Tune your brown clustering, please

Brown clustering, an unsupervised hierarchical clustering technique based on ngram mutual information, has proven useful in many NLP applications. However, most uses of Brown clustering employ the same default configuration; the appropriateness of this configuration has gone predominantly unexplored. Accordingly, we present information for practitioners on the behaviour of Brown clustering in order to assist hyper-parametre tuning, in the form of a theoretical model of Brown clustering utility. This model is then evaluated empirically in two sequence labelling tasks over two text types. We explore the dynamic between the input corpus size, chosen number of classes, and quality of the resulting clusters, which has an impact for any approach using Brown clustering. In every scenario that we examine, our results reveal that the values most commonly used for the clustering are sub-optimal

Meaning and Grammar of Nouns and Verbs

The papers collected in this book cover contemporary and original research on semantic and grammatical issues of nouns and noun phrases, verbs and sentences, and aspects of the combination of nouns and verbs, in a great variety of languages. A special focus is put on noun types, tense and aspect semantics, granularity of verb meaning, and subcompositionality. The investigated languages and language groups include Austronesian, East Asian, Slavic, German, English, Hungarian and Lakhota. The collection provided in this book will be of interest to researchers and advanced students specialising in the fields of semantics, morphology, syntax, typology, and cognitive sciences