Probabilistic Models for High-Order Projective Dependency Parsing
This paper presents generalized probabilistic models for high-order projective dependency parsing and an algorithmic framework for learning these statistical models involving dependency trees. Partition functions and marginals for high-order dependency trees can be computed efficiently, by adapting our algorithms which extend the inside-outside algorithm to higher-order cases. To show the effectiveness of our algorithms, we perform experiments on three languages---English, Chinese and Czech, using maximum conditional likelihood estimation for model training and L-BFGS for parameter estimation. Our methods achieve competitive performance for English, and outperform all previously reported dependency parsers for Chinese and Czech.
Code (0)
등록된 구현이 없습니다.
Tasks
Dependency Parsingparameter estimationVocal Bursts Intensity PredictionSimilar Papers 제목 키워드 기반
Sequential Graph Dependency Parser
We propose a method for non-projective dependency parsing by incrementally predicting a set of edges. Since the edges do not have a pre-specified order, we propose a set-based learning method. Our method blends graph, tr…
Dependency ParsingHeaded-Span-Based Projective Dependency Parsing
We propose a new method for projective dependency parsing based on headed spans. In a projective dependency tree, the largest subtree rooted at each word covers a contiguous sequence (i.e., a span) in the surface order. …
Constituency ParsingDependency ParsingHeaded-Span-Based Projective Dependency Parsing
We propose a new paradigm for projective dependency parsing based on headed spans. In a projective dependency tree, the subtree rooted at each word covers a contiguous sequence (i.e., a span) in the surface order. We c…
Dependency Parsingglobal-optimizationTowards Non-projective High-Order Dependency Parser
This paper presents a novel high-order dependency parsing framework that targets non-projective treebanks. It imitates how a human parses sentences in an intuitive way. At every step of the parse, it determines which wor…
Dependency ParsingVocal Bursts Intensity PredictionNeural Probabilistic Model for Non-projective MST Parsing
In this paper, we propose a probabilistic parsing model, which defines a proper conditional probability distribution over non-projective dependency trees for a given sentence, using neural representations as inputs. The …
modelSentence