Shane Bergsma

Bootstrapping Path-Based Pronoun Resolution

We present an approach to pronoun resolution based on syntactic paths. Through a simple bootstrapping procedure, we learn the likelihood of coreference between a pronoun and a candidate noun based on the path in the parse tree between the two entities. This path information enables us to handle previously challenging resolution instances, and also robustly addresses traditional syntactic coreference constraints. Highly coreferent paths also allow mining of precise probabilistic gender/number information. We combine statistical knowledge with well known features in a Support Vector Machine pronoun resolution classifier. Significant gains in performance are observed on several datasets.

Discriminative Learning of Selectional Preference from Unlabeled Text

We present a discriminative method for learning selectional preferences from unlabeled text. Positive examples are taken from observed predicate-argument pairs, while negatives are constructed from unobserved combinations. We train a Support Vector Machine classifier to distinguish the positive from the negative instances. We show how to partition the examples for efficient training with 57 thousand features and 6.5 million training instances. The model outperforms other recent approaches, achieving excellent correlation with human plausibility judgments. Compared to Mutual Information, it identifies 66% more verb-object pairs in unseen text, and resolves 37% more pronouns correctly in a pronoun resolution experiment.

An Expectation Maximization Approach to Pronoun Resolution

We propose an unsupervised Expectation Maximization approach to pronoun resolution. The system learns from a fixed list of potential antecedents for each pronoun. We show that unsupervised learning is possible in this context, as the performance of our system is comparable to supervised methods. Our results indicate that a probabilistic gender/number model, determined automatically from unlabeled text, is a powerful feature for this task.

Learning bilingual lexicons using the visual similarity of labeled web images

Speakers of many different languages use the Internet. A common activity among these users is uploading images and associating these images with words (in their own language) as captions, filenames, or surrounding text. We use these explicit, monolingual, image-to-word connections to successfully learn implicit, bilingual, word-to-word translations. Bilingual pairs of words are proposed as translations if their corresponding images have similar visual features. We generate bilingual lexicons in 15 language pairs, focusing on words that have been automatically identified as physical objects. The use of visual similarity substantially improves performance over standard approaches based on string similarity: for generated lexicons with 1000 translations, including visual information leads to an absolute improvement in accuracy of 8-12% over string edit distance alone.

NADA: a robust system for non-referential pronoun detection

We present

Automatic acquisition of gender information for anaphora resolution

\textsc{Nada}

A Ranking Approach to Stress Prediction for Letter-to-Phoneme Conversion

: the Non-Anaphoric Detection Algorithm.

I’m a Belieber: Social Roles via Self-identification and Conceptual Attributes

\textsc{Nada}

Glen, Glenda or Glendale: Unsupervised and Semi-supervised Learning of English Noun Gender

is a novel, publicly-available program that accurately distinguishes between the referential and non-referential pronoun it in raw English text. Like recent state-of-the-art approaches,

Large-scale semi-supervised learning for natural language processing

\textsc{Nada}