The Distributional Learning of Multi-Word Expressions: A Computational Approach (2015)

There has been much recent research in corpus and computational linguistics on distributional learning algorithms—computer code that induces latent linguistic structures in corpus data based on co-occurrences of transcribed units in that data. These algorithms have varied applications, from the investigation of human cognitive processes to the corpus extraction of relevant linguistic structures for lexicographic, second language learning, or natural language processing applications, among others. They also operate at various levels of linguistic structure, from phonetics to syntax. One area of research on distributional learning algorithms in which there remains relatively little work is the learning of multi-word, memorized, formulaic sequences, based on the co-occurrences of words. Examples of such multi-word expressions (MWEs) include kick the bucket, New York City, sit down, and as a matter of fact. In this dissertation, I present a novel computational approach to the distributional learning of such sequences in corpora. Entitled MERGE (Multi-word Expressions from the Recursive Grouping of Elements), my algorithm iteratively works by (1) assigning a statistical ‘attraction’ score to each two-word sequence (bigram) in a corpus, based on the individual and co-occurrence frequencies of these two words in that corpus; and (2) merging the highest-scoring bigram into a single, lexicalized unit. These two steps then repeat until some maximum number of iterations or minimum score threshold is reached (since, broadly speaking, the winning score progressively decreases with increasing iterations). Because one (or both) of the ‘words’ making up a winning bigram may be an output merged item from a previous iteration, the algorithm is able to learn MWEs that are in principle of any length (e.g., apple pie versus I’ll believe it when I see it). Moreover, these MWEs may contain one or more discontinuities of different sizes, up to some maximum size threshold (measured in words) specified by the user (e.g., as _ as in as tall as and as big as). Typically, the extraction of MWEs has been handled by algorithms that identify only continuous sequences, and in which the user must specify the length(s) of the sequences to be extracted beforehand; thus, MERGE offers a bottom-up, distributional-based approach that addresses these issues.In the present dissertation, in addition to describing the algorithm, I report three rating experiments and one corpus-based early child language study that validate the efficacy of MERGE in identifying MWEs. In one experiment, participants rate sequences extracted from a corpus by the algorithm for how well they instantiate true MWEs. As expected, the results reveal that the high-scoring output items that MERGE identifies early in its iterative process are rated as ‘good’ MWEs by participants (based on certain subjective criteria), with the quality of these ratings decreasing for output from later iterations (i.e., output items that were scored lower by the algorithm). In the other two experiments, participants rate high-ranking output both from MERGE and from an existing algorithm from the literature that also learns MWEs of various lengths—the Adjusted Frequency List (Brook O’Donnell 2011). Comparison of participant ratings reveals that the items that MERGE acquires are rated more highly than those acquired by the Adjusted Frequency List, suggesting that MERGE is a performance frontrunner among distributional learning algorithms of MWEs. More broadly, together the experiments suggest that MERGE acquires representations that are compatible with adult knowledge of formulaic language, and thus it may be useful for any number of research applications that rely on such formulaic language as a unit of analysis.Finally, in a study using two corpora of caregiver-child interactions, I run MERGE on caregiver utterances and then show that, of the MWEs induced by the algorithm, those that go on to be later acquired by the children receive higher scores by the algorithm than those that do not go on to be learned. These results suggest that, when applied to acquisition data, the algorithm is useful for identifying the structures of statistical co-occurrences in the caregiver input that are relevant to children in their acquisition of early multi-word knowledge.Overall, MERGE is shown to be a powerful computational approach to the distributional learning and extraction of MWEs, both when modeling adult knowledge of formulaic language, and when accounting for the early multi-word structures acquired by children.