Dependency parsers show syntactic relations between words using a directed graph, but comparing dependency parsers is difficult because of differences in theoretical models. We describe a system to convert dependency models to a structural grammar used in grammar education. Doing so highlights features that are potentially overlooked in the dependency graph, as well as exposing potential weakne...

We compare two different types of extraction patterns for automatically deriving semantic information from text: lexical patterns, built from words and word class information, and dependency patterns with syntactic information obtained from a full parser. We are particularly interested in whether the richer linguistic information provided by a parser allows for a better performance of subsequen...

We model a dependency graph as a book, a particular kind of topological space, for semantic dependency parsing. The spine of the book is made up of a sequence of words, and each page contains a subset of noncrossing arcs. To build a semantic graph for a given sentence, we design new Maximum Subgraph algorithms to generate noncrossing graphs on each page, and a Lagrangian Relaxation-based algori...

A POS-tagger can be used in front of a parser to reduce the number of combinations of possible dependency trees which, in the majority, give spurious analyses. In the paper we compare the results of the addition of three morphological taggers to the parser of the CDG Lab. The experimental results show that these models perform better than the model which do not use a morphological tagger at the...

Almost all current dependency parsers classify based on millions of sparse indicator features. Not only do these features generalize poorly, but the cost of feature computation restricts parsing speed significantly. In this work, we propose a novel way of learning a neural network classifier for use in a greedy, transition-based dependency parser. Because this classifier learns and uses just a ...

DeSR is a statistical transition-based dependency parser which learns from annotated corpora which actions to perform for building parse trees while scanning a sentence. We describe the experiments performed for the ICON 2010 Tools Contest on Indian Dependency Parsing. DesR was configured to exploit specific features from the Indian treebanks. The submitted run used a stacked combination of fou...

In this paper we explore the effect of selftraining on Hindi dependency parsing. We consider a state-of-the-art Hindi dependency parser and apply self-training by using a large raw corpus. We consider two types of raw corpus, one from same domain as of training and testing data and the other from different domain. We also do an experiment, where we add small gold-standard data to the training s...

We investigate the problem of parsing conversational data of morphologically-rich languages such as Hindi where argument scrambling occurs frequently. We evaluate a state-of-the-art non-linear transitionbased parsing system on a new dataset containing 506 dependency trees for sentences from Bollywood (Hindi) movie scripts and Twitter posts of Hindi monolingual speakers. We show that a dependenc...

Dependency structures do not have the information of phrase categories in phrase structure grammar. Thus, dependency parsing relies heavily on the lexical information of words. This paper discusses our investigation into the effectiveness of lexicalization in dependency parsing. Specifically, by restricting the degree of lexicalization in the training phase of a parser, we examine the change in...

We propose a transition-based dependency parser using Recurrent Neural Networks with Long Short-Term Memory (LSTM) units. This extends the feedforward neural network parser of Chen and Manning (2014) and enables modelling of entire sequences of shift/reduce transition decisions. On the Google Web Treebank, our LSTM parser is competitive with the best feedforward parser on overall accuracy and n...