Stem alternations in Kiranti and their implications for the morphology–phonology interface

Stem alternation is present in the verbal inflection of all documented Kiranti languages, where it ranges from the straightforward phonologically conditioned (e.g. Athpariya and Chintang) to the purely morphological and baroque (e.g. Khaling and Dumi). This paper surveys stem alternation patterns across the whole family. Its main finding is that, unlike the morphological stem alternations of West Kiranti, the phonologically-conditioned stem alternations of East Kiranti are characterized by a very striking distributional similarity (often identity) across languages, even in the presence of quite drastic affixal changes. This and other findings suggest that these stem alternation patterns should be regarded as a (morphomic) grammatical phenomenon of its own right, despite being derivable from the forms of suffixes. Furthermore, comparison with West Kiranti suggests that this coextensiveness with a coherent phonological environment actually enhances some typically morphomic traits such as diachronic resilience and productivity.

An algorithm for learning phonological classes from distributional similarity

An important question in phonology is to what degree the learner uses distributional information rather than substantive properties of speech sounds when learning phonological structure. This paper presents an algorithm that learns phonological classes from only distributional information: the contexts in which sounds occur. The input is a segmental corpus, and the output is a set of phonological classes. The algorithm is first tested on an artificial language, with both overlapping and nested classes reflected in the distribution, and retrieves the expected classes, performing well as distributional noise is added. It is then tested on four natural languages. It distinguishes between consonants and vowels in all cases, and finds more detailed, language-specific structure. These results improve on past approaches, and are encouraging, given the paucity of the input. More refined models may provide additional insight into which phonological classes are apparent from the distributions of sounds in natural languages.

Efficient Computational linguistics Framework for Concept Drift Detection

Semantic drift is a common problem in iterative information extraction. Unsupervised bagging and incorporated distributional similarity is used to reduce the difficulty of semantic drift in iterative bootstrapping algorithms, particularly when extracting large semantic lexicons. Compared to previous approaches which usually incur substantial loss in recall, DP-based cleaning method can effectively clean a large proportion of semantic drift errors while keeping a high recall.