1 citations · 3 across the 4 of their papers we have counts for
5 papers
Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGs
Songlin Yang, Wei Liu, Kewei Tu
Hidden Markov Models (HMMs) and Probabilistic Context-Free Grammars (PCFGs) are widely used structured models, both of which can be represented as factor graph grammars (FGGs), a p…
Nested Named Entity Recognition as Latent Lexicalized Constituency Parsing
Chao Lou, Songlin Yang, Kewei Tu
Nested named entity recognition (NER) has been receiving increasing attention. Recently, (Fu et al, 2021) adapt a span-based constituency parser to tackle nested NER. They treat ne…
Neural Bi-Lexicalized PCFG Induction
Songlin Yang, Yanpeng Zhao, Kewei Tu
Neural lexicalized PCFGs (L-PCFGs) have been shown effective in grammar induction. However, to reduce computational complexity, they make a strong independence assumption on the ge…
PCFGs Can Do Better: Inducing Probabilistic Context-Free Grammars with Many Symbols
Songlin Yang, Yanpeng Zhao, Kewei Tu
Probabilistic context-free grammars (PCFGs) with neural parameterization have been shown to be effective in unsupervised phrase-structure grammar induction. However, due to the cub…
Second-Order Unsupervised Neural Dependency Parsing
Songlin Yang, Yong Jiang, Wenjuan Han +1
Most of the unsupervised dependency parsers are based on first-order probabilistic generative models that only consider local parent-child information. Inspired by second-order sup…