Supertagging

Supertagging is the tagging process of assigning the correct elementary tree of LTAG, or the correct supertag, to each word of an input sentence1. In this pa- per we propose …...

This paper describes the role of supertagging in a wide-coverage CCG parser which uses a log-linear model to select an analysis. The supertagger reduces the derivation space over which model …...

We propose a conditional random field- based method for supertagging, and ap- ply it to the task of learning new lexi- cal items for HPSG-based precision gram- mars of English …...

This paper describes how external resources can be used to improve parser performance for heavily lexicalised grammars, looking at both robustness and efficiency. In terms of robust- ness, we try …...

In lexicalized grammatical formalisms, it is possible to separate lexical category assign- ment from the combinatory processes that make use of such categories, such as pars- ing and realization. We …...

Much previous work has investigated weak supervision with HMMs and tag dictionar- ies for part-of-speech tagging, but there have been no similar investigations for the harder problem of supertagging. Here, …...

In a supertagging task, sequence labeling models are commonly used. But their lim- ited ability to model long-distance informa- tion presents a bottleneck to make further im- provements. In this …...

The Viterbi algorithm is the conventional decoding algorithm most widely adopted for sequence labeling. Viterbi decoding is, however, prohibitively slow when the label set is large, because its time com- …...

AbstractWe present supertagging-based models for Tree Adjoining Grammar parsing that use neural network architectures and dense vector representation of supertags (elementary trees) to achieve state-of-the-art performance in unlabeled and labeled …...

AbstractWe present a graph-based Tree Adjoining Grammar (TAG) parser that uses BiLSTMs, highway connections, and character-level CNNs. Our best end-to-end parser, which jointly performs supertagging, POS tagging, and parsing, outperforms …...

AbstractSupertagging is a sequence prediction task where each word is assigned a piece of complex syntactic structure called a supertag. We provide a novel approach to multi-task learning for Tree …...

AbstractSupertagging is conventionally regarded as an important task for combinatory categorial grammar (CCG) parsing, where effective modeling of contextual information is highly important to this task. However, existing studies have …...

AbstractWe propose a method for formulating CCG as a recursive composition in a continuous vector space. Recent CCG supertagging and parsing models generally demonstrate high performance, yet rely on black-box …...