Yazar "Orhan, Umut" 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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    PageRank based Semantic Similarity Measure on a Graph based Turkish WordNet
    (IEEE, 2017) Tulu, Cagatay; Orhan, Umut
    Semantic similarity of texts is one of the important areas of Natural Language Processing, and there are several approaches to measure similarity: statistical, WordNet based, and hybrid. For all of these approaches, a lexical knowledge is used such as corpus or semantic network. WordNet is one of the most preferred and mature lexical knowledge base. In this study, we have focused on measuring semantic similarity of Turkish words with a graph based Turkish WordNet. In order to measure semantic similarities, a PageRank based application was chosen. For testing the success of the proposed system, RG65 standard similarity dataset was translated to Turkish and used as benchmark data. Similarity results of the translated RG65 dataset are computed using Turkish WordNet. Result of the computation shows rho=0.543 correlation with human judgement. Taking into account that Turkish WordNet is very limited in term of number of words and there is no study in this area for Turkish language, it is considered that also the low success for this study is acceptable.
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    PageRank based semantic similarity measure on a graph based Turkish WordNet
    (Institute of Electrical and Electronics Engineers Inc., 2017) Tulu, Cagatay; Orhan, Umut
    Semantic similarity of texts is one of the important areas of Natural Language Processing, and there are several approaches to measure similarity: statistical, WordNet based, and hybrid. For all of these approaches, a lexical knowledge is used such as corpus or semantic network. WordNet is one of the most preferred and mature lexical knowledge base. In this study, we have focused on measuring semantic similarity of Turkish words with a graph based Turkish WordNet. In order to measure semantic similarities, a PageRank based application was chosen. For testing the success of the proposed system, RG65 standard similarity dataset was translated to Turkish and used as benchmark data. Similarity results of the translated RG65 dataset are computed using Turkish WordNet. Result of the computation shows ?=0.543 correlation with human judgement. Taking into account that Turkish WordNet is very limited in term of number of words and there is no study in this area for Turkish language, it is considered that also the low success for this study is acceptable. © 2017 IEEE.
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    Semantic Relation’s Weight Determination on a Graph Based WordNet
    (2018) Tülü, Çağatay; Orhan, Umut; Turan, Erhan
    Determination of semantic relatedness between two textual items is one of the crucial phases in many Natural LanguageProcessing applications. In this study, a new approach to lexicon based semantic relation determination methods wasexperienced using WordNet 3.0 and Men’s real-life similarity dataset. Men’s test collection was used for thedetermination of the relation weights and determined weights were used in semantic relatedness computation. RG65similarity dataset was used for a benchmark of the proposed method and Spearman correlation 0.81 was gained, takinginto account that retrieving the relations weight using a large scale dataset and testing them with another real-life datasetpromises new perspectives to the determination of the relations weight and to the relatedness computation.