Semi-Supervised Keyphrase Extraction on Scientific Article using Fact-based Sentiment

TELKOMNIKA, Vol.16, No.4, August 2018, pp. 1771~1778
ISSN: 1693-6930, accredited First Grade by Kemenristekdikti, Decree No: 21/E/KPT/2018
DOI: 10.12928/TELKOMNIKA.v16i4.5473  1771
Received December 22, 2016; Revised January 20, 2018; Accepted February 18, 2018
Semi-Supervised Keyphrase Extraction on Scientific
Article using Fact-based Sentiment
Felix Christian Jonathan*
1
, Oscar Karnalim
2
Maranatha Christian University, Indonesia
*Corresponding author, e-mail: oscar.karnalim@gmail.com
Abstract
Most scientific publishers encourage authors to provide keyphrases on their published article.
Hence, the need to automatize keyphrase extraction is increased. However, it is not a trivial task
considering keyphrase characteristics may overlap with the non-keyphrase’s. To date, the accuracy of
automatic keyphrase extraction approaches is still considerably low. In response to such gap, this paper
proposes two contributions. First, a feature called fact-based sentiment is proposed. It is expected to
strengthen keyphrase characteristics since, according to manual observation, most keyphrases are
mentioned in neutral-to-positive sentiment. Second, a combination of supervised and unsupervised
approach is proposed to take the benefits of both approaches. It will enable automatic hidden pattern
detection while keeping candidate importance comparable to each other. According to evaluation, fact-
based sentiment is quite effective for representing keyphraseness and semi-supervised approach is
considerably effective to extract keyphrases from scientific articles.
Keywords: Fact-based sentiment; Semi-supervised approach; Keyphrase extraction; Scientific article;
Deep belief network
Copyright © 2018 Universitas Ahmad Dahlan. All rights reserved.
1. Introduction
Keyphrases (or keywords) are natural language terms used to represent the context of
a document [1]. It is frequently used to check whether given document matches user need
without reading the whole content. Keyphrases are frequently found on scientific articles [2];
scientific publishers encourage authors to provide those phrases on published article so that
prospective readers will not waste their time for reading irrelevant articles comprehensively.
Considering this need, automatic keyphrase extraction approaches have been developed to
mitigate human effort [2].
Automate keyphrase extraction is not a trivial task; keyphrase characteristics may
overlap with the non-keyphrase’s [3]. For instance, even though most keyphrases are found in
abstract, not all abstract terms are keyphrases. As a result, additional unique characteristics are
introduced to distinguish keyphrases from non-keyphrases.
It is true that the use of numerous characteristics enhances the accuracy of automatic
keyphrase extraction for scientific articles. However, to date, its accuracy is still considerably
low [4]. Hence, this paper introduces fact-based sentiment as a new keyphrase characteristic.
Different with standard sentiment, it is purely resulted from facts (with an assumption that
scientific articles contain facts). We would argue that such characteristic may enchance the
accuracy of existing keyphrase extraction since, according to our manual observation; fact-
based sentiment of keyphrases is patterned implicitly on scientific articles: most keyphrases are
mentioned while discussing novelty and benefits of their work and these aspects are frequently
written as fact-based sentences in neutral-to-positive sentiment.
To enhance the accuracy further, we also propose a combination of supervised and
unsupervised approach for extracting keyphrases. It is inspired from [5] where each candidate is
sorted in unsupervised manner (i.e., TF-IDF ranking [1] in our case) and fed into a classifier to
determine its keyphraseness in supervised manner (i.e., Deep Belief Networks (DBN) [6] in our
case).

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TELKOMNIKA Vol. 16, No. 4, August 2018: 1771-1778
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2. Related Works
In general, keyphrase extraction can be roughly classified into two categories:
unsupervised or supervised approach [2]. Unsupervised approach relies on ranking and
similarity mechanism. It assigns each keyphrase candidate with a particular score and select the
highest-scored candidates as its keyphrase. Some examples of such approach are works
proposed in [7–11] where they use graph-based ranking, topic-based clustering, simultaneous
learning, language modeling, and conditional random fields respectively. In contrast, supervised
approach relies on learning algorithm and training dataset. Different with unsupervised
approach, the pattern is not required to be defined manually. It is automatically extracted from
training dataset. Two learning algorithms which have been used in this approach are naive
bayes [12,13] and decision tree [14].
Most works about keyphrase extraction are focused on scientific articles considering
keyphases (or keywords) are required to represent each scientific article [15–19]. Some
examples of them are: 1) a work proposed in [15] that combines maximal frequent sequences
and PageRank; 2) a work proposed in [20] that extracts keyphrases based on sentence
clustering & Latent Dirichlet Allocation; 3) a work proposed in [16] that utilizes skill set concept;
and 4) a work proposed in [17] that incorporates Likey ratio.
