Social relation extraction from texts using a support-vector-machine-based dependency trigram kernel

Choi, Maengsik; Kim, Harksoo

doi:10.1016/j.ipm.2012.04.002

Cited by 21 publications

(7 citation statements)

References 17 publications

(23 reference statements)

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“…To extract personae social relations from the ACE corpus and Korean news, Choi and Kim [31] proposed the DTK algorithm based on the SVM. They divided the relation extraction process into two phases.…”

Section: B Dtk Algorithmmentioning

confidence: 99%

Extracting Deep Personae Social Relations in Microblog Posts

Yang

et al. 2020

IEEE Access

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Numerous studies have been conducted to extract relationships from different documents. However, extracting relationships from microblog posts is rarely studied. In this paper, we improve a novel kernel-based learning algorithm to mine the personae social relationships from microblog posts by combining the syntax and semantic meanings of the dependency trigram kernels (DTK). To deeply extract the personal social relationships of microblog posts, we define the relation feature words, provide seven rules for extracting these feature words, and propose a rule-based approach that mines these relation feature words from microblog posts. We construct relation feature word dictionaries for different relation types because of the lack of prominent relation features in microblog posts. We propose an algorithm to classify relation feature words by considering two features of the relation feature words, namely, syntax and semantic similarities between relation feature words in microblog posts and by using relation feature word dictionaries. Experimental results show that the average recall, precision, and F-measure of our proposed approach outperforms the original DTK in sentence selection, personae social relation extraction, and personae social relation classification. Finally, the relation graphs of five topics clarify that our proposed approach is effective for extracting personae social relations from microblog posts.

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Section: B Dtk Algorithmmentioning

confidence: 99%

Extracting Deep Personae Social Relations in Microblog Posts

Yang

et al. 2020

IEEE Access

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“…These proposed methods can be classified into three categories: supervised learning, semi-supervised learning and unsupervised learning. The most typical methods of supervised learning are kernel-based methods [10][11][12][13][14][15]. Although supervised learning-based methods perform very well, their performance much relies on the availability of a large amounts of manually labeled data.…”

Section: Entity Relation Extractionmentioning

confidence: 99%

RGloVe: An Improved Approach of Global Vectors for Distributional Entity Relation Representation

et al. 2017

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Abstract:Most of the previous works on relation extraction between named entities are often limited to extracting the pre-defined types; which are inefficient for massive unlabeled text data. Recently; with the appearance of various distributional word representations; unsupervised methods for many natural language processing (NLP) tasks have been widely researched. In this paper; we focus on a new finding of unsupervised relation extraction; which is called distributional relation representation. Without requiring the pre-defined types; distributional relation representation aims to automatically learn entity vectors and further estimate semantic similarity between these entities. We choose global vectors (GloVe) as our original model to train entity vectors because of its excellent balance between local context and global statistics in the whole corpus. In order to train model more efficiently; we improve the traditional GloVe model by using cosine similarity between entity vectors to approximate the entity occurrences instead of dot product. Because cosine similarity can convert vector to unit vector; it is intuitively more reasonable and more easily converge to a local optimum. We call the improved model RGloVe. Experimental results on a massive corpus of Sina News show that our proposed model outperforms the traditional global vectors. Finally; a graph database of Neo4j is introduced to store these relationships between named entities. The most competitive advantage of Neo4j is that it provides a highly accessible way to query the direct and indirect relationships between entities.

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“…Choi and Kim [10] propose DTK to extract people social relations from ACE corpus and Korean news. They divide relation extraction process into two phases, firstly, is select sentences which are contain relations, second is identify the relation name.…”

Section: Relate Workmentioning

confidence: 99%

Extracting Personae Interactive Relation in Chinese Microblog Based on an Improved Dependency Trigram Kernel

Yang¹,

Du²,

Meng³

2016

dtetr

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Abstract:As the rapid development of social media, microblog has arrested a lot of attention. However, the automatic information extraction task of the microblog is relatively rare because the microblog text is quite complex and irregular. In this paper, we utilized dependency trigram kernel to construct persons relations extraction model. Firstly, we described the dependency trigram kernel (DTK) for relations extraction. Secondly, we used words semantic similarity tool HowNet to improve semantic similarity of dependency trigram's words. And then we proposed "(POS,GR) (Part of Speech,Grammatical role)" pair to improve dependency trigram's words syntax similarity. Finally, we evaluated the validity of the relation extraction model by experimenting, and the results of experiment show that the F-value of our improved DTK is higher than original DTK for microblog persons relation extraction.

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Social relation extraction from texts using a support-vector-machine-based dependency trigram kernel

Cited by 21 publications

References 17 publications

Extracting Deep Personae Social Relations in Microblog Posts

Extracting Deep Personae Social Relations in Microblog Posts

RGloVe: An Improved Approach of Global Vectors for Distributional Entity Relation Representation

Extracting Personae Interactive Relation in Chinese Microblog Based on an Improved Dependency Trigram Kernel

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