Kumova Metin, Senem

Profile Picture
Profile URL
https://hdl.handle.net/20.500.14365/7136
Name Variants
Kumova Meti̇n, Senem
Metin, Senem Kumova
Kumova Metin, S.
Metin, S.K.
Job Title
Email Address
senem.kumova@ieu.edu.tr
Main Affiliation
05.04. Software Engineering
Status
Current Staff
Website
ORCID ID
0000-0002-9606-3625
Scopus Author ID
24471923700
Turkish CoHE Profile ID
87C85420722F56B4
Google Scholar ID
_kO_FSEAAAAJ
WoS Researcher ID
FZN-5765-2022
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senem_midi.png (1.39 MB)

Sustainable Development Goals

8

DECENT WORK AND ECONOMIC GROWTH
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9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
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2

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10

REDUCED INEQUALITIES
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17

PARTNERSHIPS FOR THE GOALS
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12

RESPONSIBLE CONSUMPTION AND PRODUCTION
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7

AFFORDABLE AND CLEAN ENERGY
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1

NO POVERTY
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GENDER EQUALITY
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13

CLIMATE ACTION
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4

QUALITY EDUCATION
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14

LIFE BELOW WATER
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2

ZERO HUNGER
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15

LIFE ON LAND
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PEACE, JUSTICE AND STRONG INSTITUTIONS
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CLEAN WATER AND SANITATION
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GOOD HEALTH AND WELL-BEING
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SUSTAINABLE CITIES AND COMMUNITIES
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Documents

