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1	A Study on Resolution and Retrieval of Implicit Entity References in Microblogs / マイクロブログにおける暗黙的な実体参照の解決および検索に関する研究 Lu, Jun-Li 23 March 2020 (has links) 京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第22580号 / 情博第717号 / 新制\|\|情\|\|123(附属図書館) / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授吉川正俊, 教授黒橋禎夫, 教授田島敬史, 教授田中克己(京都大学名誉教授) / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM Implicit Entity References Entity Identification Microblog Search Deep Transfer Learning Deep Reinforcement Learning 007
2	Learning Transferable Features for Diagnosis of Breast Cancer from Histopathological Images Al Zorgani, Maisun M., Irfan, Mehmood,, Ugail, Hassan 25 March 2022 (has links) No / Nowadays, there is no argument that deep learning algorithms provide impressive results in many applications of medical image analysis. However, data scarcity problem and its consequences are challenges in implementation of deep learning for the digital histopathology domain. Deep transfer learning is one of the possible solutions for these challenges. The method of off-the-shelf features extraction from pre-trained convolutional neural networks (CNNs) is one of the common deep transfer learning approaches. The architecture of deep CNNs has a significant role in the choice of the optimal learning transferable features to adopt for classifying the cancerous histopathological image. In this study, we have investigated three pre-trained CNNs on ImageNet dataset; ResNet-50, DenseNet-201 and ShuffleNet models for classifying the Breast Cancer Histopathology (BACH) Challenge 2018 dataset. The extracted deep features from these three models were utilised to train two machine learning classifiers; namely, the K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) to classify the breast cancer grades. Four grades of breast cancer were presented in the BACH challenge dataset; these grades namely normal tissue, benign tumour, in-situ carcinoma and invasive carcinoma. The performance of the target classifiers was evaluated. Our experimental results showed that the extracted off-the-shelf features from DenseNet-201 model provide the best predictive accuracy using both SVM and KNN classifiers. They yielded the image-wise classification accuracy of 93.75% and 88.75% for SVM and KNN classifiers, respectively. These results indicate the high robustness of our proposed framework. Breast cancer Deep transfer learning Machine learning classifier Histopathological image classification
3	The Perception of Lexical Similarities Between L2 English and L3 Swedish Utgof, Darja January 2008 (has links) <p>The present study investigates lexical similarity perceptions by students of Swedish as a foreign language (L3) with a good yet non-native proficiency in English (L2). The general theoretical framework is provided by studies in transfer of learning and its specific instance, transfer in language acquisition.</p><p>It is accepted as true that all previous linguistic knowledge is facilitative in developing proficiency in a new language. However, a frequently reported phenomenon is that students see similarities between two systems in a different way than linguists and theoreticians of education do. As a consequence, the full facilitative potential of transfer remains unused.</p><p>The present research seeks to shed light on the similarity perceptions with the focus on the comprehension of a written text. In order to elucidate students’ views, a form involving similarity judgements and multiple choice questions for formally similar items has been designed, drawing on real language use as provided by corpora. 123 forms have been distributed in 6 groups of international students, 4 of them studying Swedish at Level I and 2 studying at Level II. </p><p>The test items in the form vary in the degree of formal, semantic and functional similarity from very close cognates, to similar words belonging to different word classes, to items exhibiting category membership and/or being in subordinate/superordinate relation to each other, to deceptive cognates. The author proposes expected similarity ratings and compares them to the results obtained. The objective measure of formal similarity is provided by a string matching algorithm, Levenshtein distance.</p><p>The similarity judgements point at the fact that intermediate similarity values can be considered problematic. Similarity ratings between somewhat similar items are usually lower than could be expected. Besides, difference in grammatical meaning lowers similarity values significantly even if lexical meaning nearly coincides. Thus, the obtained results indicate that in order to utilize similarities to facilitate language learning, more attention should be paid to underlying similarities.</p> master master's programme language culture general linguistics linguistics foreign language acquisition similarity formal similarity semantic similarity functional similarity transfer transfer of learning language acquisition form form-based research crosslinguistic infuence one-year master english swedish lexical similarities false friends cognates deceptive cognates origin words of the same origin proto-germanic proto-indo-european competence performance comprehension corpus corpus-based data corpus-based research levenshtein distance objective similarity perceived similarity frequency prototype prototypicality proficiency context informed guess simchecker word recognition tachitoscopic experiments semantic correspondence similarity judgements eurocom surface transfer deep transfer lateral transfer English language Engelska språket Linguistic subjects Lingvistikämnen
4	The Perception of Lexical Similarities Between L2 English and L3 Swedish Utgof, Darja January 2008 (has links) The present study investigates lexical similarity perceptions by students of Swedish as a foreign language (L3) with a good yet non-native proficiency in English (L2). The general theoretical framework is provided by studies in transfer of learning and its specific instance, transfer in language acquisition. It is accepted as true that all previous linguistic knowledge is facilitative in developing proficiency in a new language. However, a frequently reported phenomenon is that students see similarities between two systems in a different way than linguists and theoreticians of education do. As a consequence, the full facilitative potential of transfer remains unused. The present research seeks to shed light on the similarity perceptions with the focus on the comprehension of a written text. In order to elucidate students’ views, a form involving similarity judgements and multiple choice questions for formally similar items has been designed, drawing on real language use as provided by corpora. 123 forms have been distributed in 6 groups of international students, 4 of them studying Swedish at Level I and 2 studying at Level II. The test items in the form vary in the degree of formal, semantic and functional similarity from very close cognates, to similar words belonging to different word classes, to items exhibiting category membership and/or being in subordinate/superordinate relation to each other, to deceptive cognates. The author proposes expected similarity ratings and compares them to the results obtained. The objective measure of formal similarity is provided by a string matching algorithm, Levenshtein distance. The similarity judgements point at the fact that intermediate similarity values can be considered problematic. Similarity ratings between somewhat similar items are usually lower than could be expected. Besides, difference in grammatical meaning lowers similarity values significantly even if lexical meaning nearly coincides. Thus, the obtained results indicate that in order to utilize similarities to facilitate language learning, more attention should be paid to underlying similarities. master master's programme language culture general linguistics linguistics foreign language acquisition similarity formal similarity semantic similarity functional similarity transfer transfer of learning language acquisition form form-based research crosslinguistic infuence one-year master english swedish lexical similarities false friends cognates deceptive cognates origin words of the same origin proto-germanic proto-indo-european competence performance comprehension corpus corpus-based data corpus-based research levenshtein distance objective similarity perceived similarity frequency prototype prototypicality proficiency context informed guess simchecker word recognition tachitoscopic experiments semantic correspondence similarity judgements eurocom surface transfer deep transfer lateral transfer Specific Languages Studier av enskilda språk General Language Studies and Linguistics

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