Transition-Based Parsing

We introduce a novel method to jointly parse and detect disfluencies in spoken utterances. Our model can use arbitrary features for pars- ing sentences and adapt itself with out-of- domain …...

We present a novel approach, called selec- tional branching, which uses confidence es- timates to decide when to employ a beam, providing the accuracy of beam search at speeds close …...

This paper presents a non-projective dependency parsing system that is transition-based and op- erates in three steps. The three steps include one classical method for projective dependency parsing and two …...

This paper describes the systems deployed by the ALPAGE team to participate to the SemEval-2014 Task on Broad-Coverage Semantic Dependency Parsing. We de- veloped two transition-based dependency parsers with extended …...

In this paper, we present a generalized transition-based parsing framework where parsers are instantiated in terms of a set of control parameters that constrain tran- sitions between parser states. This …...

We introduce a novel approach to the decoding problem in transition-based parsing: heuris- tic backtracking. This algorithm uses a se- ries of partial parses on the sentence to locate the …...

We develop a novel transition-based parsing algorithm for the abstract meaning representa- tion parsing task using exact imitation learn- ing, in which the parser learns a statistical model by imitating …...

AbstractWe report our work on building linguistic resources and data-driven parsers in the grammatical relation (GR) analysis for Mandarin Chinese. Chinese, as an analytic language, encodes grammatical information in a …...

AbstractWe present a new method for transition-based parsing where a solution is a pair made of a dependency tree and a derivation graph describing the construction of the former. From …...

AbstractWe define a mapping from transition-based parsing algorithms that read sentences from left to right to sequence labeling encodings of syntactic trees. This not only establishes a theoretical relation between …...

AbstractModeling the parser state is key to good performance in transition-based parsing. Recurrent Neural Networks considerably improved the performance of transition-based systems by modelling the global state, e.g. stack-LSTM parsers, …...

AbstractGreedy algorithms for NLP such as transition-based parsing are prone to error propagation. One way to overcome this problem is to allow the algorithm to backtrack and explore an alternative …...