scholarly journals Dependency Parsing with an Extended Finite-State Approach

2003 ◽  
Vol 29 (4) ◽  
pp. 515-544 ◽  
Author(s):  
Kemal Oflazer
Keyword(s):  
Fixed Point ◽  
Dependency Parsing ◽  
Finite State ◽  
Dependency Relations ◽  
Syntactic Dependency

This article presents a dependency parsing scheme using an extended finite-state approach. The parser augments input representation with “channels” so that links representing syntactic dependency relations among words can be accommodated and iterates on the input a number of times to arrive at a fixed point. Intermediate configurations violating various constraints of projective dependency representations such as no crossing links and no independent items except sentential head are filtered via finite-state filters. We have applied the parser to dependency parsing of Turkish.

scholarly journals Mot en trebank for amerikanorsk

2021 ◽  
Vol 11 (2) ◽  
pp. 243-250
Author(s):  
Andre Kåsen
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Automatic Assignment ◽  
Learning Techniques ◽  
Accuracy Measure ◽  
Dependency Relations ◽  
Syntactic Dependency

This article presents a method for automatic assignment of syntactic dependency relations to the corpus of American Norwegian speech (CANS). Different machine learning techniques and corpora are used. Finally, an accuracy measure is computed and compared with a relatively new treebank for spoken Norwegian.

scholarly journals Global Greedy Dependency Parsing

2020 ◽  
Vol 34 (05) ◽  
pp. 8319-8326
Author(s):  
Zuchao Li ◽  
Hai Zhao ◽  
Kevin Parnow
Keyword(s):  
Feature Extraction ◽  
Polynomial Time ◽  
Time Complexity ◽  
Linear Time ◽  
Parse Tree ◽  
Dependency Parsing ◽  
Graph Models ◽  
Parse Trees ◽  
Left And Right ◽  
Syntactic Dependency

Most syntactic dependency parsing models may fall into one of two categories: transition- and graph-based models. The former models enjoy high inference efficiency with linear time complexity, but they rely on the stacking or re-ranking of partially-built parse trees to build a complete parse tree and are stuck with slower training for the necessity of dynamic oracle training. The latter, graph-based models, may boast better performance but are unfortunately marred by polynomial time inference. In this paper, we propose a novel parsing order objective, resulting in a novel dependency parsing model capable of both global (in sentence scope) feature extraction as in graph models and linear time inference as in transitional models. The proposed global greedy parser only uses two arc-building actions, left and right arcs, for projective parsing. When equipped with two extra non-projective arc-building actions, the proposed parser may also smoothly support non-projective parsing. Using multiple benchmark treebanks, including the Penn Treebank (PTB), the CoNLL-X treebanks, and the Universal Dependency Treebanks, we evaluate our parser and demonstrate that the proposed novel parser achieves good performance with faster training and decoding.

Using Short Dependency Relations from Auto-Parsed Data for Chinese Dependency Parsing

2009 ◽  
Vol 8 (3) ◽  
pp. 1-20 ◽  
Author(s):  
Wenliang Chen ◽  
Daisuke Kawahara ◽  
Kiyotaka Uchimoto ◽  
Yujie Zhang ◽  
Hitoshi Isahara
Keyword(s):  
Dependency Parsing ◽  
Dependency Relations

scholarly journals Morphological and Syntactic Case in Statistical Dependency Parsing

2013 ◽  
Vol 39 (1) ◽  
pp. 23-55 ◽  
Author(s):  
Wolfgang Seeker ◽  
Jonas Kuhn
Keyword(s):  
Search Space ◽  
Syntactic Analysis ◽  
Use Case ◽  
Dependency Parsing ◽  
Pipeline Architecture ◽  
Grammatical Function ◽  
Constrained System ◽  
Statistical Dependency ◽  
Morphologically Rich Languages ◽  
Syntactic Dependency

Most morphologically rich languages with free word order use case systems to mark the grammatical function of nominal elements, especially for the core argument functions of a verb. The standard pipeline approach in syntactic dependency parsing assumes a complete disambiguation of morphological (case) information prior to automatic syntactic analysis. Parsing experiments on Czech, German, and Hungarian show that this approach is susceptible to propagating morphological annotation errors when parsing languages displaying syncretism in their morphological case paradigms. We develop a different architecture where we use case as a possibly underspecified filtering device restricting the options for syntactic analysis. Carefully designed morpho-syntactic constraints can delimit the search space of a statistical dependency parser and exclude solutions that would violate the restrictions overtly marked in the morphology of the words in a given sentence. The constrained system outperforms a state-of-the-art data-driven pipeline architecture, as we show experimentally, and, in addition, the parser output comes with guarantees about local and global morpho-syntactic wellformedness, which can be useful for downstream applications.

scholarly journals Equational Axioms for Probabilistic Bisimilarity (Preliminary Report)

2002 ◽  
Vol 9 (6) ◽  
Author(s):  
Luca Aceto ◽  
Zoltán Ésik ◽  
Anna Ingólfsdóttir
Keyword(s):  
Fixed Point ◽  
Preliminary Report ◽  
Monotonic Functions ◽  
Finite State ◽  
Point Operator ◽  
Bisimulation Equivalence ◽  
State Agents

This paper gives an equational axiomatization of probabilistic bisimulation equivalence for a class of finite-state agents previously studied by Stark and Smolka ((2000) <em>Proof, Language, and Interaction: Essays in Honour of Robin Milner</em>, pp. 571-595). The axiomatization is obtained by extending the general axioms of iteration theories (or iteration algebras), which characterize the equational properties of the fixed point operator on (omega-)continuous or monotonic functions, with three axiom schemas that express laws that are specific to probabilistic bisimilarity. Hence probabilistic bisimilarity (over finite-state agents) has an equational axiomatization relative to iteration algebras.

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