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.
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.
The Natural Language Decathlon (decaNLP) is a new benchmark for studying general NLP models that can perform a variety of complex, natural language tasks.
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".
A dependency parser analyzes the grammatical structure of a sentence, establishing relationships between "head" words and words which modify those heads.
Stanford CoreNLP provides a set of human language technology 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, mark up the structure of sentences in terms of phrases and syntactic dependencies, indicate which noun phrases refer to the same entities, indicate sentiment, extract particular or open-class relations between entity mentions, get the quotes people said, etc.
This post is meant as a summary of many of the concepts that I learned in Marti Hearst's Natural Language Processing class at the UC Berkeley School of Information.
G. Marco Baroni, Georgiana Dinu. 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference, (2014)
S. Bordia, и S. Bowman. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, стр. 7--15. Minneapolis, Minnesota, Association for Computational Linguistics, (июня 2019)
A. Yeh. Proceedings of the 18th Conference on Computational Linguistics - Volume 2, стр. 947--953. Stroudsburg, PA, USA, Association for Computational Linguistics, (2000)