Extracting Causation Knowledge from Natural Language Texts

Chan, Ki; Low, Boon Toh; Lam, Wai; Lam, K.P.

doi:10.1007/3-540-47887-6_55

Cited by 19 publications

(28 citation statements)

References 6 publications

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“…The second approach describes events in a syntax-driven manner, where the event text is transformed into syntax-based components, such as noun phrases (Garcia, 1997;Khoo et al, 2000;Girju & Moldovan, 2002;Chan & Lam, 2005). In our example, this representation again erroneously finds the second and third events to be most similar due to the syntactic similarity between them.…”

Section: Event Representationmentioning

confidence: 97%

Learning to Predict from Textual Data

Radinsky¹,

Davidovich²,

Markovitch³

2012

jair

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Given a current news event, we tackle the problem of generating plausible predictions of future events it might cause. We present a new methodology for modeling and predicting such future news events using machine learning and data mining techniques. Our Pundit algorithm generalizes examples of causality pairs to infer a causality predictor. To obtain precisely labeled causality examples, we mine 150 years of news articles and apply semantic natural language modeling techniques to headlines containing certain predefined causality patterns. For generalization, the model uses a vast number of world knowledge ontologies. Empirical evaluation on real news articles shows that our Pundit algorithm performs as well as non-expert humans

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Section: Event Representationmentioning

confidence: 97%

Learning to Predict from Textual Data

Radinsky¹,

Davidovich²,

Markovitch³

2012

jair

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“…[1][2][3] In this approach, a set of generic templates organized in a hierarchical fashion is designed. In this section, we present in detail the characteristics and structures of each kind of template and how the templates are organized to facilitate the causation knowledge extraction.…”

Section: Semantic Expectation-based Knowledge Extractionmentioning

confidence: 99%

“…Our framework is called SEKE (Semantic Expectation-based Knowledge Extraction), which is a semantic expectation-based knowledge extraction system. [1][2][3] The framework of SEKE consists of different kinds of generic templates organized in a hierarchical fashion. The topmost level is the semantic template of target relation and is domain independent.…”

Section: Introductionmentioning

confidence: 99%

Extracting causation knowledge from natural language texts

Chan

Lam

2005

Int. J. Intell. Syst.

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SEKE is a semantic expectation-based knowledge extraction system for extracting causation knowledge from natural language texts. It is inspired by human behavior on analyzing texts and capturing information with semantic expectations. The framework of SEKE consists of different kinds of generic templates organized in a hierarchical fashion. There are semantic templates, sentence templates, reason templates, and consequence templates. The design of templates is based on the expected semantics of causation knowledge. They are robust and flexible. The semantic template represents the target relation. The sentence templates act as a middle layer to reconcile the semantic templates with natural language texts. With the designed templates, SEKE is able to extract causation knowledge from complex sentences. Another characteristic of SEKE is that it can discover unseen knowledge for reason and consequence by means of pattern discovery. Using simple linguistic information, SEKE can discover extraction pattern from previously extracted causation knowledge and apply the newly generated patterns for knowledge discovery. To demonstrate the adaptability of SEKE for different domains, we investigate the application of SEKE on two domain areas of news articles, namely the Hong Kong stock market movement domain and the global warming domain. Although these two domain areas are completely different, in respect to their expected semantics in reason and consequence, SEKE can effectively handle the natural language texts in these two domains for causation knowledge extraction.

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“…In terms of knowledge extraction, many kinds of knowledge can be extracted from textual data, such as linguistic knowledge for Natural Language Processing (NLP) 5 and domain-specific lexical and semantic information that may be stored in a database. 6 The technology of Information Extraction 7 is also related in terms of extracting meaningful items from textual data. However, Information Extraction, typically focused in Message Understanding Conferences (MUCs), 8 is intended to find a specified class of events, such as company mergers, and to fill in a template for each instance of such an event.…”

Section: Text Mining Technologymentioning

confidence: 99%