Rebecca Hwa

Bootstrapping parsers via syntactic projection across parallel texts

Broad coverage, high quality parsers are available for only a handful of languages. A prerequisite for developing broad coverage parsers for more languages is the annotation of text with the desired linguistic representations (also known as “treebanking”). However, syntactic annotation is a labor intensive and time-consuming process, and it is difficult to find linguistically annotated text in sufficient quantities. In this article, we explore using parallel text to help solving the problem of creating syntactic annotation in more languages. The central idea is to annotate the English side of a parallel corpus, project the analysis to the second language, and then train a stochastic analyzer on the resulting noisy annotations. We discuss our background assumptions, describe an initial study on the “projectability” of syntactic relations, and then present two experiments in which stochastic parsers are developed with minimal human intervention via projection from English.

Example selection for bootstrapping statistical parsers

This paper investigates bootstrapping for statistical parsers to reduce their reliance on manually annotated training data. We consider both a mostly-unsupervised approach, cotraining, in which two parsers are iteratively re-trained on each others output; and a semi-supervised approach, corrected co-training, in which a human corrects each parsers output before adding it to the training data. The selection of labeled training examples is an integral part of both frameworks. We propose several selection methods based on the criteria of minimizing errors in the data and maximizing training utility. We show that incorporating the utility criterion into the selection method results in better parsers for both frameworks.

RECOGNIZING STRONG AND WEAK OPINION CLAUSES

There has been a recent swell of interest in the automatic identification and extraction of opinions and emotions in text. In this paper, we present the first experimental results classifying the intensity of opinions and other types of subjectivity and classifying the subjectivity of deeply nested clauses. We use a wide range of features, including new syntactic features developed for opinion recognition. We vary the learning algorithm and the feature organization to explore the effect this has on the classification task. In 10‐fold cross‐validation experiments using support vector regression, we achieve improvements in mean‐squared error over baseline ranging from 49% to 51%. Using boosting, we achieve improvements in accuracy ranging from 23% to 96%.

Bootstrapping statistical parsers from small datasets

We present a practical co-training method for bootstrapping statistical parsers using a small amount of manually parsed training material and a much larger pool of raw sentences. Experimental results show that unlabelled sentences can be used to improve the performance of statistical parsers. In addition, we consider the problem of boot-strapping parsers when the manually parsed training material is in a different domain to either the raw sentences or the testing material. We show that boot-strapping continues to be useful, even though no manually produced parses from the target domain are used.

Evaluating Translational Correspondence using Annotation Projection

Recently, statistical machine translation models have begun to take advantage of higher level linguistic structures such as syntactic dependencies. Underlying these models is an assumption about the directness of translational correspondence between sentences in the two languages; however, the extent to which this assumption is valid and useful is not well understood. In this paper, we present an empirical study that quantifies the degree to which syntactic dependencies are preserved when parses are projected directly from English to Chinese. Our results show that although the direct correspondence assumption is often too restrictive, a small set of principled, elementary linguistic transformations can boost the quality of the projected Chinese parses by 76% relative to the unimproved baseline.

Sample Selection for Statistical Parsing

Corpus-based statistical parsing relies on using large quantities of annotated text as training examples. Building this kind of resource is expensive and labor-intensive. This work proposes to use sample selection to find helpful training examples and reduce human effort spent on annotating less informative ones. We consider several criteria for predicting whether unlabeled data might be a helpful training example. Experiments are performed across two syntactic learning tasks and within the single task of parsing across two learning models to compare the effect of different predictive criteria. We find that sample selection can significantly reduce the size of annotated training corpora and that uncertainty is a robust predictive criterion that can be easily applied to different learning models.

Co-training for predicting emotions with spoken dialogue data

Natural Language Processing applications often require large amounts of annotated training data, which are expensive to obtain. In this paper we investigate the applicability of Co-training to train classifiers that predict emotions in spoken dialogues. In order to do so, we have first applied the wrapper approach with Forward Selection and Naive Bayes, to reduce the dimensionality of our feature set. Our results show that Co-training can be highly effective when a good set of features are chosen.

Supervised Grammar Induction using Training Data with Limited Constituent Information

Corpus-based grammar induction generally relies on hand-parsed training data to learn the structure of the language. Unfortunately, the cost of building large annotated corpora is prohibitively expensive. This work aims to improve the induction strategy when there are few labels in the training data. We show that the most informative linguistic constituents are the higher nodes in the parse trees, typically denoting complex noun phrases and sentential clauses. They account for only 20% of all constituents. For inducing grammars from sparsely labeled training data (e.g., only higher-level constituent labels), we propose an adaptation strategy, which produces grammars that parse almost as well as grammars induced from fully labeled corpora. Our results suggest that for a partial parser to replace human annotators, it must be able to automatically extract higher-level constituents rather than base noun phrases.

Sample Selection for Statistical Grammar Induction

Corpus-based grammar induction relies on using many hand-parsed sentences as training examples. However, the construction of a training corpus with detailed syntactic analysis for every sentence is a labor-intensive task. We propose to use sample selection methods to minimize the amount of annotation needed in the training data, thereby reducing the workload of the human annotators. This paper shows that the amount of annotated training data can be reduced by 36% without degrading the quality of the induced grammars.

A Backoff Model for Bootstrapping Resources for Non-English Languages

The lack of annotated data is an obstacle to the development of many natural language processing applications; the problem is especially severe when the data is non-English. Previous studies suggested the possibility of acquiring resources for non-English languages by bootstrapping from high quality English NLP tools and parallel corpora; however, the success of these approaches seems limited for dissimilar language pairs. In this paper, we propose a novel approach of combining a bootstrapped resource with a small amount of manually annotated data. We compare the proposed approach with other bootstrapping methods in the context of training a Chinese Part-of-Speech tagger. Experimental results show that our proposed approach achieves a significant improvement over EM and self-training and systems that are only trained on manual annotations.