +4 more
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Graph Adaptation Network with Domain-Specific Word Alignment for Cross-Domain Relation Extraction

Sensors, 2020
Cross-domain relation extraction has become an essential approach when target domain lacking labeled data. Most existing works adapted relation extraction models from the source domain to target domain through aligning sequential features, but failed to ...
Zhe Wang, Bo Yan, Chunhua Wu, Bin Wu, Xiujuan Wang, Kangfeng Zheng   +5 more
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A Machine Learning Filter for the Slot Filling Task

Information, 2018
Slot Filling, a subtask of Relation Extraction, represents a key aspect for building structured knowledge bases usable for semantic-based information retrieval.
Kevin Lange Di Cesare, Amal Zouaq, Michel Gagnon, Ludovic Jean-Louis   +3 more
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Semantic Enhanced Distantly Supervised Relation Extraction via Graph Attention Network

Information, 2020
Distantly Supervised relation extraction methods can automatically extract the relation between entity pairs, which are essential for the construction of a knowledge graph.
Xiaoye Ouyang, Shudong Chen, Rong Wang
doaj   +1 more source

Extracting Relations Between Sectors

2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT), 2022
The term "sector" in professional business life is a vague concept since companies tend to identify themselves as operating in multiple sectors simultaneously. This ambiguity poses problems in recommending jobs to job seekers or finding suitable candidates for open positions.
Atakan Kara 0001, F. Serhan Danis, Günce Keziban Orman, Sultan Nezihe Turhan   +3 more
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