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1. Introduction
A Corpus Framework For Cross-Lingual Search
Nitish Aggarwal, Tobias Wunner, Paul Buitelaar
Unit for Natural Language Processing,
Digital Enterprise Research Institute, National University of Ireland, Galway
Email: firstname.lastname@deri.org
Cross-lingual queries on text documents based on
specialized domain vocabularies are complex and
dependent on the semantic, terminological and
linguistic (STL) features of the vocabulary and
language. The challenge for a cross-lingual search is to
retrieve corpus objects which best match the user query
based on these features. Therefore both the corpus and
the query need to be STL enriched [1].
2. Method
In this work we present a framework to carry out a STL
enrichment process for document, sentence and token
corpus objects. The implementation is based on the
blackboard architecture pattern with the corpus as a
blackboard and S, T and L annotators acting on the
corpus to perform the STL enrichment process (fig. 1).
Figure. 1 STL corpus framework
We implemented L and T annotators for NLP
processing such as tokenization or part-of speech
tagging for English, German, Spanish and Dutch
processing and a S annotator enriching the corpus
objects with vocabulary annotation. On the query side
we manually implemented a set of queries with
different STL features as shown in table 1.
type Examples
S query val=IFRS_FinancialAssets, lang=DE
T query val= activos financieros, lang=ES
L query part-of speech=Verb
L query lemma=finance, part-of-speech=Noun
Table. 1 implemented STL queries
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3. Data-Set
We have constructed a multi-lingual finance data set
consisting of financial reports from Wind Energy
companies (UNLP Wind Energy Corpus) and
vocabularies for English, German, and Spanish. The
corpus comprises 96 financial reports and 1421 news
texts from 9 different wind energy companies. We also
used two financial vocabularies with STL enriched
terms. The first vocabulary is the International Finance
Reporting Standard (IFRS), which used worldwide to
create financial reports in XBRL (eXtended Business
Reporting Language) format. The second vocabulary is
developed by the xEBR (XBRL European Business
Registers) group to describe legal enterprise entities
within Europe.
Terms examples
IFRS 2487 Financial assets
Amortization Computer Software
xEBR 147 Financial fixed assets
Company address, Country
Table. 2 Financial vocabularies
4. Future work
For the future we plan to develop a broader set of
annotators and evaluate our approach on the UNLP
Wind Energy corpus with queries constructed from the
IFRS and the xEBR vocabulary. In particular we want
to explore different combinations of S, T and L features
of the queries using the framework.
We also plan to extend the framework STL enrichment
on the vocabulary side based on the lemon (lexicon
model for ontologies) Generator 1 , as developed by the
MONNET project 2 to facilitate richer STL searches.
5. 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] Cimiano et al. (2010). D2.1 Ontology-Lexicon
Model. Monnet Project Deliverable.
1 http://monnetproject.deri.ie/Lemon-Editor
2 http://www.monnet-project.eu/