It is important to note that keyphrase extraction is not the only emerging topic regarding
scientific articles. Other topics such as title generation [21], scientific data retrieval [22], scientific
article management [23], and publication repository [24] are also emerged. However, in this
work, we will only focus on keyphrase extraction.
The accuracy of keyphrase extraction on scientific articles can be enhanced through
three mechanisms: local extraction, structure utilization, and implicit behavior utilization. First,
local extraction means that only candidates from particular sections will be considered; those
sections are assumed to have all keyphrases. Some sections which have been used for local
extraction are abstract [25,26], references [18], and the first 2000 characters [27]. Second,
structure utilization means that article structure is converted to feature(s) for determining
keyphraseness. It is frequently used with an assumption that the structure of scientific article
can be generalized. This mechanism has been used in several works [28–30] where some
features are derived from article structure. Third, implicit behavior utilization means that the
behavior of scientific articles is mapped to feature(s) for determining keyphraseness. For
instance, Treeratpituk et al [31] and Berend & Farkas [32] consider acronym as one of their
learning features with an assumption that it is frequently used on scientific articles to enhance
article readability. Another example is a work proposed in [33] that does not favor terms in
bracket as a feature. They assume such terms are seldom used as keyphrases.
Nevertheless, despite several enhancement mechanisms exist, the accuracy of
keyphrase extraction on scientific articles is still low [34]. We would argue that such
phenomenon is caused by two rationales. First, some keyphrase characteristics are not
exclusively owned by keyphrases; they are also owned by non-keyphrases. Second, both
supervised and unsupervised approach have their own drawback [5]. Unsupervised approach
disables automatic hidden keyphrase pattern detection while supervised approach disables
comparable candidate importance.
3. Methodology
This paper aims to enhance the accuracy of keyphrase extraction on scientific articles
by proposing two contributions. First, a feature called fact-based sentiment is proposed. It works
in similar manner as standard sentiment except that it is purely based on fact. Such feature is
expected to strengthen keyphrase characteristics since, according to manual observation, most
keyphrases are mentioned while discussing novelty & benefits of their work and these aspects
are frequently written as fact-based sentences in neutral-to-positive sentiment. It is true that
some keyphrases are also written while discussing related works and drawbacks. However, its
occurrence is typically low due to research scope and limited paper page. Second, a
combination of supervised and unsupervised approach is proposed to take the benefits of both
approaches [5]. It will enable automatic hidden pattern detection while keeping candidate
importance comparable to each other.
In general, our proposed keyphrase extraction consists of three phases which are: 1)
keyphrase candidate identification; 2) keyphrase ranking; and 3) keyphrase classification.

TELKOMNIKA ISSN: 1693-6930 
Semi-Supervised Keyphrase Extraction on Scientific … (Felix Christian Jonathan)
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Further, our work also incorporates a module to train learning model for keyphrase
classification.
3.1. Keyphrase Candidate Identification
This phase identifies keyphrase candidates from a scientific article based on several
limitation heuristics. These heuristics are:
a. Keyphrase candidate should be a noun phrase with phrase length lower or equal with 4
words. This heuristic is applied since most keyphrase candidates are noun phrase in 1-to-4
grams according to several works (2,3). Noun phrase is identified based on DFA proposed
in [35] which regular expression can be seen in (1). This expression incorporates Penn
Treebank Part-Of-Speech (POS) notation where POS of each token is obtained using
Stamford log-linear part-of-speech tagger [36].
(ε+DT)(JJ+JJR+JJS)*(NN+NNP+NNS+NNPS)+ (1)
b. Keyphrase candidate should not contain stop words as its keyphrase member. This
heuristic is inspired from [12]. Stop word list is taken from Snowball stop word list
(http://snowball.tartarus.org/algorithms/english/stop.txt) with an assumption that such list
represents natural language stop words.
c. Keyphrase candidate should not be recognized as a named entity. This heuristic is applied
based on our manual observation from a dataset proposed in [30]. We found that most
keyphrases on scientific publications are not related to people, organization, and location
name. Named entity is recognized using Stanford Named Entity Recognizer [37].
3.2. Keyphrase Ranking
This phase sorts all keyphrase candidates based on its importance in descending order.
Keyphrase importance is defined using TF-IDF weighting (1) described in (2); tf(t) represents
term frequency of noun phrase t, df(t) represents document frequency that contain noun phrase
t, and N represents the number of documents in collection. TF-IDF is selected as our ranking
mechanism due to its very robust performance across different dataset (3).
