A LEXICON BASED APPROACH
TOWARDS CONCEPT EXTRACTION
Anoud Shaikh
Mehran University of Engineering and Technology. Sindh (Pakistan)
E–mail: anoudmajid85@gmail.com
Naeem Ahmed Mahoto
Mehran University of Engineering and Technology. Sindh (Pakistan)
E–mail: naeem.mahoto@faculty.muet.edu.pk
Mukhtiar Ali Unar
Mehran University of Engineering and Technology. Sindh (Pakistan)
E–mail: mukhtiar.unar@faculty.muet.edu.pk
Recepción: 05/03/2019 Aceptación: 19/03/2019 Publicación: 17/05/2019
Citación sugerida:
Shaikh, A., Mahoto, N. A. y Unar, M. A. (2019). A Lexicon based Approach Towards
Concept Extraction. 3C Tecnología. Glosas de innovación aplicadas a la pyme. Edición Especial, Mayo
2019, pp. 50–67. doi: http://dx.doi.org/10.17993/3ctecno.2019.specialissue2.50–67
Suggested citation:
Shaikh, A., Mahoto, N. A. & Unar, M. A. (2019). A Lexicon based Approach Towards
Concept Extraction. 3C Tecnología. Glosas de innovación aplicadas a la pyme. Special Issue, May
2019, pp. 50–67. doi: http://dx.doi.org/10.17993/3ctecno.2019.specialissue2.50–67
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254–4143
52
ABSTRACT
The emergence of digital media has tremendously increased the amount of
unstructured data. Recently 80% of data, generated over the web, is in an
unstructured format. This immense amount of data is a great source for the
knowledge discovery and thus, may be utilized for extracting purposeful
information. This study adopted a lexicon–based approach for automatic
concept extraction from online news stories and events. An application prototype
has been developed to demonstrate the applicability and eectiveness of the
adopted approach. The extracted knowledge about news stories, articles and
blogs are essential in understanding in–depth information for news analysts. This
knowledge plays a vital role in building societies since media is considered as an
opinion maker for its audience.
KEYWORDS
Online news, Unstructured data, Concept extraction.
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1. INTRODUCTION
The digital age has provided an immense amount of data in terms of news
articles, social media data, and web LaValle, Lesser, Shockley, Hopkins &
Kruschwitz, 2014; Gharehchopogh & Khalifelu, 2011). Every day, a large amount
of data is published on the news websites, micro–blogging websites and other
information repositories (Lei, Rao, Li, Quan & Wenyin, 2014). The published
news articles reveal the events happening around the world (Lei, et al., 2014).
The challenging issue, specically, in the textual data format (i.e., news articles)
is to extract purposeful information. Manually, it is a hard task to interpret a
large collection of data (Lee, Park, Kim & No, 2013). Besides, the information
hidden in unstructured data format inherently makes it dicult processing tasks,
because it deals with natural language processing. Therefore, in the current
era of information ow, media analysts and other researchers need an easily
understandable and high–level summary of information. For instance, a media
analyst may require searching news regarding a certain topic, events happening
to a certain geo–location, and/or news events based on a timeline. These and
other such queries are objectives, which requires an ecient method to answer
such queries.
Text Analytics allows knowledge discovery and purposeful nding of information
from such a massive amount of data for investigation. The extracted knowledge can
be used for better decision–making strategies and eective resource management.
Therefore, extracting purposeful knowledge from large data having natural
language involvement is an open challenge, which acquires sophisticated methods
and algorithms to deal with it. To this aim, this research study extracts concepts
from a large number of news stories and articles. The concept extraction refers
to a meaningful sequence of words that are used to represent objects, events,
activities, entities (real or imaginary), topics or ideas, which are of interest to the
users (Parameswaran, Garcia–Molina & Rajaraman, 2010; Szwed, 2015). The
concept extraction technique is a very eective way of extracting all the possible
useful and meaningful concepts from text documents. The extracted concepts,
later, may be tagged as essential concepts and may be represented in an ecient
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mechanism (Zhang, Mukherjee & Soetarman, 2013). The concepts, especially,
present the understanding of the unstructured data format. The coverage and
patterns of such concepts help in understanding in–depth about the news stories,
news articles and inclination of the author’s mindset. This knowledge about
news stories, articles and blogs are essential for news analysts and plays a vital
role in building societies, because media plays the role of opinion maker for the
inhabitants of society.
