With the rapid development and popularization of Internet and mobile communication
technologies, text data mining has attracted much attention. In particular, with the wide use …

Building computational models to account for the cortical representation of language plays
an important role in understanding the human linguistic system. Recent progress in …

Recently, much progress has been made in learning general-purpose sentence
representations that can be used across domains. However, most of the existing models …

Events are typically composed of at least actions and entities. Both actions and entities have
been shown to be represented by neural structures respecting domain organizations in the …

Phrase embedding can improve the performance of multiple NLP tasks. Most of the previous
phrase-embedding methods that only use the external or internal semantic information of …

Distributional semantic models (DSMs) generally require sufficient examples for a word to
learn a high quality representation. This is in stark contrast with human who can guess the …

We introduce a novel mixed characterword architecture to improve Chinese sentence
representations, by utilizing rich semantic information of word internal structures. Our …

Multimodal models have been proven to outperform text-based approaches on learning
semantic representations. However, it still remains unclear what properties are encoded in …

Different methods are proposed to learn phrase embedding, which can be mainly divided
into two strands. The first strand is based on the distributional hypothesis to treat a phrase as …

The quality of the phrase embedding is related to the performance of many NLP downstream
tasks. Most of the existing phrase embedding methods are difficult to achieve satisfactory …