This paper describes our system (HIT-SCIR) submitted to the CoNLL 2018 shared task on
Multilingual Parsing from Raw Text to Universal Dependencies. We base our submission on …

Despite recent successes of deep learning in many fields of natural language processing,
previous studies of emotion recognition on Twitter mainly focused on the use of lexicons and …

Supervised machine learning techniques have already been widely studied and applied to
various real-world applications. However, most existing supervised algorithms work well …

Syntactic and semantic parsing has been investigated for decades, which is one primary
topic in the natural language processing community. This article aims for a brief survey on …

Linguistic typology aims to capture structural and semantic variation across the world's
languages. A large-scale typology could provide excellent guidance for multilingual Natural …

We present a deep neural architecture that parses sentences into three semantic
dependency graph formalisms. By using efficient, nearly arc-factored inference and a …

We present a novel unsupervised approach for multilingual sentiment analysis driven by
compositional syntax-based rules. On the one hand, we exploit some of the main …

During the past decades, due to the lack of sufficient labeled data, most studies on cross-
domain parsing focus on unsupervised domain adaptation, assuming there is no target …

Multilingual sentence encoders have seen much success in cross-lingual model transfer for
downstream NLP tasks. The success of this transfer is, however, dependent on the model's …

Natural language processing (NLP) aims, broadly speaking, to teach computers to
understand human language. This is hard as the computer must comprehend many facets of …