An application prototype has been developed in this study to demonstrate the
automated concept extraction that works based on lexicon approach. On the
contrary, the machine–learning approach (i.e., supervised learning) inherently
possesses challenges due to unstructured data format. Whilst, the lexicon–based
approach has produced comparatively better results. The developed prototype
presents the applicability and eectiveness of the considered approach.
This paper is structured as follows: section 2 reports existing scientic literature
about concept extraction, section 3 describes the architecture of the developed
application. Results and discussion are reported in section 4, and nally, section
5 concludes.
2. RELATED WORK
The concept extraction has been remained focus of in the recent existing
literature (Sˇili ́c, et al., 2012; Parameswaran, et al., 2010; Villalon, et al., 2009,
Weichselbraun, et al., 2013; Termehchy, et al., 2014; Brin, 1998, Mahmood, et al.,
2018). Specially, concept extraction in the context of online news has become ta
opic of interest. For instance, social emotions have been detected using a lexicon–
based approach from news articles in (Lei, et al., 2014). CatViz Temporally –
Sliced Correspondence Analysis Visualization performs exploratory text analysis
on large collection of textual data. The basis of CatViz is Correspondence
Analysis (CA) and allows va isual analysis of dierent aspects of text data (Sˇili ́c,
et al., 2012).
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Extraction of concepts from query log data repository has been carried out in
Parameswaran, Garcia–Molina and Rajaraman (2010), where sub–concepts
and super concepts are pruned. The core concepts are taken into consideration,
which is oriented on frequency, better meaning and idea. Similarly, automatic
concept extraction from essays written by students in order to draw concept maps
is reported in Villalon and Calvo (2009) for the concept map mining purpose.
The limitations faced in machine–learning approach during training model have
been addressed in Weichselbraun, Gindl and Scharl (2013). Two potentially
ecient algorithms have been proposed Termehchy, Vakilian, Chodpathumwan
and Winslett (2014), namely: 1) Approximate Popularity Maximization (APM)
and Annotation–benet Maximization (AAM). The patterns hidden in web
documents have been explored in Brin (1998), where patterns are analyzed for
concept determination.
The Dawn (newspaper) and The New York Times (newspaper) have been focused
on Mahmood, Kausar and Khan (2018) for the purpose of textual analysis. `This
study also focused on online news stories and events published at The Dawn
(newspaper) as the data source in order to automatically extract concepts using
lexicon–based approach. The dictionaries used for the understanding of concepts
and meanings of the terms and/or concepts are WordNet and DBPedia.
3. LEXICON BASED CONCEPT EXTRACTION APPROACH
An application prototype has been developed for online news data in order to
extract key concepts. The prototype is developed using C# (c sharp) programming
language.
The application architecture of the developed prototype comprised of three
layers: Layer 1: Data Source, Layer 2: Middleware and Layer 3: News Mining
as shown in Figure 1. The purpose of each layer is reported in the subsequent
sections.
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Figure 1. Prototype Application Architecture.
3.1. LAYER 1: DATA SOURCE/PROVIDER
Data source/Provider layer crawls online news events and stories published
at The Dawn
1
newspaper ocial website. The application, however, allows
providing URL (Uniform Resource Locator) of a certain news website. This
study has focused on the news stories and articles of The Dawn newspaper. This
layer traverses the given URL to crawl its news events and stories available at its
several webpages. The crawler uses the existing APIs (Application Programming
Interfaces) for the traversing and retrieval of data from the source website (The
Dawn in this case).
3.2. LAYER 2: MIDDLEWARE
Middleware layer takes the news stories and articles and parses the given obtained
news stories. In particular, HTML (Hypertext Markup Language) parser and
DOM (Document Object Module) API has been used for the processing of news
stories. The parsed and processed data is stored in the relational database.
