Applying BERT Embeddings to Predict Legal Textual Entailment

Applying COGEX to Recognize Textual Entailment

The PASCAL RTE challenge has helped LCC to explore the applicability of enhancements that have been made to our logic form representation and WordNet lexical chains generator. Our system transforms each T-H pair into logic form representation with semantic relations. The system automatically generates NLP axioms serving as linguistic rewriting rules and lexical chain axioms that connect concept...

Arabic Textual Entailment with Word Embeddings

Determining the textual entailment between texts is important in many NLP tasks, such as summarization, question answering, and information extraction and retrieval. Various methods have been suggested based on external knowledge sources; however, such resources are not always available in all languages and their acquisition is typically laborious and very costly. Distributional word representa...

Recognizing Textual Entailment in Twitter Using Word Embeddings

In this paper, we investigate the application of machine learning techniques and word embeddings to the task of Recognizing Textual Entailment (RTE) in Social Media. We look at a manually labeled dataset (Lendvai et al., 2016) consisting of user generated short texts posted on Twitter (tweets) and related to four recent media events (the Charlie Hebdo shooting, the Ottawa shooting, the Sydney S...

Textual Entailment

Textual Entailment

The task of Recognizing Textual Entailment (RTE) requires automated systems to identify when two spans of text share a common meaning -for example, that ``Alphaville Inc.'s attempted acquisition of Bauhaus led to a jump in both companies' stock prices'' entails ``Bauahaus' stock rose'', but not ``Alphaville acquired Bauhaus''. This general capability would be a solid proxy for Natural Language ...