A logical word embedding for learning grammar
This addresses the challenge of grammar inference for natural language processing, though it appears incremental by combining existing grammatical theories.
The paper tackles the problem of unsupervised grammar learning by introducing the Logical Grammar Embedding (LGE) model, which infers lexical categories and syntactic rules from text corpora, achieving learning from as few as a hundred sentences.
We introduce the logical grammar emdebbing (LGE), a model inspired by pregroup grammars and categorial grammars to enable unsupervised inference of lexical categories and syntactic rules from a corpus of text. LGE produces comprehensible output summarizing its inferences, has a completely transparent process for producing novel sentences, and can learn from as few as a hundred sentences.