[PDF] Top 20 Adapting Self Training for Semantic Role Labeling

... Self-training (Yarowsky, 1995) is a semi- supervised algorithm which has been well stu- died in the NLP area and gained promising re- sult. It iteratively extend its training set by labe- ling the ... See full document

... Second, labeling simple sentences is much easier than labeling raw sentences and allows us to generalize more effectively across sentences with differing ...labeled training instances; using ... See full document

... Most semantic role labeling (SRL) research has been focused on training and evaluating on the same ...over- training to the particular ...for training and testing the system on ... See full document

... In this article, we propose several kernel functions to model parse tree properties in kernel- based machines, for example, perceptrons or support vector machines. In particular, we define different kinds of tree kernels ... See full document

... the training set, and use this supertagger with the parser from section 4 to generate single-best parses to test the SRL models on. It is necessary to train a secondary supertagger over the induced tags because ... See full document

... Unsupervised Grammar Induction Our first method for grammar induction is fully unsuper- vised Viterbi EM training of the Dependency Model with Valence (DMV) (Klein and Manning, 2004), with uniform initialization ... See full document

... We finally decided to take each sense tag as a class tag across different words and transform the disambiguation problem into a normal multi-class categorization problem. For example, in the Eng- lish datasets, all ... See full document

... Recently, there has been interest in distributional word representations for natural language processing. Such representations are typically learned from a large corpus using neural networks (e.g., Weston et al. (2008)), ... See full document

... creating training data for semantic role ...hypothetical semantic role labeler without having to annotate more data ...FrameNet-style semantic role label- ing is, to our ... See full document

... including semantic role labeling ...for training a model based on new annotation, as well as when adding domain-specific annotation to a general corpus for domain ... See full document

... The grand challenge of Machine Reading (Etzioni et al., 2006) requires, as a key step, a scalable system for extracting information from large, het- erogeneous, unstructured text. The traditional ap- proaches to ... See full document

... tasks. Training a fully joint model from scratch is also unrealistic because it requires text that is an- notated with all the tasks, thus making joint train- ing implausible from a learning theoretic perspective ... See full document

... The training is sped up by using the DP pro- cedure only for the exceptional ...the training of SVMs for semantic role labeling using these kernels can be sped up by a factor of several ... See full document

... of Semantic Role Labeling, show- ing that our approach can noticeably reduce training time while yielding almost unaffected classification accuracy, thus allowing us to handle larger data sets ... See full document

... used by the syntactic parsing community (Gildea, 2001). The test set was generated by selecting ev- ery 10 th sentence in the Brown Corpus. We also held out the development set used by Bacchiani et al., (2006) to tune ... See full document

... task, semantic role la- beling (SRL) aims to discover the semantic roles for each predicate within one ...syntax-enhanced self-attention model and compare it with other two strong baseline ... See full document

... With a common training set, selection can be done based on the prediction of both classifiers together. In one approach, only samples with the same predicted labels by both classifiers are selected ... See full document

... in training, and we ensure that every training instance is seen at least once per ...polyglot training is the use of pretrained multilingual word vectors, which allow represent- ing entirely distinct ... See full document

... Although the focus of this work has been on semi-supervised learning, we have developed a competitive SRL system which could be used on its own, after being trained on labeled data. Fol- lowing previous work (Strubell et ... See full document

... In this work, we consider additional model that integrates predicate disambiguation and argument labeling into one sequence labeling model. In or- der to implement an end-to-end model, we intro- duce a ... See full document