Structured and Unstructured: Extracting Information from Classics Scholarly Texts

1
Structured and
Unstructured: Extracting
Information from Classics
Scholarly Texts
Romanello, Matteo
matteo.romanello@kcl.ac.uk
King's College London
The poster presents an ongoing PhD research
project that applies Digital Humanities to
Classics. The project is focussed on the
extraction of information from modern
scholarly texts (i.e. “secondary sources”),
namely all the modern publications about
ancient works written in Greek or Latin (being
our so-called “primary sources”). The project
addresses both the problem of extracting
information fom scholarly texts in an automatic
and scalable way, and that of providing users
(i.e. scholars) with advanced and meaningful
entry points to information rather than just
search engine-like functionalities over an
electronic corpus of texts.
1. Background
A currently ongoing project such as the Million
Book Library drew considerable attention to the
characteristic features of the next generation of
digital libraries and to the consequences of a
change of scale on the practice and the results
of the research itself (Crane 2006). This is a big
chance for Humanities and Classics particularly
given the extended “shelf-life” of humanities
relative to scientific publication. However, once
those secondary sources are digitized this does
not mean that information contained within
them will be immediately accessible. Issues that
we need to address concern the accuracy of
our electronic resources, such as encoding of
Greek text, inaccurate OCR transcription due
to low image quality, problem of missing pages
(Boschetti 2009), as well as the scalability of
ways to provide access to information, given
that we cannot afford to correct manually every
scanned page.
2. Motivations
Secondary sources are without any doubt
intrinsically valuable for Classicists, as they
shape the scholarly discourse of the discipline.
Printed citations and references – and even
mentions of names or geographical places –
can be considered as being already a form of
hypertext as they virtually create links between
texts. However in the currently available digital
libraries – except for the Perseus Digital Library
1
– primary and secondary sources are scarcely
interconnected, despite the fact that one of the
main advantages of digital libraries is the way in
which they represent the hypertextual nature of
text collections.
In particular, Classics scholars are interested
in named entity mentions inside texts, as is
reflected by the widespread use of different
kinds of indexes and concordances in this
field that basically organize in a systematic
manner references to text passages when a
given entity is mentioned. Translating the
problem into computational terms, we are
faced with the task of automatic entity
extraction from a corpus of unstructured texts to
develop a discipline-specific system of semantic
information retrieval.
3. Related Work
In addition to all the unstructured information
being made available on the web, several
projects in the Humanities have produced
over the last decade an increasing amount of
structured information that was then stored in a
wide range of data formats (i.e. databases, XML
fles, etc.). The approach we undertake is to reuse
information contained within those structured
data sources as training data for a supervised
system that extracts semantic information fom
an unstructured corpus of texts. A likely
approach is suggested for instance by a research
recently conducted by IBM to investigate the
automatic creation of links between structured
data sources (i.e. database containing product
information) and unstructured texts (i.e. emails
of complaint about products) (Bhide et al.
2008). More generally, the problem implied
by our approach of determining when two bits
of information refer to the same entity has
been thoroughly explored in the AI (Artifcial
Intelligence) field (Li et al. 2005).

