Yazar "Tulu, Cagatay Neftali" seçeneğine göre listele

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    A novel embedding approach to learn word vectors by weighting semantic relations: SemSpace
    (Pergamon-Elsevier Science Ltd, 2021) Orhan, Umut; Tulu, Cagatay Neftali
    In this study, we propose a novel embedding approach, called as SemSpace, to determine word vectors of synsets and to find the best weights for semantic relations. First, SemSpace finds the optimum weights to the semantic relations in WordNet by aligning them to values produced by human intelligence, and then, determines word vectors of synsets by adjusting euclidean distances among them. Proposed approach requires two inputs; first, a lexical-semantic network such as WordNet, second, a word-level similarity dataset generated by people. In the experiments, we used WordNet 3.0 data for the lexical-semantic network, and three (RG65, WS353, and MEN3K) benchmark testsets to align semantic weights. Using the aligned semantic weights and the determined word vectors, the obtained resultsresults on the benchmark testsets are compared with literature studies. According to the obtained results, it might be concluded that SemSpace is not only successful to find word level semantic similarity values and semantic weights, but also to discover new semantic relations with their semantic levels.
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    Automatic Short Answer Grading With SemSpace Sense Vectors and MaLSTM
    (IEEE-Inst Electrical Electronics Engineers Inc, 2021) Tulu, Cagatay Neftali; Ozkaya, Ozge; Orhan, Umut
    Automatic assessment of exams is widely preferred by educators than multiple-choice exams because of its efficiency in measuring student performance, lack of subjectivity when evaluating student response, and faster evaluation time than the time consuming manual evaluation. In this study, a new approach for the Automatic Short Answer Grading (ASAG) is proposed using MaLSTM and the sense vectors obtained by SemSpace, a synset based sense embedding method built leveraging WordNet. Synset representations of the Student's answers and reference answers are given as input into parallel LSTM architecture, they are transformed into sentence representations in the hidden layer and the vectorial similarity of these two representation vectors are computed with Manhattan Similarity in the output layer. The proposed approach has been tested using the Mohler ASAG dataset and successful results are obtained in terms of Pearson (r) correlation and RMSE. Also, the proposed approach has been tested as a case study using a specific dataset (CU-NLP) created from the exam of the Natural Language Processing course in the Computer Engineering Department of Cukurova University. And it has achieved a successful correlation. The results obtained in the experiments show that the proposed system can be used efficiently and effectively in context-dependent ASAG tasks.
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    Experimental Comparison of Pre-Trained Word Embedding Vectors of Word2Vec, Glove, FastText for Word Level Semantic Text Similarity Measurement in Turkish
    (Lublin Univ Technology, Poland, 2022) Tulu, Cagatay Neftali
    This study aims to evaluate experimentally the word vectors produced by three widely used embedding methods for the word-level semantic text similarity in Turkish. Three benchmark datasets SimTurk, AnlamVer, and RG65_Turkce are used in this study to evaluate the word embedding vectors produced by three different methods namely Word2Vec, Glove, and FastText. As a result of the comparative analysis, Turkish word vectors produced with Glove and FastText gained better correlation in the word level semantic similarity. It is also found that The Turkish word coverage of FastText is ahead of the other two methods because the limited number of Out of Vocabulary (OOV) words have been observed in the experiments conducted for FastText. Another observation is that FastText and Glove vectors showed great success in terms of Spearman correlation value in the SimTurk and AnlamVer datasets both of which are purely prepared and evaluated by local Turkish individuals. This is another indicator showing that these aforementioned datasets are better representing the Turkish language in terms of morphology and inflections.