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Lee, 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 (2020) 0.02

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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.
Date: 23. 5.2020 16:19:47

Chen, H.-H.; Lin, W.-C.; Yang, C.; Lin, W.-H.: Translating-transliterating named entities for multilingual information access (2006) 0.00

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Date: 4. 6.2006 19:52:22
Source: Journal of the American Society for Information Science and Technology. 57(2006) no.5, S.645-659

Tsai, M.-.F.; Chen, H.-H.; Wang, Y.-T.: Learning a merge model for multilingual information retrieval (2011) 0.00
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Abstract

This paper proposes a learning approach for the merging process in multilingual information retrieval (MLIR). To conduct the learning approach, we present a number of features that may influence the MLIR merging process. These features are mainly extracted from three levels: query, document, and translation. After the feature extraction, we then use the FRank ranking algorithm to construct a merge model. To the best of our knowledge, this practice is the first attempt to use a learning-based ranking algorithm to construct a merge model for MLIR merging. In our experiments, three test collections for the task of crosslingual information retrieval (CLIR) in NTCIR3, 4, and 5 are employed to assess the performance of our proposed method. Moreover, several merging methods are also carried out for a comparison, including traditional merging methods, the 2-step merging strategy, and the merging method based on logistic regression. The experimental results show that our proposed method can significantly improve merging quality on two different types of datasets. In addition to the effectiveness, through the merge model generated by FRank, our method can further identify key factors that influence the merging process. This information might provide us more insight and understanding into MLIR merging.

Source

Information processing and management. 47(2011) no.5, S.635-646

Lee, L.-H.; Juan, Y.-C.; Tseng, W.-L.; Chen, H.-H.; Tseng, Y.-H.: Mining browsing behaviors for objectionable content filtering (2015) 0.00

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Source: Journal of the Association for Information Science and Technology. 66(2015) no.5, S.930-942

Bian, G.-W.; Chen, H.-H.: Cross-language information access to multilingual collections on the Internet (2000) 0.00

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Date: 16. 2.2000 14:22:39

Ku, L.-W.; Ho, H.-W.; Chen, H.-H.: Opinion mining and relationship discovery using CopeOpi opinion analysis system (2009) 0.00
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Abstract

We present CopeOpi, an opinion-analysis system, which extracts from the Web opinions about specific targets, summarizes the polarity and strength of these opinions, and tracks opinion variations over time. Objects that yield similar opinion tendencies over a certain time period may be correlated due to the latent causal events. CopeOpi discovers relationships among objects based on their opinion-tracking plots and collocations. Event bursts are detected from the tracking plots, and the strength of opinion relationships is determined by the coverage of these plots. To evaluate opinion mining, we use the NTCIR corpus annotated with opinion information at sentence and document levels. CopeOpi achieves sentence- and document-level f-measures of 62% and 74%. For relationship discovery, we collected 1.3M economics-related documents from 93 Web sources over 22 months, and analyzed collocation-based, opinion-based, and hybrid models. We consider as correlated company pairs that demonstrate similar stock-price variations, and selected these as the gold standard for evaluation. Results show that opinion-based and collocation-based models complement each other, and that integrated models perform the best. The top 25, 50, and 100 pairs discovered achieve precision rates of 1, 0.92, and 0.79, respectively.

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