Detecting entailment relationship between two sentences has profoundly impacted several different application areas of Natural Language Processing (NLP). Though recognizing textual entailment (TE) is amongst the widely studied problems, the research on detecting entailment between pieces of scientific texts is still in its infancy. To this end the paper discusses implementation of systems based on Long Short-Term Memory (LSTM) neural network and Support Vector Machine (SVM) classifiers using SCITAIL entailment dataset, a dataset in which premise and hypothesis are constituted of scientific texts. Also, a TE-based framework for cooking domain question answering is introduced. The proposed framework exploits the entailment relationship between user question and the cooking questions contained inside a Knowledge Base (KB).

检测两个句子之间的依存关系已深刻影响了自然语言处理（NLP）的几个不同应用领域。尽管识别文本蕴涵（TE）是广泛研究的问题之一，但检测科学文本之间的蕴涵的研究仍处于起步阶段。为此，本文讨论了基于长期短期记忆（LSTM）神经网络和支持向量机（SVM）分类器的系统的实现，其中使用了SCITAIL蕴含数据集，该数据集的前提和假设均由科学文本构成。此外，介绍了基于TE的烹饪域问题解答框架。所提出的框架利用了用户问题与知识库（KB）中包含的烹饪问题之间的联系关系。