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 …

Causal relation extraction is a challenging yet very important task for Natural Language
Processing (NLP). There are many existing approaches developed to tackle this task, either …

Causality extraction from natural language texts is a challenging open problem in artificial
intelligence. Existing methods utilize patterns, constraints, and machine learning techniques …

The application of machine learning to agriculture is currently experiencing a “surge of
interest” from the academic community as well as practitioners from industry. This increased …

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 …

A rule-based framework, named SCANER (Semi-automated CAusal Network Extraction from
Raw text) is presented. SCANER converts raw text into causal networks using a set of …

Causationin written natural language can express a strong relationship between events and
facts. Causation in the written form can be referred to as a causal relation where a cause …

The detection of textual Emotion-Cause Pair causality is very helpful for improving the
accuracy of emotion-cause extraction and understanding the causes behind specific events …

Автоматическое извлечение причинно-следственных связей (ПСС) из текстов
естественного языка является сложной проблемой искусственного интеллекта …

Статья посвящена исследованию методов извлечения причинно-следственных связей
из неструктурированного текста на естественном языке. Автоматическое извлечение …