Segmenting Natural Language Sentences via Lexical Unit Analysis
Yangming Li, Lemao Liu, Shuming Shi · 2021
The span-based model enjoys great popularity in recent works of sequence segmentation.However, each of these methods suffers from its own defects, such as invalid predictions.In this work, we introduce a unified span-based model, lexical unit analysis (LUA), that addresses all these matters.Segmenting a lexical unit sequence involves two steps.Firstly, we embed every span by using the representations from a pretraining language model.Secondly, we define a score for every segmentation candidate and apply dynamic programming (DP) to extract the candidate with the maximum score.We have conducted extensive experiments on 3 tasks, (e.g., syntactic chunking), across 7 datasets.LUA has established new state-ofthe-art performances on 6 of them.We have achieved even better results through incorporating label correlations.1