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Semantic, Terminological and Linguistic Features for Sentence Classification
1. Introduction
Tobias Wunner and Paul Buitelaar
Unit for Natural Language Processing,
Digital Enterprise Research Institute, National University of Ireland, Galway
Email: firstname.lastname@deri.org
Specialized domains often use shared vocabularies to
agree on terminology and semantics of the models used
in the community. However most of the information
and knowledge in these domains is usually available as
unstructured plain text in form of natural language.
Here the vocabularies can be used in a semantically
guided information extraction process to automatically
create structured content. This is known as Ontologybased
information extraction (OBIE) where the
ontology is the vocabulary. A crucial problem of these
domains is their shallow linguistic, terminological and
semantic (STL) modeling which makes it difficult to
match them to natural language text objects.
2. Classification with STL Lexicon Objects
To carry out the OBIE task in STL fashion we propose
a two way approach. In a first step we generate an STL
ontology-lexicon, in the Semantic Web compliant
lemon 1 (Lexicon Model for Ontologies) format, from
the original domain vocabulary as described in [1], [2]
using an extended version of the Lemon Generator 2 .
This provides us with a set of rich lexicon objects that
represent the S, T and L context of the vocabulary
terms. In a second step we then use its STL features to
train a classifier for sentence classification [3].
3. Financial Reporting Domain
In this work we focus on vocabularies in the accounting
domain, i.e. GAAP (General Accepted Accounting
Principles) taxonomies that are used to create and
interpret financial reports in the machine processable
XBRL (eXtended Business Reporting Language)
format. In order to apply STL enrichment we first
convert these vocabularies in the Semantic Web RDF
ontology format. In particular we are considering the
two vocabularies:
vocabulary Concepts
IFRS 3 (International Finance Reporting
Standard)
2768
xEBR (European Business Registers) 147
A typical IFRS accounting term is:
1 http://www.monnet-project.eu/lemon
2 http://monnetproject.deri.ie/Lemon-Editor
3 http://www.ifrs.org
132
Decrease Through Classified As Held For Sale
Land And Buildings
Applying the STL enrichment adds additional semantic,
terminological and linguistic structure in form of
annotations to the example term:
level annotation
S1: taxonomic S11 ifrs:LandAndBuildings
context S12 is-a dbpedia:Asset
T1: sub-term T11 ifrs:LandAndBuildings
T12 term variation = properties
L1 tokenization & L11 decrease/Preposition
stemming L12 classified/Verb_pastTense
L13 as/Preposition
L14 held/Verb_present
…
L2: subcat frame L2 “hold for sale”/Verb_Phrase
Given such an STL enriched representation of the
vocabulary term we could then select features to
classify the following sentence:
This [decrease]_L11 was due to lower gains realized
on the sale of foreclosed [assets]_S12 [held for sale]_L2.
4. Future work
For the future we plan to evaluate this approach on a
corpus of financial reports of wind energy companies
(UNLP Wind Energy Corpus) which contains concepts
from IFRS and xEBR vocabularies. We also plan to
extend our approach along the cross-lingual dimension
towards Cross-lingual Ontology-based Information
Extraction (CLOBIE) facilitating multilingual
vocabularies.
References
[1] Wunner, T., Buitelaar, P., O’Riain, S., Semantic,
Terminological and Linguistic Interpretation of XBRL. In
Proceedings of the Workshop on Reuse and Adaptation of
Ontologies and Terminologies at the 17th International
Conference on Knowledge Engineering and Knowledge
Management (EKAW), Lisbon
[2] McCrae, J., Spohr, D. and Cimiano, P. (2011). Linking
Lexical Resources and Ontologies on the Semantic Web with
lemon. In Proceedings of the 2011 Extended Semantic Web
Conference.
[3] Khoo, A., Marom, Y., Albrecht, D., “Experiments with
Sentence Classification”, In Proceedings of the 2006
Australasian Language Technology Workshop, pages 18-25