Semantic Expectation-Based Causation Knowledge Extraction: A Study on Hong Kong Stock Movement Analysis

Low, Boon Toh; Chan, Ki; Choi, Lei-Lei; Chin, Man-Yee; Lay, Sin-Ling

doi:10.1007/3-540-45357-1_15

Cited by 19 publications

(12 citation statements)

References 3 publications

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“…In recent years, there have been several attempts at automatically acquiring causal knowledge from document collections [Garcia 1997;Satou et al 1999;Khoo et al 2000;Low et al 2001;Girju and Moldovan 2002;Terada 2003;Torisawa 2003]. First, in this section, we introduce four studies of causal knowledge acquisition, three of which make use of cue phrases as we do and one of which makes use of a statistical technique.…”

Section: Related Workmentioning

confidence: 99%

Acquiring causal knowledge from text using the connective marker tame

Inui

Matsumoto

2005

ACM Transactions on Asian Language Information Processing

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In this paper, we deal with automatic knowledge acquisition from text, specifically the acquisition of causal relations. A causal relation is the relation existing between two events such that one event causes (or enables) the other event, such as "hard rain causes flooding" or "taking a train requires buying a ticket." In previous work these relations have been classified into several types based on a variety of points of view. In this work, we consider four types of causal relations-cause, effect, precond(ition) and means-mainly based on agents' volitionality, as proposed in the research field of discourse understanding. The idea behind knowledge acquisition is to use resultative connective markers, such as "because," "but," and "if " as linguistic cues. However, there is no guarantee that a given connective marker always signals the same type of causal relation. Therefore, we need to create a computational model that is able to classify samples according to the causal relation. To examine how accurately we can automatically acquire causal knowledge, we attempted an experiment using Japanese newspaper articles, focusing on the resultative connective "tame." By using machine-learning techniques, we achieved 80% recall with over 95% precision for the cause, precond, and means relations, and 30% recall with 90% precision for the effect relation. Furthermore, the classification results suggest that one can expect to acquire over 27,000 instances of causal relations from 1 year of Japanese newspaper articles.

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Section: Related Workmentioning

confidence: 99%

Acquiring causal knowledge from text using the connective marker tame

Inui

Matsumoto

2005

ACM Transactions on Asian Language Information Processing

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“…Regarding text mining in the financial domain, Koppel et al proposed a labeling method for classifying news stories as bad or good from companies' stock price changes using text mining [14]. Low et al proposed a semantic expectation-based knowledge extraction (SEKE) methodology for extracting causal relations from texts, such as news, and used an electronic thesaurus, such as WordNet [15], to extract terms representing market movement [16]. Schumaker et al tested a machine learning method with different textual representations to predict stock prices using financial news articles [17].…”

Section: Related Workmentioning

confidence: 99%

Related Stocks Selection with Data Collaboration Using Text Mining

et al. 2019

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We propose an extended scheme for selecting related stocks for themed mutual funds. This scheme was designed to support fund managers who are building themed mutual funds. In our preliminary experiments, building a themed mutual fund was found to be quite difficult. Our scheme is a type of natural language processing method and based on words extracted according to their similarity to a theme using word2vec and our unique similarity based on co-occurrence in company information. We used data including investor relations and official websites as company information data. We also conducted several other experiments, including hyperparameter tuning, in our scheme. The scheme achieved a 172% higher F1 score and 21% higher accuracy than a standard method. Our research also showed the possibility that official websites are not necessary for our scheme, contrary to our preliminary experiments for assessing data collaboration.

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“…Previous studies focus mainly on extracting cause‐effect relations (Girju 2003; Chang and Choi 2006) and underlying semantic information in a text (Marcu and Echihabi 2002; Persing and Ng 2009). While automatic detection of causal relation has been studied intensively in specific domains (Low et al 2001; Persing and Ng 2009), only a handful of works focus on open domains (Marcu and Echihabi 2002; Girju 2003; Blanco et al 2008).…”

Section: Related Workmentioning

confidence: 99%

DETECTING EMOTION CAUSES WITH A LINGUISTIC RULE‐BASED APPROACH¹

Lee

Chen

Huang

et al. 2012

Computational Intelligence

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Most theories of emotion treat recognition of a triggering cause event as an integral part of emotion processing. This paper proposes emotion cause detection as a new research area in emotion processing. As a first step toward fully automatic inference of emotion‐cause correlation, we propose a text‐driven, rule‐based approach to emotion cause detection in Chinese. First, we constructed a Chinese emotion cause annotated corpus based on our proposed annotation scheme. Next, we analyzed the corpus data, which yielded the identification of seven groups of linguistic cues and two sets of generalized linguistic rules for the detection of emotion causes. We then developed a rule‐based system for emotion cause detection based on the linguistic rules. In addition, we proposed an evaluation scheme with two phases for performance assessment. The results of our experiments show that our system achieved a promising performance for cause occurrence detection, as well as for cause event detection. The current study should lay the groundwork for future research on the inferences of implicit information and the discovery of new information based on cause‐event relation.

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Semantic Expectation-Based Causation Knowledge Extraction: A Study on Hong Kong Stock Movement Analysis

Cited by 19 publications

References 3 publications

Acquiring causal knowledge from text using the connective marker tame

Acquiring causal knowledge from text using the connective marker tame

Related Stocks Selection with Data Collaboration Using Text Mining

DETECTING EMOTION CAUSES WITH A LINGUISTIC RULE‐BASED APPROACH¹

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Semantic Expectation-Based Causation Knowledge Extraction: A Study on Hong Kong Stock Movement Analysis

Cited by 19 publications

References 3 publications

Acquiring causal knowledge from text using the connective marker tame

Acquiring causal knowledge from text using the connective marker tame

Related Stocks Selection with Data Collaboration Using Text Mining

DETECTING EMOTION CAUSES WITH A LINGUISTIC RULE‐BASED APPROACH1

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DETECTING EMOTION CAUSES WITH A LINGUISTIC RULE‐BASED APPROACH¹