Diese Datenbank enthält über 40.000 Dokumente zu Themen aus den Bereichen Formalerschließung – Inhaltserschließung – Information Retrieval.
© 2015 W. Gödert, TH Köln, Institut für Informationswissenschaft / Powered by litecat, BIS Oldenburg (Stand: 04. Juni 2021)
1Lee, Y.-Y. ; Ke, H. ; Yen, T.-Y. ; Huang, H.-H. ; Chen, H.-H.: Combining and learning word embedding with WordNet for semantic relatedness and similarity measurement.
In: Journal of the Association for Information Science and Technology. 71(2020) no.6, S.657-670.
Abstract: In this research, we propose 3 different approaches to measure the semantic relatedness between 2 words: (i) boost the performance of GloVe word embedding model via removing or transforming abnormal dimensions; (ii) linearly combine the information extracted from WordNet and word embeddings; and (iii) utilize word embedding and 12 linguistic information extracted from WordNet as features for Support Vector Regression. We conducted our experiments on 8 benchmark data sets, and computed Spearman correlations between the outputs of our methods and the ground truth. We report our results together with 3 state-of-the-art approaches. The experimental results show that our method can outperform state-of-the-art approaches in all the selected English benchmark data sets.
Themenfeld: Semantisches Umfeld in Indexierung u. Retrieval
2Huang, H.-H. ; Wang, J.-J. ; Chen, H.-H.: Implicit opinion analysis : extraction and polarity labelling.
In: Journal of the Association for Information Science and Technology. 68(2017) no.9, S.2076-2087.
Abstract: Opinion words are crucial information for sentiment analysis. In some text, however, opinion words are absent or highly ambiguous. The resulting implicit opinions are more difficult to extract and label than explicit ones. In this paper, cutting-edge machine-learning approaches - deep neural network and word-embedding - are adopted for implicit opinion mining at the snippet and clause levels. Hotel reviews written in Chinese are collected and annotated as the experimental data set. Results show the convolutional neural network models not only outperform traditional support vector machine models, but also capture hidden knowledge within the raw text. The strength of word-embedding is also analyzed.
Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23835/full.