text: Word sense induction (WSI), or the task of automatically discovering multiple senses or meanings of a word, has three main challenges: domain adaptability, novel sense detection, and sense granularity flexibility. While current latent variable models are known to solve the first two challenges, they are not flexible to different word sense granularities, which differ very much among words, from aardvark with one sense, to play with over 50 senses. Current models either require hyperparameter tuning or nonparametric induction of the number of senses, which we find both to be ineffective. Thus, we aim to eliminate these requirements and solve the sense granularity problem by proposing AutoSense, a latent variable model based on two observations: (1) senses are represented as a distribution over topics, and (2) senses generate pairings between the target word and its neighboring word. These observations alleviate the problem by (a) throwing garbage senses and (b) additionally inducing fine-grained word senses. Results show great improvements over the state-of-the-art models on popular WSI datasets. We also show that AutoSense is able to learn the appropriate sense granularity of a word. Finally, we apply AutoSense to the unsupervised author name disambiguation task where the sense granularity problem is more evident and show that AutoSense is evidently better than competing models. We share our data and code here: this https URL.
  @cite_7: Most work on word sense disambiguation has assumed that word usages are best labeled with a single sense. However, contextual ambiguity or fine-grained senses can potentially enable multiple sense interpretations of a usage. We present a new SemEval task for evaluating Word Sense Induction and Disambiguation systems in a setting where instances may be labeled with multiple senses, weighted by their applicability. Four teams submitted nine systems, which were evaluated in two settings.
  @cite_8: We present MSDA (Major Senses Discovery Algorithm) --a development over the context vector approach to (noun) sense discrimination [20, 24] that uses attributes and values instead of word features to cluster contexts, and does not require for the number of senses to be fixed beforehand. The algorithm achieves a precision of 89 on a dataset including both ambiguous and non-ambiguous nouns, twice that of previous algorithms.
  @cite_27: Given a parallel corpus, if two distinct words in language A, a1 and a2, are aligned to the same word b1 in language B, then this might signal that b1 is polysemous, or it might signal a1 and a2 are synonyms. Both assumptions with successful work have been put forward in the literature. We investigate these assumptions, along with other questions of word sense, by looking at sampled parallel sentences containing tokens of the same type in English, asking how often they mean the same thing when they are: 1. aligned to the same foreign type; and 2. aligned to different foreign types. Results for French-English and Chinese-English parallel corpora show similar behavior: Synonymy is only very weakly the more prevalent scenario, where both cases regularly occur.
  @cite_20: This paper describes our system for Task 11 of SemEval-2013. In the task, participants are provided with a set of ambiguous search queries and the snippets returned by a search engine, and are asked to associate senses with the snippets. The snippets are then clustered using the sense assignments and systems are evaluated based on the quality of the snippet clusters. Our system adopts a preexisting Word Sense Induction (WSI) methodology based on Hierarchical Dirichlet Process (HDP), a non-parametric topic model. Our system is trained over extracts from the full text of English Wikipedia, and is shown to perform well in the shared task.
  @cite_12: We develop a supersense taxonomy for adjectives, based on that of GermaNet, and apply it to English adjectives in WordNet using human annotation and supervised classification. Results show that accuracy for automatic adjective type classification is high, but synsets are considerably more difficult to classify, even for trained human annotators. We release the manually annotated data, the classifier, and the induced supersense labeling of 12,304 WordNet adjective synsets.
  @cite_17: 
summary: Previous works on WSI used context vectors and attributes @cite_8 , pretrained classification systems @cite_12 , and alignment of parallel corpus @cite_27 . In the most recent shared task on WSI @cite_7 , top models used lexical substitution method () @cite_17 and Hierarchical Dirichlet Process trained with additional instances () @cite_20 .