Douglas Roland

How Verb Subcategorization Frequencies are Affected by Corpus Choice

The probabilistic relation between verbs and their arguments plays an important role in modern statistical parsers and supertaggers, and in psychological theories of language processing. But these probabilities are computed in very different ways by the two sets of researchers. Computational linguists compute verb subcategorization probabilities from large corpora while psycholinguists compute them from psychological studies (sentence production and completion tasks). Recent studies have found differences between corpus frequencies and psycholinguistic measures. We analyze subcategorization frequencies from four different corpora: psychological sentence production data (Connine et al. 1984), written text (Brown and WSJ), and telephone conversation data (Switchboard). We find two different sources for the differences. Discourse influence is a result of how verb use is affected by different discourse types such as narrative, connected discourse, and single sentence productions. Semantic influence is a result of different corpora using different senses of verbs, which have different subcategorization frequencies. We conclude that verb sense and discourse type play an important role in the frequencies observed in different experimental and corpus based sources of verb subcategorization frequencies.

Verb subcategorization frequencies: American English corpus data, methodological studies, and cross-corpus comparisons

Verb subcategorization frequencies (verb biases) have been widely studied in psycholinguistics and play an important role in human sentence processing. Yet available resources on subcategorization frequencies suffer from limited coverage, limited ecological validity, and divergent coding criteria. Prior estimates of verb transitivity, for example, vary widely with corpus size, coverage, and coding criteria. This article provides norming data for 281 verbs of interest to psycholinguistic research, sampled from a corpus of American English, along with a detailed coding manual. We examine the effect on transitivity bias of various coding decisions and methods of computing verb biases.

Semantic similarity, predictability, and models of sentence processing

The effects of word predictability and shared semantic similarity between a target word and other words that could have taken its place in a sentence on language comprehension are investigated using data from a reading time study, a sentence completion study, and linear mixed-effects regression modeling. We find that processing is facilitated if the different possible words that could occur in a given context are semantically similar to each other, meaning that processing is affected not only by the nature of the words that do occur, but also the relationships between the words that do occur and those that could have occurred. We discuss possible causes of the semantic similarity effect and point to possible limitations of using probability as a model of cognitive effort.

Verb Subcategorization Frequency Differences between Business- News and Balanced Corpora: The Role of Verb Sense

We explore the differences in verb subcategorization frequencies across several corpora in an effort to obtain stable cross corpus subcategorization probabilities for use in norming psychological experiments. For the 64 single sense verbs we looked at, subcategorization preferences were remarkably stable between British and American corpora, and between balanced corpora and financial news corpora. Of the verbs that did show differences, these differences were generally found between the balanced corpora and the financial news data. We show that all or nearly all of these shifts in subcategorization are realised via (often subtle) word sense differences. This is an interesting observation in itself, and also suggests that stable cross corpus subcategorization frequencies may be found when verb sense is adequately controlled.