  We introduce \texttt{N-LTP}, an open-source neural language technology
platform supporting six fundamental Chinese NLP tasks: {lexical analysis}
(Chinese word segmentation, part-of-speech tagging, and named entity
recognition), {syntactic parsing} (dependency parsing), and {semantic parsing}
(semantic dependency parsing and semantic role labeling). Unlike the existing
state-of-the-art toolkits, such as \texttt{Stanza}, that adopt an independent
model for each task, \texttt{N-LTP} adopts the multi-task framework by using a
shared pre-trained model, which has the advantage of capturing the shared
knowledge across relevant Chinese tasks. In addition, a knowledge distillation
method \cite{DBLP:journals/corr/abs-1907-04829} where the single-task model
teaches the multi-task model is further introduced to encourage the multi-task
model to surpass its single-task teacher. Finally, we provide a collection of
easy-to-use APIs and a visualization tool to make users to use and view the
processing results more easily and directly. To the best of our knowledge, this
is the first toolkit to support six Chinese NLP fundamental tasks. Source code,
documentation, and pre-trained models are available at
\url{https://github.com/HIT-SCIR/ltp}.
