We explore the effect of self-training and co-training on Hindi dependency parsing. We use Malt parser, which is a state-ofthe-art Hindi dependency parser, and apply self-training using a large unannotated corpus. For co-training, we use MST parser with comparable accuracy to the Malt parser. Experiments are performed using two types of raw corpora— one from the same domain as the test data and...

We create a transition-based dependency parser using a general purpose learning to search system. The result is a fast and accurate parser for many languages. Compared to other transition-based dependency parsing approaches, our parser provides similar statistical and computational performance with best-known approaches while avoiding various downsides including randomization, extra feature req...

This paper presents a set of experiments performed on parsing the Basque Dependency Treebank. We have applied feature propagation to dependency parsing, experimenting the propagation of several morphosyntactic feature values. In the experiments we have used the output of a parser to enrich the input of a second parser. Both parsers have been generated by Maltparser, a freely data-driven depende...

Much work has been done on building a parser for natural languages, but most of this work has concentrated on supervised parsing. Unsupervised parsing is a less explored area, and unsupervised dependency parser has hardly been tried. In this paper we present two approaches for building an unsupervised dependency parser. One approach is based on learning dependency relations and the other on lea...

While dependency parsers reach very high overall accuracy, some dependency relations are much harder than others. In particular, dependency parsers perform poorly in coordination construction (i.e., correctly attaching the conj relation). We extend a state-of-the-art dependency parser with conjunction-specific features, focusing on the similarity between the conjuncts head words. Training the e...

This paper presents an effective dependency parsing approach of incorporating short dependency information from unlabeled data. The unlabeled data is automatically parsed by a deterministic dependency parser, which can provide relatively high performance for short dependencies between words. We then train another parser which uses the information on short dependency relations extracted from the...

This paper presents some preliminary results of our dependency parser for Thai. It is part of an ongoing project in developing a syntactically annotated Thai corpus. The parser has been trained and tested by using the complete part of the corpus. The parser achieves 83.64% as the root accuracy, 78.54% as the dependency accuracy and 53.90% as the complete sentence accuracy. The trained parser wi...

We investigate the stacking of dependency and phrase structure parsers, i.e. we define features from the output of a phrase structure parser for a dependency parser and vice versa. Our features are based on the original form of the external parses and we also compare this approach to converting phrase structures to dependencies then applying standard stacking on the converted output. The propos...

This paper introduces our Chinese semantic dependency parsing system for Task 9 of SemEval 2016. Our system has two components: a parser trained using the Berkeley Grammar Trainer on the Penn Chinese Treebank reannotated in a Generalized Categorial Grammar, and a multinomial logistic regression classifier. We first parse the data with the automatic parser to obtain predicate-argument dependenci...

We present a neural transition-based parser for spinal trees, a dependency representation of constituent trees. The parser uses Stack-LSTMs that compose constituent nodes with dependency-based derivations. In experiments, we show that this model adapts to different styles of dependency relations, but this choice has little effect for predicting constituent structure, suggesting that LSTMs induc...