1 The Dawn (www.dawn.com)
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A relational database is the collection of data into table formats, which are
logically related to each other. The news stories and articles comprised of several
tags as represented in Figure 2.
Figure 2. HTML webpage tags in a tree structure.
Middleware layer takes the news stories and articles and parses the given obtained
news stories. In particular, HTML (Hypertext Markup Language) parser and
DOM (Document Object Module) API has been used for the processing of news
stories. The parsed and processed data is stored in the relational database. A
relational database is the collection of data into table formats, which are logically
related to each other. The news stories and articles comprised of several tags as
represented in Figure 2.
HTML Parser is, basically, a library and it is used in parsing if text les formatted
in HTML. Likewise, DOM API, written in JavaScript, is an object representation
of webpage. The news stores and articles are provided as an input to the third
layer of the developed prototype application.
3.3. LAYER 3: NEWS MINING
This layer is the key layer that actually automatically extracts concepts present
in the collected news stories and articles. In particular, this layer comprised of
Mining Manager, which performs necessary text preprocessing steps to transform
the collected and stored news stories and articles into a suitable format for further
processing.
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Mining Manager: it performs tokenization, stemming, stopword removal
operations before actual processing of automatic concept extraction.
Tokenization: this operation breaks given textual data into its tokens (i.e., terms
or words). For instance, consider a sentence ‘This study aims at automatic concept
extraction using lexicon–based approach.’ The tokenization produces the following
outcomes: ‘This’, ‘study’, ‘aims’, ‘at’, ‘automatic’, ‘concept’, ‘extraction’,
‘using’, ‘lexicon’, ‘based’, ‘approach’, ‘.’.
Stemming: stemming refers to an operation in which words (i.e., tokens)
obtained from the previous step (i.e., tokenization) are acquired into their roots
or base words. For instance, ‘Multiplying’ becomes ‘Multipli’, ‘Engineering’
becomes ‘Engine’ and many more. This step helps into a reduction of redundant
terms used in textual data.
Stopword Removal: this operation prunes unnecessary words present in the
text. These unnecessary words usually refer to auxiliary verbs and grammatically
articles. For example, ‘the’ ‘is’ ‘am’ ‘are’ ‘was’ ‘and’ ‘a’ ‘an’ and many more.
The processed tokens are further used as an input for automatic concept
extraction. The application uses popular bag–of–words (BOW) as vector space
representation model for the processed tokens. The words that are left after
stopword removal operation are the bag–of–words, each word has its frequency
in certain news story or article. The BOW is supplied to Concept Extraction
module for determining concepts.
Concept Extraction: The concept extraction module determines the meaning
and being concept state of terms, which have been processed at Mining Manager.
The BOW is further supplied to concept extraction module as shown in Figure
3 that is connected with dictionaries: WordNet, DBpedia and Linked data to
determine the meaning and concept for a given word of BOW.
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Figure 3. Lexicon based Concept Extraction Approach.
Each word in BOW undergoes for the concept extraction process. The outcomes
of the concepts are later used for visualization. In particular, the frequency of the
concepts is measured for a given article or news story. The word cloud is displayed for
the concepts and graphs represent the trends of the concepts available in the news.
4. RESULTS AND DISCUSSION
This section discusses the outcomes of the developed application prototype.
Figure 4 represents the crawled data. A certain URL of the newspaper website
is provided to crawl its data and store into a database. The collected story is
displayed at the user interface of the application as in Figure 4.
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Figure 4. Developed Application Prototype.
Figure 5 represents the concepts extraction and the frequencies used for the
BOW as an input for concept extraction module discussed in section 3.3. Since
the BOW is large in number, the prototype allows increasing or decreasing the
number terms in BOW based on their frequencies.
Figure 5. Prototype – Concepts and their Frequencies.
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The graphs and concepts in terms of word cloud are presented in the developed
prototype for a better understanding of concepts present in the news stories and
articles as reported in Figure 6.
Figure 6. Prototype – Concepts and their Word Cloud.
The outcomes of the approach help in understanding in–depth news stories
and articles, which may be used as a baseline for the decision–making strategies.
