The paper describes the overall design of a new two stage constraint based hybrid approach to dependency parsing. We define the two stages and show how different grammatical construct are parsed at appropriate stages. This division leads to selective identification and resolution of specific dependency relations at the two stages. Furthermore, we show how the use of hard constraints and soft co...

Radically different approaches have been proved to be effective for phrase-structure and dependency parsers in the last decade. Here, we aim to exploit the divergence in these approaches and show the utility of features extracted from the automatic dependency parses of sentences for a discriminative phrase-structure parser. Our experiments show a significant improvement over the state-of-the-ar...

We show that informative lexical categories from a strongly lexicalised formalism such as Combinatory Categorial Grammar (CCG) can improve dependency parsing of Hindi, a free word order language. We first describe a novel way to obtain a CCG lexicon and treebank from an existing dependency treebank, using a CCG parser. We use the output of a supertagger trained on the CCGbank as a feature for a...

In this paper we demonstrate that it is possible to parse dependency structures deterministically in linear time using syntactic heuristic choices. We first prove theoretically that deterministic, linear parsing of dependency structures is possible under certain conditions. We then discuss a fully implemented parser and argue that those conditions hold for at least one natural language. Empiric...

This paper describes a semi-supervised approach to improving statistical dependency parsing using dependency-based word clusters. After applying a baseline parser to unlabeled text, clusters are induced using K-means with word features based on the dependency structures. The parser is then re-trained using information about the clusters, yielding improved parsing accuracy on a range of differen...

We compare three different approaches to parsing into syntactic, bi-lexical dependencies for English: a ‘direct’ data-driven dependency parser, a statistical phrase structure parser, and a hybrid, ‘deep’ grammar-driven parser. The analyses from the latter two are post-converted to bilexical dependencies. Through this ‘reduction’ of all three approaches to syntactic dependency parsers, we determ...

Singlish can be interesting to the ACL community both linguistically as a major creole based on English, and computationally for information extraction and sentiment analysis of regional social media. We investigate dependency parsing of Singlish by constructing a dependency treebank under the Universal Dependencies scheme, and then training a neural network model by integrating English syntact...

We present a transition-based AMR parser that directly generates AMR parses from plain text. We use Stack-LSTMs to represent our parser state and make decisions greedily. In our experiments, we show that our parser achieves very competitive scores on English using only AMR training data. Adding additional information, such as POS tags and dependency trees, improves the results further.

The paper describes the overall design of a new two stage constraint based hybrid approach to dependency parsing. We define the two stages and show how different grammatical construct are parsed at appropriate stages. This division leads to selective identification and resolution of specific dependency relations at the two stages. Furthermore, we show how the use of hard constraints and soft co...