We present an extension to incremental shift-reduce parsing that handles discontinuous constituents, using a linear classifier and beam search. We achieve very high parsing speeds (up to 640 sent./sec.) and accurate results (up to 79.52 F1 on TiGer).

In this paper we describe an enhanced shift-reduce parsing method which differs from the traditional transition-based model in two ways: first, we maintain multiple transition paths after each transition step, in order to alleviate the serious risk of going astray for the only-one-path transition; second, we adopt the online training algorithm rather than the classical training-after-extraction...

We present a constituent shift-reduce parser with a structured perceptron that finds the optimal parse in a practical runtime. The key ideas are new feature templates that facilitate state merging of dynamic programming and A* search. Our system achieves 91.1 F1 on a standard English experiment, a level which cannot be reached by other beam-based systems even with large beam sizes.1

The SPMRL 2013 shared task was the opportunity to develop and test, with promising results, a simple beam-based shift-reduce dependency parser on top of the tabular logic programming system DYALOG. The parser was also extended to handle ambiguous word lattices, with almost no loss w.r.t. disambiguated input, thanks to specific training, use of oracle segmentation, and large beams. We believe th...

In this paper we show how our semantic parser (Knowledge Parser or K-Parser) identifies various kinds of event mentions in the input text. The types include recursive (complex) and non recursive event mentions. KParser outputs each event mention in form of an acyclic graph with root nodes as the verbs that drive those events. The children nodes of the verbs represent the entities participating ...

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...

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...

A data-parallel processing approach is promising for real-time volume rendering because of the massive parallelism in volume rendering. In data-parallel volume rendering, local results processing elements(PEs) generate from allocated subvolumes are integrated to form a final image. Generally, the integration causes an overhead unavoidable in data-parallel volume rendering due to communications ...