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WordNet Applications

23-06-2015 | 18:37
This paper describes WordNet design and development, discussing its
origins, the objectives it initially intended to reach and the subsequent use to which
it has been put, the factor that has determined its structure and success. The emphasis
in this description of the product is on its main applications, given the instrumental
nature of WordNet, and on the improvements and upgrades of the tool itself, along
with its use in natural language processing systems. The purpose of the paper is to
identify the most significant recent trends with respect to this product, to provide a full
and useful overview of WordNet for researchers working in the field of information
retrieval. The existing literature is reviewed and present applications are classified to
concur with the areas discussed at the First International WordNet Congress.

1 Introduction

WordNet, one of a series of manually compiled electronic dictionaries, is restricted to no
specific domain and covers most English nouns, adjectives, verbs and adverbs. Although
there are similar products, such as Roget’s International Thesaurus, or CYC, Cycorp: Makers
of the Cyc Knowledge Server for artificial intelligence-based Common Sense. CYC contains
100,000 concepts and thousands of relations. Each concept is assigned to certain terms related
by prepositional logic. The present paper analyses the reasons for WordNet’s success and, in
particular, the main applications of the tool over the last ten years.


2 Wordnet Development

The origin of this tool is to build a lexical-conceptual model and database, consisting of
both lexical units and the relations between such units, structured into a relational semantic


Originally intending to create a product that could combine the advantages of electronic
dictionaries and on-line thesauri, an expert team of linguists and psycholinguists headed by
G. A. Miller began research at Princeton University’s Cognitive Science Laboratory in 1985
that would culminate in the appearance of WordNet in 1993.


WordNet offers researchers, many of which were not initially envisaged by the authors,
along with its cost-free use and well-documented open code. The result has been the
appearance of applications in different fields of research, making it an ideal tool for
disambiguation of meaning, semantic tagging and information retrieval. Therefore, although
four members manage, maintain and develop WordNet many other teams collaborate in
driving implementation of the product, as attested by two facts:


1. The speedy pace of release of new versions of WordNet.

2. Organised world-wide promotion of WordNet, through the creation of the Global


WordNet Association, which, in conjunction with CIIL Mysore, IIT Bombay and
IIIT Hyderabad, held the 1st International WordNet Conference in 2002. Primarily
technical, the conference was structured under six areas of interest: Linguistics, WordNet
architecture, WordNet as a lexical resource and component of NLP, Tools and Methods
for WordNet Developme, nt., Standardisation., Applications (information extraction and
retrieval, document structuring and categorisation, language teaching).


These six topics are still present in the 2nd International Conference of the Global WordNet
Association (GWC 2004) held at Masaryk University, Brno.


3 Applications

The success of WordNet, as mentioned, is largely due to its accessibility, quality and potential
in terms of NLP. Figure 1 below shows the results of a search run on the bibliographic
database LISA, INSPEC, IEEE and ResearchIndex and on the Universidad Carlos III’s
OPAC. The documents were publised from 1994 till 2003. This search, while not necessarily
exhaustive in respect of WordNet research, does nonetheless show how such research effort
is distributed. It will be observed that the major use of this tool has been in the area of
conceptual disambiguation.


3.1 Improvements in WordNet

The data record of publications dealing with WordNet shows that there has been a tendency
to improve the product in a number of respects. The objective is to make WordNet much more
effective and relevant than any existing on-line dictionary by incorporating greater semantic
wealth and taking a more contextual approach. Several possibilities have been explored to
achieve this:


Studies geared to improving software There is a clear prevalence, in terms of volume, of
papers geared to expanding and enriching the WordNet structure. One of such endeavours
is VerbNet [1], designed to make up for shortcomings in the associative relations between
verbs; another is the Lingua::WordNet interface [2], which furnishes an editable presentation
of WordNet, with meronym categories never before implemented. Finally, substantial efforts
have been made to standardise, integrate and distribute the tool.


