Frontier Pruning for Shift-Reduce CCG Parsing

نویسندگان

  • Stephen Merity
  • James Curran
چکیده

We apply the graph-structured stack (GSS) to shift-reduce parsing in a Combinatory Categorial Grammar (CCG) parser. This allows the shift-reduce parser to explore all possible parses in polynomial time without resorting to heuristics, such as beam search. The GSSbased shift-reduce parser is 34% slower than CKY in the finely-tuned C&C parser. We perform frontier pruning on the GSS, increasing the parsing speed to be competitive with the C&C parser with a small accuracy penalty.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

LSTM Shift-Reduce CCG Parsing

We describe a neural shift-reduce parsing model for CCG, factored into four unidirectional LSTMs and one bidirectional LSTM. This factorization allows the linearization of the complete parsing history, and results in a highly accurate greedy parser that outperforms all previous beam-search shift-reduce parsers for CCG. By further deriving a globally optimized model using a task-based loss, we i...

متن کامل

Shift-Reduce CCG Parsing

CCGs are directly compatible with binarybranching bottom-up parsing algorithms, in particular CKY and shift-reduce algorithms. While the chart-based approach has been the dominant approach for CCG, the shift-reduce method has been little explored. In this paper, we develop a shift-reduce CCG parser using a discriminative model and beam search, and compare its strengths and weaknesses with the c...

متن کامل

Expected F-Measure Training for Shift-Reduce Parsing with Recurrent Neural Networks

We present expected F-measure training for shift-reduce parsing with RNNs, which enables the learning of a global parsing model optimized for sentence-level F1. We apply the model to CCG parsing, where it improves over a strong greedy RNN baseline, by 1.47% F1, yielding state-of-the-art results for shiftreduce CCG parsing.

متن کامل

Shift-Reduce CCG Parsing with a Dependency Model

This paper presents the first dependency model for a shift-reduce CCG parser. Modelling dependencies is desirable for a number of reasons, including handling the “spurious” ambiguity of CCG; fitting well with the theory of CCG; and optimizing for structures which are evaluated at test time. We develop a novel training technique using a dependency oracle, in which all derivations are hidden. A c...

متن کامل

Neural Shift-Reduce CCG Semantic Parsing

We present a shift-reduce CCG semantic parser. Our parser uses a neural network architecture that balances model capacity and computational cost. We train by transferring a model from a computationally expensive loglinear CKY parser. Our learner addresses two challenges: selecting the best parse for learning when the CKY parser generates multiple correct trees, and learning from partial derivat...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2011