Automatic Extraction of Causal Relations from Text using Linguistically Informed Deep Neural Networks (original) (raw)
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A survey on extraction of causal relations from natural language text
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As an essential component of human cognition, cause–effect relations appear frequently in text, and curating cause–effect relations from text helps in building causal networks for predictive tasks. Existing causality extraction techniques include knowledge-based, statistical machine learning (ML)-based, and deep learning-based approaches. Each method has its advantages and weaknesses. For example, knowledge-based methods are understandable but require extensive manual domain knowledge and have poor cross-domain applicability. Statistical machine learning methods are more automated because of natural language processing (NLP) toolkits. However, feature engineering is labor-intensive, and toolkits may lead to error propagation. In the past few years, deep learning techniques attract substantial attention from NLP researchers because of its powerful representation learning ability and the rapid increase in computational resources. Their limitations include high computational costs and ...
2020
Causality can be understood as the relationship between two events such that the occurrence of one event results in the occurrence of the other event either directly or indirectly. This paper aims to identify whether the given sentences have a causality effect present in them and to classify the cause and effect words/phrases if present. The approach used for classification uses deep learning algorithms and the annotation task uses machine translation. These models are applied to the dataset provided by CEREX@FIRE2020. The best result for the causality identification part was obtained from BiLSTM with an F1 score of 0.60 and for the second task of annotation as cause and effect, NMT with Bahdanau attention mechanism with an F1 score of 0.44.
Causality Mining in Natural Languages Using Machine and Deep Learning Techniques: A Survey
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The era of big textual corpora and machine learning technologies have paved the way for researchers in numerous data mining fields. Among them, causality mining (CM) from textual data has become a significant area of concern and has more attention from researchers. Causality (cause-effect relations) serves as an essential category of relationships, which plays a significant role in question answering, future events predication, discourse comprehension, decision making, future scenario generation, medical text mining, behavior prediction, and textual prediction entailment. While, decades of development techniques for CM are still prone to performance enhancement, especially for ambiguous and implicitly expressed causalities. The ineffectiveness of the early attempts is mainly due to small, ambiguous, heterogeneous, and domain-specific datasets constructed by manually linguistic and syntactic rules. Many researchers have deployed shallow machine learning (ML) and deep learning (DL) te...
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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.
Automatic Extraction of Causal Relations from Natural Language Texts: A Comprehensive Survey
Automatic extraction of cause-effect relationships from natural language texts is a challenging open problem in Artificial Intelligence. Most of the early attempts at its solution used manually constructed linguistic and syntactic rules on small and domain-specific data sets. However, with the advent of big data, the availability of affordable computing power and the recent popularization of machine learning, the paradigm to tackle this problem has slowly shifted. Machines are now expected to learn generic causal extraction rules from labelled data with minimal supervision, in a domain independent-manner. In this paper, we provide a comprehensive survey of causal relation extraction techniques from both paradigms, and analyse their relative strengths and weaknesses, with recommendations for future work.
2020
Cause-effect relation extraction is the problem of detecting causal relations expressed in a text. The extraction of causal-relations from texts might be beneficial for the improvement of various natural language processing (NLP) tasks including Q/A, text-summarization, opinion mining, and event analysis. However, cause-effect relation in the text is sparse, ambiguous, sometimes implicit, and has a linguistically complex construct. To address these challenges FIRE-2020 introduced a shared task focusing on cause-effect relation extraction (CEREX). We propose a feature based supervised classification model with a naive rule-based classifier. We define a set of rules based on a causal connective dictionary and stop-words. Besides, we use a fusion of hand-crafted features and transfer learning embeddings to train our SVM based supervised classification model. Experimental results exhibit that our proposed method achieved the topnotch performance for cause-effect relation extraction and ...
LangResearchLab_NC at FinCausal 2020, Task 1: A Knowledge Induced Neural Net for Causality Detection
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Identifying causal relationships in a text is essential for achieving comprehensive natural language understanding. The present work proposes a combination of features derived from pre-trained BERT with linguistic features for training a supervised classifier for the task of Causality Detection. The Linguistic features help to inject knowledge about the semantic and syntactic structure of the input sentences. Experiments on the FinCausal Shared Task1 datasets indicate that the combination of Linguistic features with BERT improves overall performance for causality detection. The proposed system achieves a weighted average F1 score of 0.952 on the post-evaluation dataset.
Causal BERT: Language Models for Causality Detection Between Events Expressed in Text
Lecture Notes in Networks and Systems, 2021
Causality understanding between events is a critical natural language processing task that is helpful in many areas, including health care, business risk management and finance. On close examination, one can find a huge amount of textual content both in the form of formal documents or in content arising from social media like twitter, dedicated to communicating and exploring various types of causality in the real world. Recognizing these "Cause-Effect" relationships between natural language events continues to remain a challenge simply because it is often expressed implicitly. Implicit causality is hard to detect through most of the techniques employed in literature and can also, at times be perceived as ambiguous or vague. Also, although well known datasets do exist for this problem, the examples in them are limited in the range and complexity of the causal relationships they depict especially when related to implicit relationships. Most of the contemporary methods are either based on lexico-semantic pattern matching or are feature-driven supervised methods. Therefore, as expected these methods are more geared towards handling explicit causal relationships leading to limited coverage for implicit relationships and are hard to generalize. In this paper, we investigate the language model's capabilities for causal association among events expressed in natural language text using sentence context combined with event information, and by leveraging masked event context with in-domain and out-of-domain data distribution. Our proposed methods achieve the state-of-art performance in three different data distributions and can be leveraged for extraction of a causal diagram and/or building a chain of events from unstructured text.
Automatic Extraction of Cause-Effect Relations in Natural Language Text
2013
The discovery of causal relations from text has been studied adopting various approaches based on rules or Machine Learning (ML) techniques. The approach proposed joins both rules and ML methods to combine the advantage of each one. In particular, our approach first identifies a set of plausible cause-effect pairs through a set of logical rules based on dependencies between words then it uses Bayesian inference to reduce the number of pairs produced by ambiguous patterns. The SemEval-2010 task 8 dataset challenge has been used to evaluate our model. The results demonstrate the ability of the rules for the relation extraction and the improvements made by the filtering process.