[PDF] Top 20 Semi supervised Domain Adaptation for Dependency Parsing
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Semi supervised Domain Adaptation for Dependency Parsing
... multi-source domain adaptation problem assumes there are labeled datasets for multiple source ...target domain, the chal- lenge is how to effectively combine knowledge in the source ...constituent ... See full document
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An Empirical Study of Semi supervised Structured Conditional Models for Dependency Parsing
... English dependency- parsing data sets were constructed using a stan- dard set of head-selection rules (Yamada and Mat- sumoto, 2003) to convert the phrase structure syn- tax of the Treebank to ... See full document
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Dependency Parsing and Domain Adaptation with LR Models and Parser Ensembles
... One of the simplest improvements to our ap- proach is simply to train more models with no oth- er changes to our set-up. As mentioned in section 5, the addition of a backward SVM model did im- prove accuracy on the ... See full document
7
Semi Supervised Classification for Extracting Protein Interaction Sentences using Dependency Parsing
... the dependency parse tree of a sentence (the SPK ...the domain of protein-protein interaction extraction in (Bunescu and Mooney, ...path dependency features may be due to the edit- distance based ... See full document
10
Multilingual Dependency Parsing and Domain Adaptation using DeSR
... main adaptation tracks of the CoNLL 2007 shared ...Shift/Reduce parsing algorithm, using specific rules to handle non-projective ...the domain adaptation track we applied a tree revision ... See full document
7
Domain Adaptation by Constraining Inter Domain Variability of Latent Feature Representation
... a semi-supervised setting for do- main adaptation where only unlabeled data is available for the target ...source domain only and, consequently, a classifier relying on such clusters would ... See full document
10
Learning Reliability of Parses for Domain Adaptation of Dependency Parsing
... The baseline parser was trained only on the PTB labeled data (as described in Section 1). The pro- posed method (PTB+unlabel (18,000 sents.)) out- performed the baseline by approximately 0.5%, and also beat all the ... See full document
6
Dependency Parsing Domain Adaptation using Transductive SVM
... in domain adapta- tion, although these results are not directly com- parable to ours because they refer to constituency parsing rather than dependency ...generative parsing and discriminative ... See full document
5
Semi supervised Convolutional Networks for Translation Adaptation with Tiny Amount of In domain Data
... uses semi-supervised convolutional neu- ral networks (CNNs) to select in-domain training data for statistical machine trans- ...with semi-supervised CNN, then this model computes ... See full document
10
Semi supervised Dependency Parsing using Bilexical Contextual Features from Auto Parsed Data
... out-of- domain data, we use the Brown portion of the PTB (Brown), as well as the test-sets of different do- mains available in the Google Web Treebank: An- swers, Blogs, Emails, Reviews and ... See full document
6
Improved Parsing and POS Tagging Using Inter Sentence Consistency Constraints
... with domain adaptation and lightly supervised ...for dependency parsing and part-of-speech tagging. For domain adaptation, we show an error reduction of up to ... See full document
11
Combining Active Learning and Partial Annotation for Domain Adaptation of a Japanese Dependency Parser
... Japanese dependency parser to new domains, and showed that active learning is not limited to single-domain ...like parsing. This strategy reduced the amount of in- domain training data needed ... See full document
9
Semi-supervised adaptation of RNNLMs by fine-tuning with domain-specific auxiliary features
... for domain adaptation of RNNLMs are latent Dirichlet allocation (LDA) [15] features ex- tracted from the ...for semi-supervised adapta- tion, aimed at compensating for the missing features and ... See full document
5
Domain Adaptation for Syntactic and Semantic Dependency Parsing Using Deep Belief Networks
... for domain adaptation. However, their work deals with domain adaptation for sentiment classification, which uses much fewer features and training ... See full document
12
Domain Adaptation for Parsing
... instructions remotely by mobile telephone. The original CReST corpus contains a small number of novel tags to handle phenomena that are com- mon in dialog data but not in newspaper text, such as imperative verbs. These ... See full document
9
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing
... Semi-supervised Semantic Role Labeling Using the Latent Words Language Model Koen Deschacht and Marie-Francine Moens.. Semantic Dependency Parsing of NomBank and PropBank: An Efficient I[r] ... See full document
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Domain Adaptation for Dependency Parsing via Self Training
... the parse scores, which is based on the observa- tion that a higher parse score is correlated with a higher parsing quality. The second method uses the method of Mejer and Crammer (2012) to com- pute the Delta ... See full document
10
Semi Supervised Feature Transformation for Dependency Parsing
... for dependency parsing ...improve dependency parsing. Suzuki et al. (2009) extended a Semi-supervised Structured Conditional Model (SS- SCM) of Suzuki and Isozaki (2008) to the ... See full document
11
Frustratingly Hard Domain Adaptation for Dependency Parsing
... on domain adapta- tion (Ben-David et al., 2006) attributes adaptation loss to two sources: the difference in the distribu- tion between domains and the difference in label- ing ...functions. ... See full document
5
Semi supervised Dependency Parsing using Lexical Affinities
... Table 3 shows two different kinds of errors that impact the global error rate. The first one concerns very common dependencies that have a high accu- racy but, due to their frequency, hurt the global er- ror rate of the ... See full document
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