Using dual-layer CRFs for event causal relation extraction

Fu, Jianfeng; Liu, Zongtian; Liu, Wei; Guo, Qiang

doi:10.1587/elex.8.306

Cited by 8 publications

(4 citation statements)

References 4 publications

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“…Event causal relation extraction (ECRE) differs with event evolution relationship identification in this paper [18,19]. The major difference is that they work with different definitions of 'Event'.…”

Section: Event Causal Relation Extractionmentioning

confidence: 99%

A Semantic Aspect-Based Vector Space Model to Identify the Event Evolution Relationship within Topics

Xi¹,

Li²,

Liu³

2015

Journal of Computing Science and Engineering

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Understanding how the topic evolves is an important and challenging task. A topic usually consists of multiple related events, and the accurate identification of event evolution relationship plays an important role in topic evolution analysis. Existing research has used the traditional vector space model to represent the event, which cannot be used to accurately compute the semantic similarity between events. This has led to poor performance in identifying event evolution relationship. This paper suggests constructing a semantic aspect-based vector space model to represent the event: First, use hierarchical Dirichlet process to mine the semantic aspects. Then, construct a semantic aspect-based vector space model according to these aspects. Finally, represent each event as a point and measure the semantic relatedness between events in the space. According to our evaluation experiments, the performance of our proposed technique is promising and significantly outperforms the baseline methods.

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Section: Event Causal Relation Extractionmentioning

confidence: 99%

A Semantic Aspect-Based Vector Space Model to Identify the Event Evolution Relationship within Topics

Xi¹,

Li²,

Liu³

2015

Journal of Computing Science and Engineering

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“…Mani accomplish event relation classification with semi-supervised method, which is based on rule and EM. Fu [8] do some research works on causal relation. MA [9] manually annotated related relation among events for Chinese texts collected, but he only aims at related relation among events.…”

Section: Related Workmentioning

confidence: 99%

CEC2.0-Based Detection Event Relation for Chinese Text

Wang¹,

Liu²,

Liao³

et al. 2015

Proceedings of the 2015 International Conference on Materials Engineering and Information Technology Applications

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Event is the basic unit of knowing, remembering and understanding the real world. Event is regarded as the basic unit of knowledge representation. We detect event relation between two events for some Chinese texts by matching event relation pair table, which are constructed based on the CEC2.0 (called the Chinese Event Corpus). The experimental result indicates that there is a good outcome for detecting event relation with this method, the value of the precision, recall and F-measure respective is 83.6%, 77% and 80.2%. The motivation is to know the dynamic process of event happening and developing in the real world by the event relation in Chinese texts.

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“…In addition, some scholars have studied event causal relation [8][9][10][11][12]. The event causal relation is also a special temporal relation.…”

Section: Introductionmentioning

confidence: 99%

“…Khoo distinguished event causal relation for explicit and implicit relation [9]; Gan proposed a structure analysis method based on event causal relation [10]; Blanco defined causal relation between events and introduced the machine learning technique to extract the marked causal relation [11]. Besides, Fu presented a method for event causal relation extraction by using dual-layer Conditional Random Fields (CRFs) [12].…”

Section: Introductionmentioning

confidence: 99%

Event Relations Extraction Based on Event Co-occurrence Network

Liao

Sun

Liu

2015

2015 8th International Symposium on Computational Intelligence and Design (ISCID)

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Cognitive scientists believe that humans memorize and understand the real world through "event". Event relations extraction has become increasingly important in some natural language processing applications. In this paper, we firstly consider the event as a basic semantic unit and present a new event co-occurrence network. Then, we study event relations extraction based on this event co-occurrence network. We used the association rule mining method to extract event co-occurrence pairs from event co-occurrence networks, and got the semantic relations between event classes after generalizing and analyzing these event co-occurrence pairs. The experimental results show that our event relations extraction method has good performance. Keywords-Event co-occurrence; Event relations extraction; Non-taxonomic relations; Chinese Emergency Corpus I.INTRODUCTION With the in-depth research for the event, more and more people pay attention to the analysis and knowledge mining of event relations in recent years. Event relations extraction has become increasingly important in some natural language processing applications, such as information retrieval, automatic summarization, and question answering and so on.Studies of event relations are mostly focused on taxonomic relations and temporal relations at present, and they are also two kinds of common event relations in texts. The taxonomic relation reflects a static characteristic between events, and the dynamic relation between events is more represented by the temporal relation.As the event taxonomic relation, there have been some typical researches. Vargas-Vera and Celjuska divided event into 41 different types and specified the corresponding slot for each event class, and they established the hierarchical relation between event classes at the same time [1]. VerbNet (an online dictionary) was built by Kipper Karin in 2005, and he studied the taxonomic relation between verbs and spread 5200 verbs over 237 top classes [2]. The ACE evaluation conference divided the event into 8 big classes and 33 sub classes [3].The establishment of events in time order relation is the purpose of studying event temporal relation, which begins with the extraction of time expressions in the text. I. Mani introduced time expressions can be tagged, the temporal structure of events and other related time information extraction [4]. Wang used the method based on machine learning to determine the corresponding time expressions of events and the mapping relation between time information and event information in the text [5]. In recent years,

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Using dual-layer CRFs for event causal relation extraction

Cited by 8 publications

References 4 publications

A Semantic Aspect-Based Vector Space Model to Identify the Event Evolution Relationship within Topics

A Semantic Aspect-Based Vector Space Model to Identify the Event Evolution Relationship within Topics

CEC2.0-Based Detection Event Relation for Chinese Text

Event Relations Extraction Based on Event Co-occurrence Network

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