35

Citations

172

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Documents

28

Citations

92

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Scholarly Output

48

Articles

20

Views / Downloads

79/140

Supervised MSc Theses

7

Supervised PhD Theses

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WoS Citation Count

92

Scopus Citation Count

172

WoS h-index

5

Scopus h-index

6

Patents

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Projects

15

WoS Citations per Publication

1.92

Scopus Citations per Publication

3.58

Open Access Source

14

Supervised Theses

7

JournalCount
26th IEEE Signal Processing and Communications Applications Conference, SIU 20182
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)2
International Journal of Advanced Computer Science and Applications2
2015 23Rd Sıgnal Processıng And Communıcatıons Applıcatıons Conference (Sıu)1
2017 25th Signal Processing and Communications Applications Conference, SIU 20171
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Now showing 1 - 10 of 48
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 1
    Optimizing High-Dimensional Text Embeddings in Emotion Identification: a Sliding Window Approach
    (Science and Technology Publications, Lda, 2024) Uymaz, H.A.; Metin, S.K.
    Natural language processing (NLP) is an interdisciplinary field that enables machines to understand and generate human language. One of the crucial steps in several NLP tasks, such as emotion and sentiment analysis, text similarity, summarization, and classification, is transforming textual data sources into numerical form, a process called vectorization. This process can be grouped into traditional, semantic, and contextual vectorization methods. Despite their advantages, these high-dimensional vectors pose memory and computational challenges. To address these issues, we employed a sliding window technique to partition high-dimensional vectors, aiming not only to enhance computational efficiency but also to detect emotional information within specific vector dimensions. Our experiments utilized emotion lexicon words and emotionally labeled sentences in both English and Turkish. By systematically analyzing the vectors, we identified consistent patterns with emotional clues. Our findings suggest that focusing on specific sub-vectors rather than entire high-dimensional BERT vectors can capture emotional information effectively, without performance loss. With this approach, we examined an increase in pairwise cosine similarity scores within emotion categories when using only sub-vectors. The results highlight the potential of the use of sub-vector techniques, offering insights into the nuanced integration of emotions in language and the applicability of these methods across different languages. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.
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    Conference Object
    Citation - WoS: 10
    Citation - Scopus: 14
    Collocation Extraction in Turkish Texts Using Statistical Methods
    (2010) Kumova Metin S.; Karao?lan B.
    Collocation is the combination of words in which words appear together more often than by chance. Since collocations are blocks of meaning, they play an important role in natural language processing applications (word sense disambiguation, part of speech tagging, machine translation, etc). In this study, a corpus of Turkish is subjected to the following statistical techniques: frequency of occurrence, mutual information and hypothesis tests. We have utilized both stemmed and surface form of corpus to explore the effect of stemming in collocation extraction. The techniques are evaluated by recall and precision measures. Chi-square hypothesis test and mutual information methods have produced better results compared to other methods on Turkish corpus. In addition, we have found that a stemmed corpus facilitates discrimination between successful and unsuccessful collocation extraction methods. © 2010 Springer-Verlag Berlin Heidelberg.
  • Thumbnail Image
    Conference Object
    Citation - WoS: 1
    Citation - Scopus: 3
    A Procedure To Build Multiword Expression Data Set
    (IEEE, 2017) Metin, Senem Kumova; Taze, Mehmet
    In this paper, we propose a procedure employing natural language processing methods to build a golden standard multiword expression data set and present our Turkish MWE data set of 3946 positive and 4230 negative candidates that is built following the proposed procedure. The proposed procedure covers three main tasks. The first task is collecting a variety of MWE data resources in order to extract MWE candidates. We suggest the use of corpora together with idiom and term dictionaries. Second task in building MWE data set is extracting different types of MWE candidates from the resources. Here, we suggest the aggregation of four methods. Firstly, statistical methods are applied to extract MWE candidates that have high occurrence frequencies. Secondly, the linguistic properties such as part of speech patterns are considered to select MWE candidates. Thirdly, the candidates that mimic the properties of idioms or are already true idioms are chosen. Lastly, the candidates with domain specific properties, term-similar, are extracted. The final task to build a golden standard MWE data set is the labeling. In this task, the candidates are labeled either as MWE or non-MWE by multiple judges.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 2
    Standard Co-Training in Multiword Expression Detection
    (Springer International Publishing Ag, 2017) Metin, Senem Kumova
    Multiword expressions (MWEs) are units in language where multiple words unite without an obvious/known reason. Since MWEs occupy a prominent amount of space in both written and spoken language materials, identification of MWEs is accepted to be an important task in natural language processing. In this paper, considering MWE detection as a binary classification task, we propose to use a semi-supervised learning algorithm, standard co-training [1] Co-training is a semi-supervised method that employs two classifiers with two different views to label unlabeled data iteratively in order to enlarge the training sets of limited size. In our experiments, linguistic and statistical features that distinguish MWEs from random word combinations are utilized as two different views. Two different pairs of classifiers are employed with a group of experimental settings. The tests are performed on a Turkish MWE data set of 3946 positive and 4230 negative MWE candidates. The results showed that the classifier where statistical view is considered succeeds in MWE detection when the training set is enlarged by co-training.
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    Conference Object
    Enhancing Text Embeddings for Emotion Detection: A Study on Dimensionality Reduction and Lexicon Filtering
    (Springer Science and Business Media Deutschland GmbH, 2026) Uymaz, H.; Kumova Metin, S.