TFIDF(t,D) = tf(t)*(-
2
log (df(t)/N)) (2)
To handle affixes phenomena found on natural language, candidates with similar
lemma are merged as one candidate where its score is summed and its lemma is considered as
their candidate term. For example, suppose there are two candidates which are networker and
networking where TF-IDF score for networker is 1 and TF-IDF score for networking is 2. Since
both candidates yield similar lemma (i.e., network), both candidates will be replaced with a
candidate called network where its TF-IDF score is 1 + 2 = 3. Lemma for each candidate is
obtained using Stanford CoreNLP [38].
3.3. Keyphrase Classification
After keyphrase candidates are stored on descending order based on their respective
importance, each candidate will be popped out from the beginning of the list and fed to a
classifier until N keyphrases are selected. Our approach incorporates Deep Belief Networks (6)
(DBN) as our classifier since DBN is a deep learning algorithm and deep learning is proven to
be more effective than standard learning on various learning task [39]. DBN is commonly used
to extract deep hierarchical representation based on given dataset. As suggested by Bengio et
al [40], our DBN is also pre-trained with Restricted Boltzmann Machine (RBM) so that its initial
node weights are more synchronized with the data itself.
Our classifier incorporates 9 classification features which are listed on Table 1. The first
three features represent TF and IDF. Even though most recent works only apply either TF & IDF
or TF-IDF since they share considerably similar characteristic [41], we believe that utilizing them
at once may yield more representative pattern. SDD, SDS, and PDS are calculated based on its
average relative position toward a particular component where each number is pre-normalized
based on their respective size to avoid misleading pattern.

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Table 1. Classification Features
ID Feature Description
TF Term Frequency The number of candidate occurrence within given PDF file
IDF Inverse Document Frequency Inverse number of candidate occurrence within collection
TFIDF
Term Frequency – Inverse
Document Frequency
A numeric value to represent candidate importance toward given document
in a collection
SDD Section Distance in Document
The average number of sections preceding its container section where each
number is normalized by the total number of section
SDS Sentence Distance in Section
The average number of sentence preceding its container sentences where
each number is normalized by the total number of sentence in container
section
PDS Phrase Distance in Sentence
The average number of words preceding its phrase occurrence where each
number is normalized by the total number of words in container sentence
WC Word Count Total words on keyphrase candidate
PL Phrase Length Total characters on keyphrase candidate
FSV Fact-based Sentiment Value The average fact-based sentiment value of sentence container
Fact-based sentiment is our unique feature which has not been incorporated on other
keyphrase extraction works. Its value for each candidate is defined as the average sentiment
value for each sentence where the candidate occurs. Sentiment value is obtained using
Sentiment Analysis module on Stanford CoreNLP (38). This module returns an integer value
ranged from 0 to 4: 0 represents extremely negative; 2 represents neutral; and 4 represents
extremely positive. For example, suppose a keyphrase candidate Artificial Neural Network is
occurred in two sentences from an article. The first sentence is Artificial Neural Network is easy
to use and understand compared to statistical methods whereas the second one is It is hard to
interpret the model of Artificial Neural Network since this approach is a black box once it is
trained. Based on Sentiment Analysis module on Stanford CoreNLP, the first sentence is
assigned as 3 (positive) whereas the second one is assigned as 1 (negative). Thus, fact-based
sentiment value of Artificial Neural Network will be (3 + 1)/2 = 2 (neutral).
3.4. Learning Model Training
Since DBN requires training data to tune its weight, each article given as a part of
training data is converted into 80 training instances; half of them are keyphrases while the rest
of them are non-keyphrases. Keyphrases are selected from human-tagged keyphrases on given
article. If the number of actual keyphrase is lower than 40, actual keyphrases will be
oversampled till their number reaches 40. In contrast, non-keyphrases are selected based on
TF-IDF ranking.
It is important to note that we do not include all non-keyphrases as instances based on
two rationales. First, the number of non-keyphrases for each article is extremely large and
processing all of them may be inefficient. Second, the proportion between keyphrases and non-
keyphrases for each article is extremely imbalance. It may generate biased result if all of them
are included as training instances.
4. Evaluation
4.1. Evaluating Learning Features
The effectiveness of classification features incorporated in our approach will be
evaluated by calculating accuracy difference between default and feature-excluded scheme for
each feature. The detail of how to calculate the difference can be seen in (3). It is calculated by
subtracting the accuracy of default scheme with the accuracy of feature-excluded scheme.
