Suzanne Stevenson

Automatic verb classification based on statistical distributions of argument structure

Automatic acquisition of lexical knowledge is critical to a wide range of natural language processing tasks. Especially important is knowledge about verbs, which are the primary source of relational information in a sentence-the predicate-argument structure that relates an action or state to its participants (i.e., who did what to whom). In this work, we report on supervised learning experiments to automatically classify three major types of English verbs, based on their argument structure-specifically, the thematic roles they assign to participants. We use linguistically-motivated statistical indicators extracted from large annotated corpora to train the classifier, achieving 69.8 accuracy for a task whose baseline is 34, and whose expert-based upper bound we calculate at 86.5. A detailed analysis of the performance of the algorithm and of its errors confirms that the proposed features capture properties related to the argument structure of the verbs. Our results validate our hypotheses that knowledge about thematic relations is crucial for verb classification, and that it can be gleaned from a corpus by automatic means. We thus demonstrate an effective combination of deeper linguistic knowledge with the robustness and scalability of statistical techniques.

Semantic role labeling: an introduction to the special issue

Semantic role labeling, the computational identification and labeling of arguments in text, has become a leading task in computational linguistics today. Although the issues for this task have been studied for decades, the availability of large resources and the development of statistical machine learning methods have heightened the amount of effort in this field. This special issue presents selected and representative work in the field. This overview describes linguistic background of the problem, the movement from linguistic theories to computational practice, the major resources that are being used, an overview of steps taken in computational systems, and a description of the key issues and results in semantic role labeling (as revealed in several international evaluations). We assess weaknesses in semantic role labeling and identify important challenges facing the field. Overall, the opportunities and the potential for useful further research in semantic role labeling are considerable.

An Unsupervised Model for Text Message Normalization

Cell phone text messaging users express themselves briefly and colloquially using a variety of creative forms. We analyze a sample of creative, non-standard text message word forms to determine frequent word formation processes in texting language. Drawing on these observations, we construct an unsupervised noisy-channel model for text message normalization. On a test set of 303 text message forms that differ from their standard form, our model achieves 59% accuracy, which is on par with the best supervised results reported on this dataset.

A Probabilistic Computational Model of Cross-Situational Word Learning

Words are the essence of communication: They are the building blocks of any language. Learning the meaning of words is thus one of the most important aspects of language acquisition: Children must first learn words before they can combine them into complex utterances. Many theories have been developed to explain the impressive efficiency of young children in acquiring the vocabulary of their language, as well as the developmental patterns observed in the course of lexical acquisition. A major source of disagreement among the different theories is whether children are equipped with special mechanisms and biases for word learning, or their general cognitive abilities are adequate for the task. We present a novel computational model of early word learning to shed light on the mechanisms that might be at work in this process. The model learns word meanings as probabilistic associations between words and semantic elements, using an incremental and probabilistic learning mechanism, and drawing only on general cognitive abilities. The results presented here demonstrate that much about word meanings can be learned from naturally occurring child-directed utterances (paired with meaning representations), without using any special biases or constraints, and without any explicit developmental changes in the underlying learning mechanism. Furthermore, our model provides explanations for the occasionally contradictory child experimental data, and offers predictions for the behavior of young word learners in novel situations.

Unsupervised type and token identification of idiomatic expressions

Idiomatic expressions are plentiful in everyday language, yet they remain mysterious, as it is not clear exactly how people learn and understand them. They are of special interest to linguists, psycholinguists, and lexicographers, mainly because of their syntactic and semantic idiosyncrasies as well as their unclear lexical status. Despite a great deal of research on the properties of idioms in the linguistics literature, there is not much agreement on which properties are characteristic of these expressions. Because of their peculiarities, idiomatic expressions have mostly been overlooked by researchers in computational linguistics. In this article, we look into the usefulness of some of the identified linguistic properties of idioms for their automatic recognition. Specifically, we develop statistical measures that each model a specific property of idiomatic expressions by looking at their actual usage patterns in text. We use these statistical measures in a type-based classification task where we automatically separate idiomatic expressions (expressions with a possible idiomatic interpretation) from similar-on-the-surface literal phrases (for which no idiomatic interpretation is possible). In addition, we use some of the measures in a token identification task where we distinguish idiomatic and literal usages of potentially idiomatic expressions in context.