    Emotion detection in textual data is a crucial task in Natural language processing (NLP), yet standard word embeddings often fail to capture emotional nuances. This study explores an emotion-enrichment approach that refines text representations by integrating emotional information into word embeddings. In the study, two key challenges are primarily addressed: limitations of emotion lexicons, which may include ambiguous or misclassified words, and high-dimensional vector representations, which may increase computational complexity. To improve lexicon quality, which is an important data source in emotion enrichment studies, a filtering mechanism is introduced aiming to remove the words with inconsistent emotional associations, enhancing lexicon precision. Additionally, a sliding window-based dimensionality reduction method is applied to BERT embeddings to identify emotion-rich vector segments, reducing computational cost while preserving emotional information. Experiments are conducted in both English and Turkish to evaluate the impact of lexicon filtering and dimensionality reduction on emotion detection. Results show that filtering improves the accuracy of emotion-enriched representations, while sub-vector selection gives the possibility of finding more representative parts about emotional content. By focusing on emotion-relevant vector dimensions, the proposed method achieves superior performance compared to full-dimensional embeddings. This research contributes to multilingual emotion representation by refining lexicon-based enrichment strategies and optimizing embedding spaces for emotion detection. The findings highlight the importance of structured lexicon filtering and targeted dimensionality reduction in improving sentiment and emotion analysis models. © 2025 Elsevier B.V., All rights reserved.
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    Conference Object
    A Proposal for Corpus Normalization
    (IEEE, 2013) Karaoglan, Bahar; Kisla, Tarik; Dincer, Bekir Taner; Metin, Senem Kumova
    In order to compare work done under natural language processing, the corpora involved in different studies should be standardized/normalized. Entropy, used as language model performance metric, totally depends on signal information. Whereas, when language is considered semantic information should also be considered. Here we propose a metric that exploits Zipf's and Heaps' power laws to respresent semantic information in terms of signal information and estimates the amount of information anticipated from a corpus of given length in words. The proposed metric is tested on 20 different lengths of sub-corpora drawn from major corpus in Turkish (METU). While the entropy changed depending on the length of the corpus, the value of our proposed metric stayed almost constant which supports our claim about normalizing the corpus.
  • Thumbnail Image
    Conference Object
    Citation - WoS: 2
    Citation - Scopus: 3
    Extracting the Features of Similarity in Short Texts
    (IEEE, 2015) Kisla, Tarik; Metin, Senem Kumova; Karaoglan, Bahar
    Automatic identification of text similarity has found applications in information retrieval, text summarization, assessment of machine translation, assessment of question answering, word sense disambiguation and many more. In this work, the results of discrimant analysis applied to find out the cumulative effect of the attributes used in the literature so far (ratio of common words, text lentgths, common word sequences, synonyms, hypernyms, hyponyms) in detecting word similarity are reported.
  • Thumbnail Image
    Article
    Enlarging Multiword Expression Dataset by Co-Training
    (Scientific Technical Research Council Turkey-Tubitak, 2018) Kumova Metin, Senem
    In multiword expressions (MWEs), multiple words unite to build a new unit in language. When MWE identification is accepted as a binary classification task, one of the most important factors in performance is to train the classifier with enough number of labelled samples. Since manual labelling is a time-consuming task, the performances of MWE recognition studies are limited with the size of the training sets. In this study, we propose the comparison-based and common-decision co-training approaches in order to enlarge the MWE dataset. In the experiments, the performances of the proposed approaches were compared to those of the standard co-training [1] and manual labelling where statistical and linguistic features are employed as two different views of the MWE dataset [2]. A number of tests with different settings were performed on a Turkish MWE dataset. Ten different classifiers were utilized in the experiments and the best performing classifier pair was observed to be the SMO-SMO pair. The experimental results showed that the common-decision co-training approach is an alternative to hand-labeling of large MWE datasets and both newly proposed approaches outperform the standard co-training [2] when the training set is to be enlarged in MWE classification.
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    Article
    Citation - WoS: 2
    Citation - Scopus: 4
    Evaluation of Function Words in Turkish Based on the Zipf's 1. Law
    (Gazi Univ, Fac Engineering Architecture, 2008) Metin, Senem Kumova
    In this study, function words that are used to construct the grammatical structure in Natural Language and that does not change the information content of the text have been investigated. Application of Zipf's first Law on Turkish texts and expectance of high frequency words to be function words constitute the fundamentals of the research. In the study the test for the proposed method has been performed on different corpus including Turkish texts ans results have been evaluated.
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    Article
    Citation - Scopus: 1
    Navigating the Storm: the Impact of the Russia-Ukraine War on EU's Quest for Strategic Autonomy
    (Routledge Journals, Taylor & Francis Ltd, 2025) Unaldilar, Sinem; Unal, Betul Aydogan; Metin, Senem Kumova
    This article investigates how the Russia-Ukraine war has reshaped the European Union's defence priorities and its pursuit of strategic autonomy. Using advanced text analytics and machine learning methods of over 26,000 European External Action Service (EEAS) documents, we examine shifts in topic prevalence across the Strategic Compass while considering broader geopolitical dynamics, including Brexit, China's positioning and US-NATO relations. Using EEAS documents spanning from one year before to two years after the Russian invasion, we employ a two-stage topic modelling approach. First, we classify EEAS documents under the Strategic Compass dimensions through majority voting, utilising text embedding and supervised learning models, followed by detailed mapping of documents to specific subtopics derived from the Strategic Compass framework by topic modelling. Our findings reveal a significant reorientation from a pre-war emphasis on long-term capability development towards an increased focus on immediate crisis response and strengthened international partnerships postinvasion. While the EU demonstrated enhanced operational capacity and partnership-building, persistent challenges remain in achieving comprehensive strategic autonomy and becoming an international actor. These results suggest that external crises can accelerate strategic autonomy in discourse. However, achieving genuine independence requires more effective crisis response, long-term capability development, and strong partnership management.