The news media has been used widely for opinion making purposes. Thus, the
extracted concepts help in getting an insight into the news events, articles and
mindset of the journalists.
5. RESEARCH CHALLENGES AND LIMITATIONS
To acquire data for this study, The Dawn newspaper has been targeted due to its
popularity and neutrality. This could be considered as the limitation of the study
since the emphasis of the study remained over concept extraction using lexicon
approach. However, the developed approach may also be provided a dataset of
any other newspaper.
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The challenges that have been encountered during the course of the research
study is PakistaniEnglish words. The injection of Urdu words in English has
been referred to as Pakistani English. For instance, chai–wala, ziaism, Sahab and
Naya Pakistan are some of the PakistaniEnglish vocabulary. The challenge is to
determine the concepts from this derived vocabulary. PakistaniEnglish vocabulary
has been not addressed in the study due to lack of its lexical chains and thorough
grammatical aspects that help in understanding words.
6. CONCLUSION
This study reported a lexicon–based approach for concept extraction. In particular,
a working prototype has been developed to demonstrate the applicability and
eectiveness of the approach. The application automatically crawls news events
and stories, which are stored in a relational database. The focus remained on The
Dawn newspaper for the data source due to its neutrality and popularity in the
region.
The collected news data has been gone through necessary text processing phases
in order to transform it for further process. The concepts have been extracted with
the help of WordNet, DBPedia and Linked Data. The extracted concepts, later,
have been displayed with visualization techniques such as Word Cloud and charts.
Generally, media has an inuential role over the minds of its audience. Thus,
the extracted concepts may help in understanding the core concepts available
in the news events and stories that may lead to strategic decision–making. The
outcomes of this study may assist and help media analysts to have an in–depth
understanding of media personnel and general public opinion about news and
facts on the ground.
The deviation of lexical chains in terms of PakistaniEnglish words will be
considered as future work. In particular, developing PakistaniEnglish corpus to
tackle the limitations of this study would be a focus in future work.
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ACKNOWLEDGEMENTS
This research has been performed under the Institute of ICT Mehran University
of Engineering and Technology, Pakistan and funded by the ICT Endowment for
Sustainable Development.
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AUTHORS
Anoud Shaikh
Ms. Anoud Shaikh is lecturer in the Department of Software
Engineering at MUET, Pakistan. She received her M.E degree from
MUET Pakistan in 2011 and is presently pursuing her PhD working on
Text Analytics. Her research interests include Software Engineering,
Databases and Data Analytics.
Naeem Ahmed Mahoto
Dr. Naeem Ahmed Mahoto is an Associate Professor and Chairman
of the Department of Software Engineering, MUET Pakistan. He
received his Master degree in Computer Engineering from MUET,
Pakistan and Ph.D in Information Engineering from Politecnico di
Torino, Italy, in 2013. His research interests are focused in the eld
of data mining and bioinformatics. His research activities are also
devoted to summarization of web documents, sentiment analysis, data
visualization and data mining.
Mukhtiar Ali Unar
Prof. Dr. Mukhtiar Ali Unar is the Dean Faculty of Electrical,
Electronics and Computer Systems Engineering and a meritorious
Professor at the Department of Computer Systems Engineering,
MUET, Pakistan. He did his B.E in Electronic Engineering from
MUET in 1986, M.Sc in Electrical and Electronic Engineering in
1995 and Ph.D in Articial Intelligence from University of Glasgow,
UK in 1999. He also remained the pro vice chancellor of MUET,
S.Z.A.Bhutto campus, Khairpur Mir’s and Director Institute of
Information & Communication Technologies MUET, Pakistan. He
has 30 years of teaching, research & management/admin experience.
He is the author of more than 60 journal/conference papers of
national/international repute.
His research interests include Artical Intelligence, Control System
Design, Digital Signal Processing and Knowledge Discovery. Dr. Unar
is a member of IEEE (USA), an aliate of International Federation
of Automatic Control, a member of Pakistan Institute of Engineers
and a member of Pakistan Engineering Council.
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