Multi-turn conversation understanding is a major challenge for building intelligent dialogue
systems. This work focuses on retrieval-based response matching for multi-turn conversation …

It is intuitive that NLP tasks for logographic languages like Chinese should benefit from the
use of the glyph information in those languages. However, due to the lack of rich …

Chinese word segmentation (CWS) is a fundamental step of Chinese natural language
processing. In this paper, we build a new toolkit, named PKUSEG, for multi-domain word …

Segmenting a chunk of text into words is usually the first step of processing Chinese text, but
its necessity has rarely been explored. In this paper, we ask the fundamental question of …

A bottleneck problem with Chinese named entity recognition (NER) in new domains is the
lack of annotated data. One solution is to utilize the method of distant supervision, which has …

Neural word segmentation research has benefited from large-scale raw texts by leveraging
them for pretraining character and word embeddings. On the other hand, statistical …

Implicit discourse relation recognition is a challenging task as the relation prediction without
explicit connectives in discourse parsing needs understanding of text spans and cannot be …

Character-based and word-based methods are two main types of statistical models for
Chinese word segmentation, the former exploiting sequence labeling models over …

Neural models with minimal feature engineering have achieved competitive performance
against traditional methods for the task of Chinese word segmentation. However, both …

Lexical analysis is believed to be a crucial step towards natural language understanding
and has been widely studied. Recent years, end-to-end lexical analysis models with …