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Previous studies of data-driven dependency parsing have shown that the distribution of parsing errors are correlated with theoretical properties of the models used for learning and inference. In this paper, …...

We propose a novel machine learning task that consists in learning to predict which words in a text are fixated by a reader. In a first pilot experiment, we show …...

AbstractJoint morphological and syntactic analysis has been proposed as a way of improving parsing accuracy for richly inflected languages. Starting from a transition-based model for joint part-of-speech tagging and dependency …...

We propose a transition-based model for joint word segmentation, POS tagging and text normalization. Different from pre- vious methods, the model can be trained on standard text corpora, overcoming the …...

Two of the main problems in creating an Indonesian parser with high accuracy are the lack of sentence diversity in treebank used for training and suboptimal uses of parsing techniques. …...

AbstractTraditional methods for deep NLG adopt pipeline approaches comprising stages such as constructing syntactic input, predicting function words, linearizing the syntactic input and generating the surface forms. Though easier to …...

AbstractSyntax has been a useful source of information for statistical RST discourse parsing. Under the neural setting, a common approach integrates syntax by a recursive neural network (RNN), requiring discrete …...

AbstractIn Chinese dependency parsing, the joint model of word segmentation, POS tagging and dependency parsing has become the mainstream framework because it can eliminate error propagation and share knowledge, where …...

AbstractUnlike widely used Named Entity Recognition (NER) data sets in generic domains, biomedical NER data sets often contain mentions consisting of discontinuous spans. Conventional sequence tagging techniques encode Markov assumptions …...