If you use the code, please kindly cite the following paper:
Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, Xuan Zhu. Learning Entity and Relation Embeddings for Knowledge Graph Completion. The 29th AAAI Conference on Artificial Intelligence (AAAI'15).
Stanford CoreNLP provides a set of natural language analysis tools. It can give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, and mark up the structure of sentences in terms of phrases and word dependencies, indicate which noun phrases refer to the same entities, indicate sentiment, extract open-class relations between mentions, etc.
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
This is a list of 100 important natural language processing (NLP) papers that serious students and researchers working in the field should probably know about and read.
The Natural Language Decathlon (decaNLP) is a new benchmark for studying general NLP models that can perform a variety of complex, natural language tasks.
ConceptNet Numberbatch consists of state-of-the-art semantic vectors (also known as word embeddings) that can be used directly as a representation of word meanings or as a starting point for further machine learning.
Wikipedia-based question answering system for natural language questions, open topic model, Wiki, Wikipedia, Knowledge Enhanced Embodied Cognitive Interaction Technology.
In this tutorial we look at the word2vec model by Mikolov et al. This model is used for learning vector representations of words, called "word embeddings".
2020-2021 International Conferences in Artificial Intelligence, Machine Learning, Computer Vision, Data Mining, Natural Language Processing and Robotics
M. Artetxe, G. Labaka, I. Lopez-Gazpio, and E. Agirre. Proceedings of the 22nd Conference on Computational Natural Language Learning, page 282--291. Association for Computational Linguistics, (2018)
S. Blodgett, S. Barocas, H. Daumé III, and H. Wallach. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, page 5454--5476. Online, Association for Computational Linguistics, (July 2020)
S. Bordia, and S. Bowman. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, page 7--15. Minneapolis, Minnesota, Association for Computational Linguistics, (June 2019)
S. Cordeiro, C. Ramisch, M. Idiart, and A. Villavicencio. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1, page 1986--1997. The Association for Computer Linguistics, (2016)
L. Flek. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, page 7828--7838. Online, Association for Computational Linguistics, (July 2020)