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Found 7 papers, 5 papers with code
A Coarse-to-Fine Labeling Framework for Joint Word Segmentation, POS Tagging, and Constituent Parsing
In the coarse labeling stage, the joint model outputs a bracketed tree, in which each node corresponds to one of four labels (i. e., phrase, subphrase, word, subword).
Learning node embeddings via summary graphs: a brief theoretical analysis
Recent works try to improve scalability via graph summarization -- i. e., they learn embeddings on a smaller summary graph, and then restore the node embeddings of the original graph.
Bridging Pre-trained Language Models and Hand-crafted Features for Unsupervised POS Tagging
In recent years, large-scale pre-trained language models (PLMs) have made extraordinary progress in most NLP tasks.
An In-depth Study on Internal Structure of Chinese Words
Several previous works on syntactic parsing propose to annotate shallow word-internal structures for better utilizing character-level information.
Summarizing graphs using configuration model
As a solution, graph summarization, which aims to find a compact representation that preserves the important properties of a given graph, has received much attention, and numerous algorithms have been developed for it.
Social and Information Networks
Fast and Accurate Neural CRF Constituency Parsing
Estimating probability distribution is one of the core issues in the NLP field.
Ranked #1 on
Constituency Parsing
on CTB7
Is POS Tagging Necessary or Even Helpful for Neural Dependency Parsing?
In the pre deep learning era, part-of-speech tags have been considered as indispensable ingredients for feature engineering in dependency parsing.
