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Thursday, August 13 • 1:35pm - 2:00pm
Assessing the Probability for a Noun to be Countable

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It is difficult for ESL learners to discern if a noun is countable or not and in what context. This study applies Machine Learning techniques and tries to make it clear in what conditions nouns should be treated as countable or not.

A stochastic learning model like a well-known Bayesian Network is used for the purpose. The model is trained to estimate the probability of simultaneous appearance of countable/uncountable nouns with other non-target words.

Native English texts taken from a corpus are converted into Latent Semantic Index (LSI) vectors with part-of-speech tags and used to train the model. An LSI is an indexing number of words, which becomes the same when words are used in same or similar contexts and consequently have the same or similar meaning. Thus, LSI vectors make the word space compact and reduce ambiguity. The results will be shown as a probability in which nouns should be treated as countable or uncountable in the contexts under examination. The high probability means it is easy to determine whether nouns should be treated as countable or uncountable in the contexts. On the contrary, the low probability means it is difficult to determine.

Such information would be useful for both teachers and students of ESL. Furthermore, the trained model can be applied to machine translation systems. There are several related works in the field. Baldwin and Bold (2003) have proposed a clustering method to estimate countability from corpus data. Nagata et al. (2006) have proposed a method to determine countability of nouns. However, they used deterministic models and could not estimate the probability of countability.

We believe the probability is important to determine what kind of context is difficult for ESL students to discern countability and what contexts should be used in teaching and learning noun countability.


Thursday August 13, 2015 1:35pm - 2:00pm EDT
CGIS South S050 1730 Cambridge St, Cambridge, MA