PhiloNet: creating semantic concordances for the analysis of philosophical texts

PhiloNet: creating semantic concordances for the
analysis of philosophical texts

Luisa
Bentivogli

ITC-irst, Trento
bentivo@irst.itc.it

Fabio
Pianesi

ITC-irst, Trento
pianesi@irst.itc.it

Emanuele
Pianta

ITC-irst, Trento
pianta@irst.itc.it

2001

New York University

New York, NY

editor

encoder

Sara
A.
Schmidt

Philosophical text-analysis
semantic concordances
WordNet

1. Introduction
The PhiloNet project is a joint effort between ITC-irst and the Department of
Philosophy of the University of Bologna that aims at providing students and
scholars of philosophy with new computing tools. In particular, we are
developing tools for computer-aided analysis of digitized (philosophical)
texts capable of overcoming the limits of traditional methods, by offering
greater flexibility, analytical power, and adaptation to the goals of
different users.
In the field of text analysis a number of already available software tools
can produce very accurate lists of words, word concordances, word
frequencies, collocations and so on (Biber et al., 1998). These
computational techniques are all word-based, and they hardly provide support
for more sophisticated analyses, like the targeting of concepts. It is
conceivable, however, that exactly the latter kind of analytical tools are
more suitable for a domain, such as philosophy, where the need for
accessing, elucidating, comparing, etc. concepts across and within texts is
an important part of the work of scholars. Thus, new computational
concept-oriented resources are needed. More precisely, resources must be
available that list concepts, enumerate their mutual relationships, and
point at their realization at the word level.
In this work we report on preliminary results obtained by developing
techniques to create concept-based concordances rather than word-based ones,
by using generic and domain-specific (philosophical) wordnets as tools.

2. Semantic concordances for text-analysis
We use the term "semantic concordance" to refer to the set of text passages
in which a concept occurs. Concepts are expressed in texts through words and
therefore, to build semantic concordances, two lexical semantic issues must
be dealt with: polysemy (when a given word expresses different concepts) and
synonymy (when different words express the same concept). If one searches
for a concept on the base of a word "w" expressing that concept, polysemy
affects precision (the proportion of retrieved words that are actually
correct) because along with occurrences of "w" expressing the desired
concept, those expressing the other concepts conveyed by "w" will also be
retrieved. On the other hand, synonymy affects recall (the proportion of
correct words actually retrieved in answer to a search request) because all
the synonyms of "w", hence other possible occurrences of the target concept,
are missed. Thus, to move from a word-based to a concept-based text-analysis
it is necessary to disambiguate each occurrence of a word in a text, and to
have a list of the words that can express the same concept.
To this purpose, WordNet (Miller,1990) turns out to be a very useful tool.
WordNet is a lexical database, created at Princeton University, in which
nouns, verbs, adjectives and adverbs are organized into sets of synonyms
(synsets), representing lexical concepts. Synsets are linked by means of
various relations, including hypo/hypernymy, meronymy, and antonymy. As far
as semantic concordances are concerned, WordNet synsets can be used to
extend the search for a word to all its synonyms. Moreover, the relations
between synsets can be exploited to search related concepts, something which
is absolutely not available with normal word-based techniques. For instance,
we can search for a concept and its hyponyms or hypernyms. WordNet can also
be useful for addressing the polysemy problem, as it is currently used as a
tool for word sense disambiguation (WSD), both automatic and manual. In
manual WSD, WordNet plays the role of extensive sense inventory; a
well-known effort of this kind is the SemCor project (Miller,1993) in which
a subset of the Brown Corpus was tagged with WordNet senses for educational
use.
Our proposal consists in manually annotating philosophical texts with synsets
selected from an extension of the generic WordNet called PhiloNet (which is
specialized for the philosophical domain, see below), and having a user
interface that given a synset/concept permits the accessing of all the
passages in which the concept is used.