Competition and recency in a hybrid network model of syntactic disambiguation

The competitive attachment model of human parsing is a hybrid connectionist architecture consisting of a distributed feature passing method for establishing syntactic relations within the network, and a numeric competition mechanism for resolving ambiguities, which applies to all syntactic relations. Because the approach employs a uniform mechanism for establishing syntactic relations, and a single competition mechanism for disambiguation, the model can capture general behaviors of the human parser that hold across a range of syntactic constructions. In particular, attachment and binding relations are similarly processed and are therefore subject to the very same influences of disambuguation and processing over time. An important influence on the competitive disambiguation process is distance within the network. Decay of numeric activation, along with distributed feature passing through the network structure, has an unavoidable effect on the outcome of attachment and binding competitions. Inherent properties of the model thus lead to a principled explanation of recency effects in the human parsing of both attachment and filler/gap ambiguities.

Pulling their Weight: Exploiting Syntactic Forms for the Automatic Identification of Idiomatic Expressions in Context

Much work on idioms has focused on type identification, i.e., determining whether a sequence of words can form an idiomatic expression. Since an idiom type often has a literal interpretation as well, token classification of potential idioms in context is critical for NLP. We explore the use of informative prior knowledge about the overall syntactic behaviour of a potentially-idiomatic expression (type-based knowledge) to determine whether an instance of the expression is used idiomatically or literally (token-based knowledge). We develop unsupervised methods for the task, and show that their performance is comparable to that of state-of-the-art supervised techniques.

A Computational Model of Early Argument Structure Acquisition

How children go about learning the general regularities that govern language, as well as keeping track of the exceptions to them, remains one of the challenging open questions in the cognitive science of language. Computational modeling is an important methodology in research aimed at addressing this issue. We must determine appropriate learning mechanisms that can grasp generalizations from examples of specific usages, and that exhibit patterns of behavior over the course of learning similar to those in children. Early learning of verb argument structure is an area of language acquisition that provides an interesting testbed for such approaches due to the complexity of verb usages. A range of linguistic factors interact in determining the felicitous use of a verb in various constructions-associations between syntactic forms and properties of meaning that form the basis for a number of linguistic and psycholinguistic theories of language. This article presents a computational model for the representation, acquisition, and use of verbs and constructions. The Bayesian framework is founded on a novel view of constructions as a probabilistic association between syntactic and semantic features. The computational experiments reported here demonstrate the feasibility of learning general constructions, and their exceptions, from individual usages of verbs. The behavior of the model over the timecourse of acquisition mimics, in relevant aspects, the stages of learning exhibited by children. Therefore, this proposal sheds light on the possible mechanisms at work in forming linguistic generalizations and maintaining knowledge of exceptions.

Distinguishing Subtypes of Multiword Expressions Using Linguistically-Motivated Statistical Measures

We identify several classes of multiword expressions that each require a different encoding in a (computational) lexicon, as well as a different treatment within a computational system. We examine linguistic properties pertaining to the degree of semantic idiosyncrasy of these classes of expressions. Accordingly, we propose statistical measures to quantify each property, and use the measures to automatically distinguish the classes.

Statistical measures of the semi-productivity of light verb constructions

We propose a statistical measure for the degree of acceptability of light verb constructions, such as take a walk, based on their linguistic properties. Our measure shows good correlations with human ratings on unseen test data. Moreover, we find that our measure correlates more strongly when the potential complements of the construction (such as walk, stroll, or run) are separated into semantically similar classes. Our analysis demonstrates the systematic nature of the semi-productivity of these constructions.