text: 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.
  @cite_14: This paper describes an extremely lexicalized probabilistic model for fast and accurate HPSG parsing. In this model, the probabilities of parse trees are defined with only the probabilities of selecting lexical entries. The proposed model is very simple, and experiments revealed that the implemented parser runs around four times faster than the previous model and that the proposed model has a high accuracy comparable to that of the previous model for probabilistic HPSG, which is defined over phrase structures. We also developed a hybrid of our probabilistic model and the conventional phrase-structure-based model. The hybrid model is not only significantly faster but also significantly more accurate by two points of precision and recall compared to the previous model.
  @cite_22: 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 estimation is performed, reducing the space required for discriminative training. It also dramatically increases the speed of the parser. We show that large increases in speed can be obtained by tightly integrating the supertagger with the CCG grammar and parser. This is the first work we are aware of to successfully integrate a supertagger with a full parser which uses an automatically extracted grammar. We also further reduce the derivation space using constraints on category combination. The result is an accurate wide-coverage CCG parser which is an order of magnitude faster than comparable systems for other linguistically motivated formalisms.
  @cite_8: 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 robustness, we try using different types of external data to increase lexical coverage, and find that simple POS tags have the most effect, increasing coverage on unseen data by up to 45 . We also show that filtering lexical items in a supertagging manner is very effective in increasing efficiency. Even using vanilla POS tags we achieve some efficiency gains, but when using detailed lexical types as supertags we manage to halve parsing time with minimal loss of coverage or precision.
  @cite_27: 
  @cite_34: We present a simple, but surprisingly effective, method of self-training a two-phase parser-reranker system using readily available unlabeled data. We show that this type of bootstrapping is possible for parsing when the bootstrapped parses are processed by a discriminative reranker. Our improved model achieves an f-score of 92.1 , an absolute 1.1 improvement (12 error reduction) over the previous best result for Wall Street Journal parsing. Finally, we provide some analysis to better understand the phenomenon.
summary: Our concepts of are similar with the work of . For lexicalized grammars such as Combinatory Categorial Grammar (CCG), Tree-Adjoining Grammar (TAG) and Head-Driven Phrase Structure Grammar (HPSG), each word in the input sentence is assigned one or more super tags, which are used to identify the syntactic role of the word for constraint parsing @cite_27 @cite_22 @cite_34 @cite_14 @cite_8 . For a lexicalized grammar, can benefit the parsing in both accuracy and efficiency by offering information. In particular, defined the concept in TAG, which is similar to our . However, there are three differences. First, the is defined to describe the main syntactic tree structure with a series of unary projections, while is defined to describe how words can start or end hierarchical constituents (it is possible to be empty if the word cannot start or end constituents). Second, are extracted from gold trees and used to prune the search space of parsing as hard constraints. In contrast, we use constituent hierarchies as soft features. Third, use to prune a chart parsing, while we use to improve a linear shift-reduce parser.