Default scheme represents learning scheme that incorporates all classification features whereas
feature-excluded scheme is similar to default scheme except that it excludes target feature. The
accuracy for each schema is calculated using 10-fold cross validation while DBN are set with
500 epochs, 0.1 learning rate, 0.9 momentum, and 5 layers with 9 nodes for each layer.
acc_diff(f) = default_acc – f_exc_acc(f) (3)
For our evaluation dataset, we adapt dataset proposed in (30) which consists of 211
scientific articles. We convert keyphrases for each article to their respective lemma to overcome

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affix issues and remove articles without proper keyphrases. As a result, we have 16.320
instances from 204 articles.
Accuracy difference for each feature can be seen on Figure 1. Horizontal axis
represents classification features whereas its vertical axis represents accuracy difference
values. Several findings can be deducted from such result. First, IDF is the most important
feature; it yields the highest accuracy difference. It is natural since IDF represents the
uniqueness of keyphrase candidate toward given article in collection. Second, TFIDF can be
replaced with TF and IDF since it generates small accuracy difference (even though it is still a
positive difference). Third, the variance of our keyphrase phrase length is quite high since PL
yields the lowest accuracy difference. Fourth, PL should not be used as a feature since its
accuracy difference yields a negative value.
Figure 1. Accuracy difference of classification features
When compared to other classification features, Fact-based Sentiment Value (FSV) is
considerably important since its accuracy difference outperforms half of our proposed features.
It outperforms SDS, PDS, WC, and PL. In other words, it can be stated that fact-based
sentiment is quite effective for differentiating keyphrases from non-keyphrases.
4.2. Evaluating Overall Effectiveness
The overall effectiveness of our proposed approach is measured using standard IR
metrics namely precision, recall, and F-measure. Each IR-metric is generated by comparing
generated keyphrases with human-tagged keyphrases under three retrieving schemes: Top-5,
Top-10, and Top-15. In terms of evaluation dataset. we utilize dataset used for evaluating
learning features by deriving it to three datasets: Default Dataset (DD), Occurrence Dataset
(OD), and Candidate Dataset (CD).
First, DD is generated by replicating dataset used for evaluating learning features. It is
conducted to measure overall effectiveness in general. Second, OD is generated by excluding
human-tagged keyphrases that are not found on article content. It is conducted to measure
overall effectiveness when selected keyphrase is found on article content. Third, CD is
generated by excluding human-tagged keyphrases that are not found on article content or are
not recognized as keyphrase candidate through proposed candidate selection heuristic. It is
conducted to measure the effectiveness of TF-IDF + DBN for extracting keyphrases.
Evaluation results for overall effectiveness can be seen on Table 2. In all datasets,
recall is proportional to the number of retrieved keyphrases yet inversely proportional to
precision. Further, their respective harmonic mean (i.e., F-measure) is still lowered when the
number of retrieved keyphrases increases. We would argue that both findings are natural
considering the number of assigned keyphrases for each article is considerably small (a typical
scientific article only has about 3 to 5 keyphrases).

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Table 2. Evaluation Metrics for Overall Effectiveness
Dataset Retrieving Scheme Precision Recall F-Measure
Default Dataset (DD)
Top-5 12.25% 15.23% 13.22%
Top-10 7.01% 17.57% 9.79%
Top-15 5.02% 18.93% 7.81%
Occurrence Dataset (OD)
Top-5 12.31% 18.01% 14.01%
Top-10 7.04% 20.85% 10.18%
Top-15 5.06% 22.63% 8.05%
Candidate Dataset (CD)
Top-5 12.40% 20.02% 14.62%
Top-10 7.10% 23% 10.47%
Top-15 5.10% 24.85% 8.24%
Among three datasets, DD yields the lowest effectiveness, followed by OD and CD
respectively. Hence, it can be stated that some keyphrases are not found on article content or
excluded as the result of candidate selection heuristics. However, since the differences are
considerably small, it can be stated that most keyphrases are still found on its article content
and passed our candidate selection heuristics. Our proposed approach yields 13.22% F-
measure in Top-5 default scheme when evaluated based on DD. Therefore, it can be stated that
our approach is moderately effective considering most keyphrase extraction approaches
generate similar F-measure [34].
5. Conclusion and Future Work
In this paper, we have proposed a keyphrase extraction approach that utilizes fact-
based sentiment and semi-supervised approach. According to our evaluation, two findings can
be deducted. First, fact-based sentiment is quite effective for representing keyphraseness; it
ranked as the 5th in terms of accuracy difference. Second, semi-supervised approach is
considerably effective to extract keyphrases from scientific articles. it generates moderate
F-measure.
For future work, we plan to measure the effectiveness of our approach on different
scientific article dataset. We want to know whether its impact is consistent toward various
datasets. In addition, we also plan to compare the effectiveness of our approach when
compared with other publicly available keyphrase extraction system such as KEA [12] and
GenEx [14].
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