Multilingual WordNet One of the most relevant endeavours has been the development of
EuroWordNet, a project based on WordNet structure whose ultimate purpose is to develop
multilingual databases with wordnets for several European languages. Each wordnet adopts
an autonomous lexicalisation structure and all are interconnected through an interlinguistic
index, for which relations have been added and modified and new levels identified in
WordNet. For a multilingual description of EuroWordNet see [3,4].


This paper poses the possibility of automatically transferring a list of English verbs,
classified by their syntactic characteristics, to WordNet synsets.


3.2 Improvements in Natural Language Processing Systems

Such improvements are found in a substantially larger number of papers on WordNet,
regarded here to be a tool well suited to a series of applications such as discussed below:


Information retrieval and extraction These operations are closely related to organisation
and representation of knowledge on the Internet. One of the lines of research pursued is the
application of artificial intelligence to information retrieval, stressing the local components
and inferential process of human reasoning in the design of automatic information retrieval
systems. The method for incorporating logic and inference focused on WordNet shortly after
it appeared [5]. WordNet has been used as a comprehensive semantic lexicon in a module for
full text message retrieval in a communication aid, in which queries are expanded through
keyword design [6]. WordNet has, then, started to be used as a linguistic knowledge tool
to represent and interpret the meaning of, and provide the user with efficient and integrated
access to, information; integration, indeed, has become an increasingly necessary feature
with the development of multiple database access systems and one in which WordNet’s
identification and interpretation of semantic equivalents is extraordinarily useful [7].


Mandala [8] proposed the use of WordNet as a tool for the automatic construction of
thesauri, based either on co-occurrence determined by automatic statistical identification of
semantic relations, or on the predicate-argument association, in which the most significant
words of an environment (predicate) and those with which they relate are identified to
construct the argument. In another vein, Moldovan [9] opted to use WordNet in the
development of a natural language interface to optimise the precision of Internet search
engines by expanding queries.


Concept identification in natural language This operation is designed to detect the terms
requested, not only for extraction, but to suit them to the full semantic richness and
complexity of a given information need. WordNet applications have followed a dual course
in such applications:


1. Disambiguation i.e., precision and relevance in response to a query via resolution of

semantic inconsistencies. Moldovan [9] described schematically the semantic disambiguation
as follows:


(1) All the noun–verb pairs in the sentence are selected.


(2) The most likely meaning of the term is chosen (subprocess that Moldovan calls
terminological disambiguation). Internet is used with this goal.


(3) Drawing from the most frequently appearing concepts (step 2), all the nouns are
selected in the “glossaries” of each verb and its hierarchical subordinates.


(4) Drawing from the most frequently appearing concepts, all the nouns are selected in
the “glossaries” of each noun and its hierarchical subordinates.


(5) A formula is applied to calculate the concepts common to thenouns in points 3

and 4.


Disambiguation is unquestionably the most abundant and varied WordNet application.
Indeed, there is a wide range of possibilities.


WordNet has served as a support for the development of tools to enhance the efficiency of
Internet resource searches. One example is the IWA/H project for building an ontological
framework able to disambiguate search criteria via mixed knowledge representation
technique systems (ARPA KRSL); others include tools such as Oingo and SimpliFind,
two Internet products that avoid ambiguity in natural language searches by using the
WordNet lexicon, duly expanded by creating millions of word associations to refine the
search process.


The use of WordNet for improving search engines is interesting the IWA/H project was
based on the MORE technique developed by the RBSE project for more efficient retrieval
of Internet resources, as discussed by Eichmann [10].


WordNet has, naturally, been used for disambiguation in traditional models to enhance
information retrieval efficiency: for the development of a classifier, implemented with
WordNet, able to combine a neurone-like network to process subject contexts and
a network to process local context; for the exploitation of a Bayesian network able
to establish lexical relations with WordNet as a source of knowledge, integrating
symbolic and statistical information [11]; for the development of a statistical classifier,
implemented with WordNet lexical relations, able to identify the meaning of words,
combining the context with local signs [12]; and as support for the development of a
computational similarity model to add on-line semantic representation to the statistical
corpus. WordNet has, therefore, proved its worth as an ideal methodological element to
disambiguate the meaning of words in information extraction systems [13]. As a result,
projects have been launched to disambiguate nouns in English language texts using
specification marks deriving from WordNet taxonomies as a knowledge base, as well
as to reduce polysemy in verbs, classified by their meanings via predicate associations,
with a view to optimising information retrieval. Methods for nouns [14] and verbs [1,4]
has already been analysed.


