Transition-based models can be fast and accurate for constituent parsing. Compared with chart-based models, they leverage richer features by extracting history information from a parser stack, which spans over non-local constituents. On the other hand, during incremental parsing, constituent information on the right hand side of the current word is not utilized, which is a relative weakness of shift-reduce parsing. To address this limitation, we leverage a fast neural model to extract lookahead features. In particular, we build a bidirectional LSTM model, which leverages the full sentence information to predict the hierarchy of constituents that each word starts and ends. The results are then passed to a strong transition-based constituent parser as lookahead features. The resulting parser gives 1.3 absolute improvement in WSJ and 2.3 in CTB compared to the baseline, given the highest reported accuracies for fully-supervised parsing.
Under the lexicalized grammar, this can benefit the parsing with more accuracy and efficiency as @cite_11 . Recently, the works on obtaining the super tags appear. proposed the efficient methods to obtain super tags for HPSG parsing using dependency information. and turn to design recursive neural network for for CCG parsing. In contrast, our models predict the constituent hierarchy instead of single super tag for each word in the input sentence, which are also likely regarded as the member of multiple ordered labels prediction family.
@cite_11
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Accurate dependency recovery has recently been reported for a number of wide-coverage statistical parsers using Combinatory Categorial Grammar (CCG). However, overall figures give no indication of a parser’s performance on specific constructions, nor how suitable a parser is for specific applications. In this paper we give a detailed evaluation of a CCG parser on object extraction dependencies found in WSJ text. We also show how the parser can be used to parse questions for Question Answering. The accuracy of the original parser on questions is very poor, and we propose a novel technique for porting the parser to a new domain, by creating new labelled data at the lexical category level only. Using a supertagger to assign categories to words, trained on the new data, leads to a dramatic increase in question parsing accuracy.