Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Explaining away ambiguity: Learning verb selectional preference with
  Bayesian networks   This paper presents a Bayesian model for unsupervised learning of verb
selectional preferences. For each verb the model creates a Bayesian network
whose architecture is determined by the lexical hierarchy of Wordnet and whose
parameters are estimated from a list of verb-object pairs found from a corpus.
``Explaining away'', a well-known property of Bayesian networks, helps the
model deal in a natural fashion with word sense ambiguity in the training data.
On a word sense disambiguation test our model performed better than other state
of the art systems for unsupervised learning of selectional preferences.
Computational complexity problems, ways of improving this approach and methods
for implementing ``explaining away'' in other graphical frameworks are
discussed.

11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg