This paper studies how word embeddings trained on the British National Corpus interact with part of speech boundaries. Our work targets the Universal PoS tag set, which is currently actively being used for annotation of a range of languages. We experiment with training classifiers for predicting PoS tags for words based on their embeddings. The results show that the information about PoS affiliation contained in the distributional vectors allows us to discover groups of words with distributional patterns that differ from other words of the same part of speech. This data often reveals hidden inconsistencies of the annotation process or guidelines. At the same time, it supports the notion of soft' or graded' part of speech affiliations. Finally, we show that information about PoS is distributed among dozens of vector components, not limited to only one or two features.
It seems clear that one can infer data about PoS classes of words from distributional models in general, including embedding models. As a next step then, these models could also prove useful for deeper analysis of part of speech boundaries, leading to discovery of separate words or whole classes that tend to behave in non-typical ways. Discovering such cases is one possible way to improve the performance of existing automatic PoS taggers @cite_1 . These outliers' may signal the necessity to revise the annotation strategy or classification system in general. Section describes the process of constructing typical PoS clusters and detecting words that belong to a cluster different from their traditional annotation.
@cite_1
2170206653
This paper presents an algorithm for tagging words whose part-of-speech properties are unknown. Unlike previous work, the algorithm categorizes word tokens in context instead of word types. The algorithm is evaluated on the Brown Corpus.