Programme booklet (pdf)

Abstract
38
CLIN 21 – CONFERENCE PROGRAMME
Computing Semantic Relations from Heterogeneous
Information Sources
Panchenko, Alexander
UCL CENTAL
Computation of semantic relations between terms or concepts is a general problem in
Natural Language Processing and a subtask of automatic thesaurus construction.
This work describes and compares available heterogeneous information sources which
can be used for mining semantic relations such as texts, electronic dictionaries and
encyclopedias, lexical ontologies and thesauri, folksonomies, surfaces of words, query
logs of search engines, and so forth. Most of the existing algorithms use a single
information source for extracting semantic knowledge: Distributional Analysis relies on
text, Extented Lesk uses dictionary definitions, Jiang-Conrath distance employs a
semantic network such as WordNet and so on. We show that different methods
capture different aspects of the terms’ relatedness: while one acquires similarities of
word contexts, others capture similarities of syntactic contexts, term definitions,
surfaces forms etc.
In these settings, there is a need for a general model capable to aggregate different
aspects of semantic similarity from all available information sources and methods in an
optimal and consistent way. We discuss how such a model can be implemented with a
linear combination, and using tensors (i.e. multi-way arrays). We describe two ways of
using tensors for calculation of semantic relations in the context of multiple
information sources, which we call “adjacency tensor” and “feature tensor”. The sparse
tensor factorization methods PARAFAC, Non-negative Tensor Factorization (NTF), and
Memory-Efficient Tucker (MET) are suggested in order to fusion information about
terms from different methods and information sources. We conclude that tensors can
be used for representing terms, while tensor factorizations can serve to generalize data
about terms’ relatedness.
Corresponding author: alexander.panchenko@student.uclouvain.be