2
4. Method
The very first phase of the project is devoted to
the task of building our corpus of unstructured
texts. So far we considered two corpora of
texts: the papers contained in the open archive
Princeton/Stanford Working Papers in Classics
2
(Josiah Ober et al. 2007) and the articles
published by the journal Lexis
3
available online
under an open access policy. Although the texts
are already available in electronic format, some
pre-processing is needed – particularly for the
sequences of Greek text contained – before we
can start extracting information.
In the second phase we integrate different
structured data sources into a single knowledge
base. As far as this task is concerned, it
is possible to observe at least two main
categories of lack of interoperability. The first
category consists of cases where entities that
are similar from an ontological perspective
(e.g. a geographical place name) are encoded
using different data structures (i.e. the same
place name could be encoded using elements
belonging to diferent XML dialects). The
second category covers cases where chunks
of information common to more than one
collection are described with different degrees
of depth and precision. For instance, the
name “Alexandria” inside an inscription is just
marked up as a place name where instead
a collection of geographical data offers many
details for the city of Alexandria such as
coordinates, orthographical variants of the
name, denomination in diferent languages etc.
For this purpose we mean to apply high level
ontologies to aggregate information related to
the same entity but spread over diferent data
sources. Among the most suitable ontology
vocabularies are worth to be mentioned FOAF,
4
CIDOC CRM,
5
FRBRoo
6
and YAGO.
7
At a further stage we are taking into account how
to automatically extract information from the
corpus. We are mainly interested in extracting:
1) named entities; 2) bibliographic references;
3) canonical references, namely references to
ancient texts expressed in a concise form and
characterized by a logical reference scheme (e.g.
based on references to books or lines of a work
instead of page numbers).
The very first step in named entities processing
is the recognition and identification of named
entities within texts. Once identified the
named entities should be classified and then
disambiguated on the basis of the context.
This task will be accomplished through the
comparison of semantic spaces using methods
and algorithms developed in the field of
Latent Semantic Analysis (LSA) (Rubenstein
& Goodenough 1965; Sahlgren 2006). In
particular the semantic spaces of all the contexts
where a given named entity appear will be
compared with each other in order to determine
which resources are really referring to the same
entity.
For the information extraction task we use
mainly tools based on machine learning
methods, such as Conditional Random Fields
(CRF) or Support Vector Machine (SVM). Some
of them are already available as open source
sofware and just need to be trained to work
with our data, while others are being specifcally
developed such as a Canonical Reference
Extractor (Romanello et al. 2009). Information
contained in the knowledge base is meant to
be used as training material for those sofware
components.
5. Further Work
This ongoing project is expected to prove a
scalable approach to extract entity references
from unstructured corpora of texts, such as
OCRed materials. In addition to this, it will
prove how to reuse several data sources of
structured information to train text mining
components and it will show to what extent
those data sources can be made interoperable
with each other. Finally we plan to evaluate with
some experts how effective such an information
retrieval system is in helping Classicists with
their research.
Acknowledgement
This research project is supported by an AHRC
(Arts and Humanities Research Council) award.
References
Bhide, M. et al.
(2008). 'Enhanced Business
Intelligence using EROCS'.
Data Engineering,
2008. ICDE 2008. IEEE 24th International

3
Conference on Data Engineering, 2008.
Pp.
1616-1619.
http://ieeexplore.ieee.org/iel5/4
492792/4497384/04497635.pdf?tp=&arnumber=4
497635&isnumber=4497384
.
Boschetti, F.
(2009) (2009). 'Improving
OCR Accuracy for Classical Critical Editions'.
Proceedings of the 13th European Conference
on Research and Advanced Technology for
Digital Libraries (ECDL 2009).
Springer.
Crane, G.
(2006). 'What Do You Do
with a Million Books?'.
D-Lib Magazine.
12(3)
.
http://www.dlib.org/dlib/march06/cra
ne/03crane.html
(accessed March 19, 2009).
Josiah Ober et al.
(2007). 'Toward Open
Access in Ancient Studies: The Princeton-
Stanford Working Papers in Classics'.
http://ww
w.atypon- link.com/ASCS/doi/abs/10.2972/hes
p.76.1.229
(accessed July 15, 2009).
Li, X., Morie, P., Roth, D.
(2005). 'Semantic
integration in text: from ambiguous names to
identifiable entities'.
AI Mag.
26(1)
: 45-58.
htt
p://portal.acm.org/citation.cfm? id=1090494
(accessed March 12, 2010).
Romanello, M., Boschetti, F., Crane, G.
(2009). 'Citations in the digital library of
classics: extracting canonical references by
using conditional random fields'.
NLPIR4DL
'09: Proceedings of the 2009 Workshop on
Text and Citation Analysis for Scholarly Digital
Libraries.
Morristown, NJ, USAAssociation for
Computational Linguistics, pp. 80–87.
Notes
1.
http://www.perseus.tufts.edu/hopper/
2.
http://www.princeton.edu/~pswpc/
3.
http://lexisonline.eu/
4.
http://www.foaf-project.org/
5.
http://cidoc.ics.forth.gr/
6.
http://cidoc.ics.forth.gr/frbr_inro.html
7.
http://www.mpi-inf.mpg.de/yago-naga/yago/