Both bottom-up and top-down strategies have been used for neural transition-based constituent parsing. The parsing strategies differ in terms of the order in which they recognize productions in the derivation tree, where bottom-up strategies and top-down strategies take post-order and pre-order traversal over trees, respectively. Bottom-up parsers benefit from rich features from readily built partial parses, but lack lookahead guidance in the parsing process; top-down parsers benefit from non-local guidance for local decisions, but rely on a strong encoder over the input to predict a constituent hierarchy before its construction.To mitigate both issues, we propose a novel parsing system based on in-order traversal over syntactic trees, designing a set of transition actions to find a compromise between bottom-up constituent information and top-down lookahead information. Based on stack-LSTM, our psycholinguistically motivated constituent parsing system achieves 91.8 F1 on WSJ benchmark. Furthermore, the system achieves 93.6 F1 with supervised reranking and 94.2 F1 with semi-supervised reranking, which are the best results on the WSJ benchmark.
Our work is related to left corner parsing. formalize this in automata theory, which have appeared frequently in the compiler literature. apply the strategy into parsing. Typical works investigate the transformation of syntactic trees based on left-corner rules @cite_6 @cite_11 @cite_5 . In contrast, we propose a novel general transition-based in-order constituent parsing system.
@cite_5 @cite_6 @cite_11
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The frequency of words and syntactic constructions has been observed to have a substantial effect on language processing. This begs the question of what causes certain constructions to be more or less frequent. A theory of grounding (Phillips, 2010) would suggest that cognitive limitations might cause languages to develop frequent constructions in such a way as to avoid processing costs. This paper studies how current theories of working memory fit into theories of language processing and what influence memory limitations may have over reading times. Measures of such limitations are evaluated on eye-tracking data and the results are compared with predictions made by different theories of processing. The apparatus includes a cluster of knife blades which is moved up and down to slice the fruit; a free floating cylinder having axial slots and freely resting on the cluster, the cluster being disposed within the free floating member with its blades being positioned within the slots; an apertured cup holding a cored fruit positioned below the cluster, the core passage of the fruit being aligned with the aperture in the cup; and a celling knife disposed below the cup which is raised into the core passage in the fruit to remove the seed cell material thereupon. The sequence of operation is commenced by positioning the fruit in the cup below the cluster and above the celling knife. The free floating member is brought to rest on the fruit while the celling knife is raised into the core passage in the fruit, gradually expanded to remove the seed cell material therefrom in small particles, collapsed and retracted from the core passage. The cluster of knife blades is advanced against the fruit to slice same and retracted while the free floating member, being restrained by the flexible stop member, extracts wedged slices of the fruit from between the knife blades. Human syntactic processing shows many signs of taking place within a general-purpose short-term memory. But this kind of memory is known to have a severely constrained storage capacity---possibly constrained to as few as three or four distinct elements. This article describes a model of syntactic processing that operates successfully within these severe constraints, by recognizing constituents in a right-corner transformed representation (a variant of left-corner parsing) and mapping this representation to random variables in a Hierarchic Hidden Markov Model, a factored time-series model which probabilistically models the contents of a bounded memory store over time. Evaluations of the coverage of this model on a large syntactically annotated corpus of English sentences, and the accuracy of a a bounded-memory parsing strategy based on this model, suggest this model may be cognitively plausible.