Tejaswini Deoskar

Re-estimation of Lexical Parameters for Treebank PCFGs

We present procedures which pool lexical information estimated from unlabeled data via the Inside-Outside algorithm, with lexical information from a treebank PCFG. The procedures produce substantial improvements (up to 31.6% error reduction) on the task of determining subcategorization frames of novel verbs, relative to a smoothed Penn Treebank-trained PCFG. Even with relatively small quantities of unlabeled training data, the re-estimated models show promising improvements in labeled bracketing f-scores on Wall Street Journal parsing, and substantial benefit in acquiring the subcategorization preferences of low-frequency verbs.

Improving Dependency Parsers using Combinatory Categorial Grammar

Subcategorization information is a useful feature in dependency parsing. In this paper, we explore a method of incorporating this information via Combinatory Categorial Grammar (CCG) categories from a supertagger. We experiment with two popular dependency parsers (Malt and MST) for two languages: English and Hindi. For both languages, CCG categories improve the overall accuracy of both parsers by around 0.3-0.5% in all experiments. For both parsers, we see larger improvements specifically on dependencies at which they are known to be weak: long distance dependencies for Malt, and verbal arguments for MST. The result is particularly interesting in the case of the fast greedy parser (Malt), since improving its accuracy without significantly compromising speed is relevant for large scale applications such as parsing the web.

An Incremental Algorithm for Transition-based CCG Parsing

Incremental parsers have potential advantages for applications like language modeling for machine translation and speech recognition. We describe a new algorithm for incremental transition-based Combinatory Categorial Grammar parsing. As English CCGbank derivations are mostly right branching and non-incremental, we design our algorithm based on the dependencies resolved rather than the derivation. We introduce two new actions in the shift-reduce paradigm based on the idea of ‘revealing’ (Pareschi and Steedman, 1987) the required information during parsing. On the standard CCGbank test data, our algorithm achieved improvements of 0.88% in labeled and 2.0% in unlabeled F-score over a greedy non-incremental shift-reduce parser.

Shift-Reduce CCG Parsing using Neural Network Models

We present a neural network based shift- reduce CCG parser, the first neural-network based parser for CCG. We also study the im- pact of neural network based tagging mod- els, and greedy versus beam-search parsing, by using a structured neural network model. Our greedy parser obtains a labeled F-score of 83.27%, the best reported result for greedy CCG parsing in the literature (an improve- ment of 2.5% over a perceptron based greedy parser) and is more than three times faster. With a beam, our structured neural network model gives a labeled F-score of 85.57% which is 0.6% better than the perceptron based counterpart.

Learning structural dependencies of words in the Zipfian Tail

Using semi-supervised EM, we learn finegrained but sparse lexical parameters of a generative parsing model (a PCFG) initially estimated over the Penn Treebank. Our lexical parameters employ supertags, which encode complex structural information at the pre-terminal level, and are particularly sparse in labeled data -- our goal is to learn these for words that are unseen or rare in the labeled data. In order to guide estimation from unlabeled data, we incorporate both structural and lexical priors from the labeled data. We get a large error reduction in parsing ambiguous structures associated with unseen verbs, the most important case of learning lexico-structural dependencies. We also obtain a statistically significant improvement in labeled bracketing score of the treebank PCFG, the first successful improvement via semi-supervised EM of a generative structured model already trained over large labeled data.