At the same time, new disambiguation models have been tested in conjunction with
WordNet by: generating ontological databases with a systematic classification of
multiple meanings derived from WordNet [15]; or generating broad corpora to signify
words on the grounds of WordNet synonymies or definitions in the wording of
queries [16]. One result has been the appearance of GINGER II, an extensive dictionary
semantically tagged using 45 WordNet categories and an algorithm for interpreting semantic text by determining verb senses, identifying thematic roles and joining prepositional phrases [17]. More recently R. Mihalcea and D. Moldovan presented AutoASC, which automatically generates sense tagged corpora that prove to be very effective for disambiguation in information retrieval; this product incorporates the latest WordNet gloss definitions [18].

2. Semantic distance Three concepts recur in WordNet literature that entail a certain
amount of ambiguity: terminological distance, semantic distance and conceptual distance.
The terms semantic distance and conceptual distance are found to be used in several
studies to pursue the same objective and deploy the same methodology for resolving
the issue at hand. Terminological distance, by contrast, often appears to refer to the suitability
of the word selected to express a given concept.


Semantic distance is understood to mean the contextual factor of precision in meaning.
In his particularly relevant papers, Resnik [19] computes class similarity, defining class
to be the nouns of a synset plus the nouns in all the subordinate synsets. Although
the concept of semantic similarity between classes was proposed by Resnick [19].


WordNet was quickly enlisted to build and operate with FEDDICT, a prototype on-line
dictionary to develop an information retrieval technique based on the measurement of
the conceptual distance between words, in which WordNet semantic relations proved
to be highly useful [20]. A very interesting sequel to this endeavour was provided by
McTavish [21] who used WordNet semantic domains to establish categories that could
be used to analyse conceptual semantic distances in terms of social environments to
better organise terms for retrieval.


Computational linguistics is, however, the area that has placed the greatest emphasis
on relations and semantic distances between lexemes, the measures of which were
classified by A. Budanitsky [22]. This paper highlights the measures that use WordNet
as a resource and for implementation of functions, in particular: Hist-St. And Leacock–
Chodorow, in which similarity, albeit in the IS-A link only, rests on the shortest path
between two synsets; and Resnik, Jiang, Conrath and Lin, for all of whom information
content is a determining factor of similarity in their measures of distance.


Query expansion In 1994 Smeaton [23] proposed an expansion system based on calculating
the tf-idf for the query terms and adding to it half the tf-idf for the WordNet synonyms for
these terms. Gonzalo [24] later reported the benefits of applying WordNet to queries, using
it as a WSD (Word Sense Disambiguator) able to enhance the search process by including
semantically related terms and thus retrieve texts in which the query terms do not specifically


Document structuring and categorisation Intellective efforts and operations in this area
are geared to a new organisation and representation of knowledge. In this case the focus is
on the aspects of the tool suited to document categorisation: extraction of semantic traits by
grammatical categorisation of WordNet nouns, verbs and adjectives [25]; and categorisation
of the relevance of the data in INFOS by predicting user interest on the basis of a hybrid
model using keywords and WordNet conceptual representation of knowledge [26].


Further research along these lines came in the form of a computational method presented
by S. M. Harabagiu [27] for recognising cohesive and coherent structures in texts, drawing on WordNet lexical-semantic information, whose objective is to build designs for the association between sentences and coherence relations as well as to find lexical characteristics in coherence categories. WordNet became an ancillary tool for semantic ontology design geared to high quality information extraction from the web, and has prompted new endeavours such

as the WebOntEx (Web Ontology Extraction) prototype developed by Keng Woei Tan [28]
which is designed to create ontologies for the semantic description of data in the web.


Judith Klavans [29] devised an algorithm for automatically determining the genre of a
paper on the grounds of the WordNet verb categories used. With their WN-Verber, they
determined that some verbal synsets and their highest subordinates are less frequent in certain
document typologies.


