Automatic extraction of causal chains from text

Huminski, Aliaksandr; Ng, Yan Bin

doi:10.32655/libres.2020.29.2.3

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…Their proposed architecture also reports F-measure of 0.89 and 0.90 for cause and effect sequences on an extension of the SEMEVAL 2010 dataset. In a recently published paper, Huminski et al have employed a rule-based method for automatic extraction of causal chains from text [29]. Extensive survey on extraction of causal relations from natural language text can be found in [6], and in the recently published work by Yang et al in [30].…”

Section: Related Workmentioning

confidence: 99%

A Joint Model for Detecting Causal Sentences and Cause-Effect Relations from Text

Dasgupta

Naskar

Dey

et al. 2022

Towards a Knowledge-Aware AI

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Text documents are rich repositories of causal knowledge. While journal publications typically contain analytical explanations of observations on the basis of scientific experiments conducted by researchers, analyst reports, News articles or even consumer generated text contain not only viewpoints of authors, but often contain causal explanations for those viewpoints. As interest in data science shifts towards understanding causality rather than mere correlations, there is also a surging interest in extracting causal constructs from text to provide augmented information for better decision making. Causality extraction from text is viewed as a relation extraction problem which requires identification of causal sentences as well as detection of cause and effect clauses separately. In this paper, we present a joint model for causal sentence classification and extraction of cause and effect clauses, using a sequence-labeling architecture cascaded with fine-tuned Bidirectional Encoder Representations from Transformers (BERT) language model. The cause and effect clauses are further processed to identify named entities and build a causal graph using domain constraints. We have done multiple experiments to assess the generalizability of the model. It is observed that when fine-tuned with sentences from a mixed corpus, and further trained to solve both the tasks correctly, the model learns the nuances of expressing causality independent of the domain. The proposed model has been evaluated against multiple state-of-the-art models proposed in literature and found to outperform them all.

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Section: Related Workmentioning

confidence: 99%

A Joint Model for Detecting Causal Sentences and Cause-Effect Relations from Text

Dasgupta

Naskar

Dey

et al. 2022

Towards a Knowledge-Aware AI

View full text Add to dashboard Cite

show abstract

Stochastic Diffusion Model for Analysis of Dynamics and Forecasting Events in News Feeds

2021

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One of the problems of forecasting events in news feeds, is the development of models which allow for work with semi structured information space of text documents. This article describes a model for forecasting events in news feeds, which is based on the use of stochastic dynamics of changes in the structure of non-stationary time series in news clusters (states of the information space) on the basis of use of diffusion approximation. Forecasting events in a news feed is based on their text description, vectorization, and finding the cosine value of the angle between the given vector and the centroids of various information space semantic clusters. Changes over time in the cosine value of such angles between the above vector and centroids can be represented as a point wandering on the [0, 1] segment. This segment contains a trap at the event occurrence threshold point, which the wandering point may eventually fall into. When creating the model, we have considered probability patterns of transitions between states in the information space. On the basis of this approach, we have derived a nonlinear second-order differential equation; formulated and solved the boundary value problem of forecasting news events, which allowed obtaining theoretical time dependence on the probability density function of the parameter distribution of non-stationary time series, which describe the information space evolution. The results of simulating the events instance probability dependence on time (with sets of parameter values of the developed model, which have been experimentally determined for already occurred events) show that the model is consistent and adequate (all the news events which have been used for the model verification occur with high values of probability (within the order of 80%), or if these are fictitious events, they can only occur over the course of inadmissible long time).

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Automatic extraction of causal chains from text

Cited by 2 publications

References 8 publications

A Joint Model for Detecting Causal Sentences and Cause-Effect Relations from Text

A Joint Model for Detecting Causal Sentences and Cause-Effect Relations from Text

Stochastic Diffusion Model for Analysis of Dynamics and Forecasting Events in News Feeds

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