Evaluation of Morphological Embeddings for

Romanov, Vitaly; Khusainova, Albina

doi:10.18653/v1/w19-2010

Cited by 5 publications

(5 citation statements)

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“…Neural network-based language models produce outstanding results in a variety of NLP downstream tasks, including document categorization, named entity recognition, and machine translation (Romanov and Khusainova, 2019;Kalyan et al, 2021;Kapočiūtė-Dzikienė et al, 2021). Although the using such new-generation language models in calculating similarity scores is not very common, they have been applied in a few studies as of 2018.…”

Section: Methods Combining Word and Semantic Informationmentioning

confidence: 99%

“…• Neural network-based language models produce outstanding results in a variety of NLP downstream tasks, including document categorization, named entity recognition, and machine translation (Romanov and Khusainova, 2019;Kalyan et al, 2021;Kapočiūtė-Dzikienė et al, 2021). Few studies on the application of neural network-based models to the calculation of similarity scores have shown that they are more effective than other semantic-based methods (Ogunleye et al, 2018;Duan et al, 2019;Yong et al, 2019).…”

Section: Future Directions For Similarity Score Computationmentioning

confidence: 99%

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Reviewer Assignment Problem: A Systematic Review of the Literature

Meltem¹,

Yanık

Amasyalı

2023

jair

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Appropriate reviewer assignment significantly impacts the quality of proposal evaluation, as accurate and fair reviews are contingent on their assignment to relevant reviewers. The crucial task of assigning reviewers to submitted proposals is the starting point of the review process and is also known as the reviewer assignment problem (RAP). Due to the obvious restrictions of manual assignment, journal editors, conference organizers, and grant managers demand automatic reviewer assignment approaches. Many studies have proposed assignment solutions in response to the demand for automated procedures since 1992. The primary objective of this survey paper is to provide scholars and practitioners with a comprehensive overview of available research on the RAP. To achieve this goal, this article presents an in-depth systematic review of 103 publications in the field of reviewer assignment published in the past three decades and available in the Web of Science, Scopus, ScienceDirect, Google Scholar, and Semantic Scholar databases. This review paper classified and discussed the RAP approaches into two broad categories and numerous subcategories based on their underlying techniques. Furthermore, potential future research directions for each category are presented. This survey shows that the research on the RAP is becoming more significant and that more effort is required to develop new approaches and a framework.

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Section: Methods Combining Word and Semantic Informationmentioning

confidence: 99%

Section: Future Directions For Similarity Score Computationmentioning

confidence: 99%

Reviewer Assignment Problem: A Systematic Review of the Literature

Meltem¹,

Yanık

Amasyalı

2023

jair

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“…This study presents the application of word embedding methods to effectively use rich information in the abstract and title of project proposals in Turkish. Neural network-based word embedding methods (e.g., FastText, BERT) are used in numerous NLP tasks successfully (Romanov and Khusainova, 2019; Kapočiūtė-Dzikienė et al ., 2021; Kalyan et al ., 2021). However, a project proposal grouping research based on such next-generation representation approaches has not yet been reported to the best of our knowledge.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Neural network technologies for word embedding have recently shown remarkable results in NLP tasks (Romanov and Khusainova, 2019; Kapočiūtė-Dzikienė et al ., 2021; Kalyan et al ., 2021). However, a project proposal grouping study based on high-performance neural network-based textual feature extraction techniques has not yet been reported to the best of our knowledge.…”

Section: Introductionmentioning

confidence: 99%

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A comparative analysis of text representation, classification and clustering methods over real project proposals

Aksoy

Yanık

Amasyalı

2023

IJICC

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PurposeWhen a large number of project proposals are evaluated to allocate available funds, grouping them based on their similarities is beneficial. Current approaches to group proposals are primarily based on manual matching of similar topics, discipline areas and keywords declared by project applicants. When the number of proposals increases, this task becomes complex and requires excessive time. This paper aims to demonstrate how to effectively use the rich information in the titles and abstracts of Turkish project proposals to group them automatically.Design/methodology/approachThis study proposes a model that effectively groups Turkish project proposals by combining word embedding, clustering and classification techniques. The proposed model uses FastText, BERT and term frequency/inverse document frequency (TF/IDF) word-embedding techniques to extract terms from the titles and abstracts of project proposals in Turkish. The extracted terms were grouped using both the clustering and classification techniques. Natural groups contained within the corpus were discovered using k-means, k-means++, k-medoids and agglomerative clustering algorithms. Additionally, this study employs classification approaches to predict the target class for each document in the corpus. To classify project proposals, various classifiers, including k-nearest neighbors (KNN), support vector machines (SVM), artificial neural networks (ANN), classification and regression trees (CART) and random forest (RF), are used. Empirical experiments were conducted to validate the effectiveness of the proposed method by using real data from the Istanbul Development Agency.FindingsThe results show that the generated word embeddings can effectively represent proposal texts as vectors, and can be used as inputs for clustering or classification algorithms. Using clustering algorithms, the document corpus is divided into five groups. In addition, the results demonstrate that the proposals can easily be categorized into predefined categories using classification algorithms. SVM-Linear achieved the highest prediction accuracy (89.2%) with the FastText word embedding method. A comparison of manual grouping with automatic classification and clustering results revealed that both classification and clustering techniques have a high success rate.Research limitations/implicationsThe proposed model automatically benefits from the rich information in project proposals and significantly reduces numerous time-consuming tasks that managers must perform manually. Thus, it eliminates the drawbacks of the current manual methods and yields significantly more accurate results. In the future, additional experiments should be conducted to validate the proposed method using data from other funding organizations.Originality/valueThis study presents the application of word embedding methods to effectively use the rich information in the titles and abstracts of Turkish project proposals. Existing research studies focus on the automatic grouping of proposals; traditional frequency-based word embedding methods are used for feature extraction methods to represent project proposals. Unlike previous research, this study employs two outperforming neural network-based textual feature extraction techniques to obtain terms representing the proposals: BERT as a contextual word embedding method and FastText as a static word embedding method. Moreover, to the best of our knowledge, there has been no research conducted on the grouping of project proposals in Turkish.

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Advancing natural language processing (NLP) applications of morphologically rich languages with bidirectional encoder representations from transformers (BERT): an empirical case study for Turkish

et al. 2021

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Language model pre-training architectures have demonstrated to be useful to learn language representations. bidirectional encoder representations from transformers (BERT), a recent deep bidirectional self-attention representation from unlabelled text, has achieved remarkable results in many natural language processing (NLP) tasks with fine-tuning. In this paper, we want to demonstrate the efficiency of BERT for a morphologically rich language, Turkish. Traditionally morphologically difficult languages require dense language pre-processing steps in order to model the data to be suitable for machine learning (ML) algorithms. In particular, tokenization, lemmatization or stemming and feature engineering tasks are needed to obtain an efficient data model to overcome data sparsity or high-dimension problems. In this context, we selected five various Turkish NLP research problems as sentiment analysis, cyberbullying identification, text classification, emotion recognition and spam detection from the literature. We then compared the empirical performance of BERT with the baseline ML algorithms. Finally, we found enhanced results compared to base ML algorithms in the selected NLP problems while eliminating heavy pre-processing tasks.

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Evaluation of Morphological Embeddings for

Cited by 5 publications

References 4 publications

Reviewer Assignment Problem: A Systematic Review of the Literature

Reviewer Assignment Problem: A Systematic Review of the Literature

A comparative analysis of text representation, classification and clustering methods over real project proposals

Advancing natural language processing (NLP) applications of morphologically rich languages with bidirectional encoder representations from transformers (BERT): an empirical case study for Turkish

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