Audio and video retrieval This is a challenge in need of increasingly urgent attention
in view of the burgeoning development of hypermedia and non-text information. The
MultiMediaMiner [30], is a prototype to extract multimedia information and knowledge from
the web that uses WordNet to generate conceptual hierarchies for interactive information
retrieval and build multi-dimensional cubes for multi-media data. Finally, WordNet has
been used in query expansion to index radio news programme transcriptions effected by a
prototype designed to retrieve information from radio broadcasts [31].


Other WordNet applications

Parameterisable information systems While anecdotal, the J. Chai [32] proposal to create an
information system (called Meaning Extraction System) that can be configured in terms of a
specific user profile is appealing. The user chooses from a series of texts (training collection)
the ones that appear to be of greatest interest. WordNet identifies the hierarchical (IS-A)
synsets related to the terminology of the documents selected. This process generates rules
that enable the system to identify, a priori, the documents that the user will find to be of


Language teaching and translation applications As discussed in point 3.1.2, applications
have been devised and tested to improve the composition of texts drafted by non-native
English writers. However, yet another line of research has been addressed in international
conferences on WordNet, namely, foreign language teaching. One example is the article by
X. Hu and A. Graesser, which proposes using the WordNet vocabulary to evaluate pupils’
command of a given language [33].


As a translation aid based on the application of semantic distance algorithms, WordNet
has also been used to develop a potential error corrector for the positioning of words [34].


One very intuitive formula consists of using conceptual interlingua representation of texts
and queries such as used in the CINDOR system, which accommodates WordNet-supported
inter-linguistic combinations, obviating the need for an expert translation for retrieval. The
CINDOR system was presented and tested at TREC-7 and seems to be useful for cross- or
combined linguistic retrieval [35].


4 Trends

Trends are difficult to ascertain and evaluate in view of the clearly instrumental and
application-based dimension that underlies WordNet’s success. Nonetheless, a comparative
analysis of the most recent publications provides some insight into a number of trends in
WordNet use:


1. Development of interlinguistic indices for multilingual conceptual equivalence, without
translation. Subsidiarily, this endeavour has also been geared to perfecting integrated
access to information, driven by the rapid development of multiple database access


2. Use as an ideal tool to optimise the retrieval capacity of existing systems: natural
language interfaces for search engines; automatic generation of tools for semantic
disambiguation of concepts (corpora, dictionaries, directories, thesauri) and the creation
of knowledge summaries from expanded queries.


3. Support for the design of grammatical categorisations designed to classify information
by aspects and traits, but in particular to design and classify semantic ontologies that
organise web data – semantically, to be sure.


4. Basis for the development of audio-visual and multi-media information retrievalsystems.
5. In the last 3 years ontologies construction have been one of the most dynamic areas and
its applications to the semantic web [36].


5 Conclusions

Although WordNet applications are growing steadily and research may be expected to
increase in the coming years as new versions are released, the tool has certain shortcomings
that should be addressed in future studies.


Limitations and Problems are:


1. Although WordNet is an electronic resource, it was, after all, designed for manual
consultation and not for automatic processing of natural language texts; as a result, no
particular emphasis was placed on enabling the system to automatically differentiate
between the various concepts involved.


2. Another problem is its multidisciplinarity, which prompts flawed operation in many NLP
systems, due to which processing is usually conducted with sublanguages or specific


3. Classification was performed manually, which means that the reasons and depth of
classification may not be consistent.


4. While the synset simplification affords obvious advantages, in the longer term it leads to
shortcomings. These are particularly acute in semantic proximity calculations and may
create insuperable situations whenever the context of the discourse in which the relation
appears is not contained in the synset information.


5. The overabundance of nuance in the concepts calls, in nearly any NLP application, for
prior calculation of the frequency of the concept in a given domain. Such calculation
is one of the sources of system error, especially where WordNet glosses – extracted, as
noted above, from the Brown Corpus – are used, due to the uneven coverage afforded to
the different domains.

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