Lambek Categorial Grammar (LCG) parsing has been proved to be an NP-complete problem. However, in the bounded-order case, the complexity can be reduced to polynomial time. (CITATION) first introduced the term graph, a simple graphical representation for LCG parsing, but his algorithm for using it remained largely inscrutable. (CITATION) later proposed a polynomial algorithm for bounded-order LCG parsing based on cyclic linear logic, yet both approaches remain largely theoretical, with no open-source implementations available. In this work, we combine the term-graph representation with insights from cyclic linear logic to develop a novel parsing algorithm for bounded-order LCG. Furthermore, we release our parser as an open-source tool.

The inside-outside algorithm is widely utilized in statistical models related to context-free grammars. It plays a key role in the EM estimation of probabilistic context-free grammars. In this work, we introduce an inside-outside algorithm for Probabilistic Lambek Categorical Grammar (PLCG)

In this work, we introduce a generative model, PLC+, for generating Lambek Categorial Grammar(LCG) sequents. We also introduce a simple method to numerically estimate the model’s parameters from an annotated corpus. Then we compare our model with probabilistic context-free grammars (PCFGs) and show that PLC+ simultaneously assigns a higher probability to a common corpus, and has greater coverage.

In this paper, we define an abstract task called structural realization that generates words given a prefix of words and a partial representation of a parse tree. We also present a method for solving instances of this task using a Gated Graph Neural Network (GGNN). We evaluate it with standard accuracy measures, as well as with respect to perplexity, in which its comparison to previous work on language modelling serves to quantify the information added to a lexical selection task by the presence of syntactic knowledge. That the addition of parse-tree-internal nodes to this neural model should improve the model, with respect both to accuracy and to more conventional measures such as perplexity, may seem unsurprising, but previous attempts have not met with nearly as much success. We have also learned that transverse links through the parse tree compromise the model’s accuracy at generating adjectival and nominal parts of speech.

We approach the problem of generalizing pre-trained word embeddings beyond fixed-size vocabularies without using additional contextual information. We propose a subword-level word vector generation model that views words as bags of character n-grams. The model is simple, fast to train and provides good vectors for rare or unseen words. Experiments show that our model achieves state-of-the-art performances in English word similarity task and in joint prediction of part-of-speech tag and morphosyntactic attributes in 23 languages, suggesting our model’s ability in capturing the relationship between words’ textual representations and their embeddings.