3. An example of semantic concordances for philosophical texts
In order to experiment with WordNet as a tool for creating semantic
concordances, we analyzed the different philosophical concepts expressed by
the word "reason" in Pearson's work "The Grammar of Science". The text was
digitized by the University of Bologna following the TEI guidelines and
using XML-based encoding schemes. The passages of the text relevant for our
example were then manually semantically tagged by using the synsets of the
specialized philosophical wordnet called PhiloNet.
Three different types of concordances have been tested on Pearson's work:
traditional word-based concordances, semantic concordances based on the
generic Princeton WordNet, and semantic concordances based on the
specialized PhiloNet.
A simple word-based concordance produces 125 occurrences of the word
"reason(s)", including verbs and nouns relating to different senses such as:
about these conceptions we reason, endeavouring to ascertain their
ask whether we have any reason for conceiving atoms as
human powers of perception and reason, lies the mystery and the
universe to be guided by reason. But reason, because it is a directive
power ......
As can be seen, with a word-based concordance it is the scholar interested in
the study of the philosophical concepts of "reason" who has to single out,
among the 125 occurrences found, the ones that are relevant to her purpose.
We expect semantic concordances based on WordNet to deliver more focused
information. In WordNet, the word "reason" occurs in six synsets, but only
the one reproduced here is relevant for our philosophical purposes:
Sense 3 reason, understanding, intellect -- (the capacity for rational
thought or inference or discrimination)
hypernym => faculty, mental faculty, module
This synset can be considered a philosophical concept, as it refers to the
British empiricist tradition starting with Locke, in which reason is broadly
viewed as the generic power or faculty of knowing.
Having Pearson's text tagged with WordNet synsets and by using the
philosophical synset above as a search key to find occurrences of the
relevant philosophical concept of reason, we obtain more focused results
compared to the ones obtained with the word-based concordance. In fact, 8
occurrences of the verb "to reason" and 40 occurrences of the noun "reason",
which were retrieved in the word-based concordance seen above, are now
dropped because expressing non-philosophical senses. The synset-based search
produces 22 occurrences of the philosophical concept as delivered by the
word "reason", along with previously undetected occurrences due to synonyms
of "reason": 2 occurrences due to the word "understanding", and 4 due to
"intellect", as for instance in:
the only proof that our intellect has been keen enough to
in his Essay concerning Human Understanding, where he writes
Furthermore, we can exploit the relations between synsets available from
WordNet to obtain information about concepts semantically related to the one
we are interested in. In our example we find 27 occurrences of the hypernym
of "reason, understanding, intellect", that is "faculty, mental faculty, module":
the power of the reasoning faculty possesses of resuming
product of the reflective faculty, analyzing the process of
perception, .......
It seems to us that even this simple experiment demonstrates the advantages
and power of the semantic concordance methodology based on the current,
generic WordNet. However Princeton WordNet, aiming at capturing common
linguistic knowledge, contains only one philosophically relevant concept for
"reason". Therefore, in a WordNet-based concordance, the other occurrences
of the word "reason" in different though philosophically relevant senses are
missed.
The results of the concept-based concordance of "reason" can be further
refined by having access to a specialized philosophical wordnet. To this
purpose we used PhiloNet, a wordnet specialized in the philosophical domain,
currently under development in conjunction with the University of Bologna.
PhiloNet is a component of MultiWordNet (Bentivogli et al., 2000), an
aligned multilingual lexical database built at ITC-irst on the basis of
Princeton WordNet. PhiloNet is developed in two stages. At the first stage,
philosophical synsets already existing in WordNet are identified, labeled as
philosophical, and imported into PhiloNet. At the second stage, new
philosophical synsets are added to PhiloNet and linked to the generic
WordNet. The criterion followed for the identification and for the creation
of philosophical synsets is to represent only current philosophical concepts
as found in philosophical handbooks. Moreover, PhiloNet allows context
labels to be attached to synsets, words, or relations to represent special
pieces of information that would be hardly captured by means of the usual
lexical and semantic relationships. For instance, if a philosopher or
philosophical school uses a idiosyncratic term to refer to a concept that
corresponds to a common philosophical concept, a context label can be added
to that word in the synset specifying the philosopher/s for which the
synonymy holds.
Coming back to our example, PhiloNet contains the synset "reason,
understanding, intellect" which has been imported from the generic WordNet
and other seven philosophical synsets:
(1) "reason", a generic synset in which the concept of reason is
broadly represented as the most distinctive characteristic of human
beings guiding them.
(2) "reason, dianoia"
(3) "reason, evidence, good sense"
(4) "reason, self-consciousness"
(5) "reason, calculus, computation"
(6) "reason, foundation, essence"
(7) "reason, law, natural law, law of nature".

We can now perform more sophisticated analyses with respect to those possible
on the basis of the generic WordNet. As a result, we can see that in
Pearson's work synset (1) occurs 18 times, (6) occurs 28 times, (7) occurs
51 times (8 through the word "reason", 14 through "law", 22 through "natural
law", 7 through "law of nature") while the other concepts yield no
occurrences.
Summing up the results of our experiments with the concept of "reason" in
Pearson's work "The Grammar of Science", we can see that the three types of
concordance provide different levels of precision and recall of the
information retrieved:
-Word concordance: 125 lines all due to the word "reason" and not
distinguishing between relevant (77) and non relevant (48)
occurrences. Precision is 61.6% and recall is 61.1%.
-WordNet-based semantic concordance: 28 lines all relevant and
referring to one philosophical concept (22 due to "reason", 2 to
"understand", and 4 to "intellect"). Precision is 100% and recall is
22.2%.
-PhiloNet-based semantic concordance: 126 lines all relevant and
distinguishing four different philosophical concepts. In these 126
lines, 77 are due to the word "reason", 2 to "understanding", 4 to
"intellect", 14 to "law", 22 to "natural law", and 7 to "law of
nature". Both precision and recall amount to 100%.

4. Conclusion and prospects
In this paper we have introduced and discussed two levels of concept-based
textual analysis: a "linguistic level", relying on a generic WordNet, and a
"domain level", requiring a philosophical WordNet.
These two levels should be carefully identified and delimited. In particular,
it is important that the domain-based wordnet contains only concepts that
are widely accepted in the field. Respecting these guidelines will guarantee
that the tagging performed on a philosophical text can be used by students
and scholars of different theoretical background.
At still another level, very specialized wordnets relative to single books
make sense, and can be used in the proposed methodology. However, in this
case the theoretical background and aims of the scholar building the wordnet
and tagging the text become crucial. In a way, the resulting tagged text
should be seen more akin to an essay than to a reference work. On the other
hand, pushing the metaphor a little further, linguistic and domain-based
text analysis can be compared with general dictionaries and philosophy
handbooks, respectively.
In conclusion, the preliminary results of our research support the
feasibility and importance of a concept-based text analysis.

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