Most of the sequence tagging tasks in natural language processing require to recognize segments with certain syntactic role or semantic meaning in a sentence. They are usually tackled with Conditional Random Fields (CRFs), which do indirect word-level modeling over word-level features and thus cannot make full use of segment-level information. Semi-Markov Conditional Random Fields (Semi-CRFs) model segments directly but extracting segment-level features for Semi-CRFs is still a very challenging problem. This paper presents Gated Recursive Semi-CRFs (grSemi-CRFs), which model segments directly and automatically learn segmentlevel features through a gated recursive convolutional neural network. Our experiments on text chunking and named entity recognition (NER) demonstrate that grSemi-CRFs generally outperform other neural models.
REF and Ye and Ling (2018) apply semi-CRFs to word-level inputs for named entity recognition.
Segment-Level Sequence Modeling using Gated Recursive Semi-Markov Conditional Random Fields
ACL
Computer Science