Search (9 results, page 1 of 1)

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) 0.01
```
0.010973599 = product of:
  0.043894395 = sum of:
    0.043894395 = weight(_text_:data in 5871) [ClassicSimilarity], result of:
      0.043894395 = score(doc=5871,freq=4.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.29644224 = fieldWeight in 5871, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046875 = fieldNorm(doc=5871)
  0.25 = coord(1/4)
```
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.
Ku, L.-W.; Chen, H.-H.: Mining opinions from the Web : beyond relevance retrieval (2007) 0.01
```
0.009144665 = product of:
  0.03657866 = sum of:
    0.03657866 = weight(_text_:data in 605) [ClassicSimilarity], result of:
      0.03657866 = score(doc=605,freq=4.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.24703519 = fieldWeight in 605, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=605)
  0.25 = coord(1/4)
```
Abstract

Documents discussing public affairs, common themes, interesting products, and so on, are reported and distributed on the Web. Positive and negative opinions embedded in documents are useful references and feedbacks for governments to improve their services, for companies to market their products, and for customers to purchase their objects. Web opinion mining aims to extract, summarize, and track various aspects of subjective information on the Web. Mining subjective information enables traditional information retrieval (IR) systems to retrieve more data from human viewpoints and provide information with finer granularity. Opinion extraction identifies opinion holders, extracts the relevant opinion sentences, and decides their polarities. Opinion summarization recognizes the major events embedded in documents and summarizes the supportive and the nonsupportive evidence. Opinion tracking captures subjective information from various genres and monitors the developments of opinions from spatial and temporal dimensions. To demonstrate and evaluate the proposed opinion mining algorithms, news and bloggers' articles are adopted. Documents in the evaluation corpora are tagged in different granularities from words, sentences to documents. In the experiments, positive and negative sentiment words and their weights are mined on the basis of Chinese word structures. The f-measure is 73.18% and 63.75% for verbs and nouns, respectively. Utilizing the sentiment words mined together with topical words, we achieve f-measure 62.16% at the sentence level and 74.37% at the document level.

Theme

Data Mining
Huang, H.-H.; Wang, J.-J.; Chen, H.-H.: Implicit opinion analysis : extraction and polarity labelling (2017) 0.01
```
0.009052756 = product of:
  0.036211025 = sum of:
    0.036211025 = weight(_text_:data in 3820) [ClassicSimilarity], result of:
      0.036211025 = score(doc=3820,freq=2.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.24455236 = fieldWeight in 3820, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0546875 = fieldNorm(doc=3820)
  0.25 = coord(1/4)
```
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.
Lee, L.-H.; Juan, Y.-C.; Tseng, W.-L.; Chen, H.-H.; Tseng, Y.-H.: Mining browsing behaviors for objectionable content filtering (2015) 0.01
```
0.006466255 = product of:
  0.02586502 = sum of:
    0.02586502 = weight(_text_:data in 1818) [ClassicSimilarity], result of:
      0.02586502 = score(doc=1818,freq=2.0), product of:
        0.14807065 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046827413 = queryNorm
        0.17468026 = fieldWeight in 1818, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1818)
  0.25 = coord(1/4)
```
Abstract

This article explores users' browsing intents to predict the category of a user's next access during web surfing and applies the results to filter objectionable content, such as pornography, gambling, violence, and drugs. Users' access trails in terms of category sequences in click-through data are employed to mine users' web browsing behaviors. Contextual relationships of URL categories are learned by the hidden Markov model. The top-level domains (TLDs) extracted from URLs themselves and the corresponding categories are caught by the TLD model. Given a URL to be predicted, its TLD and current context are empirically combined in an aggregation model. In addition to the uses of the current context, the predictions of the URL accessed previously in different contexts by various users are also considered by majority rule to improve the aggregation model. Large-scale experiments show that the advanced aggregation approach achieves promising performance while maintaining an acceptably low false positive rate. Different strategies are introduced to integrate the model with the blacklist it generates for filtering objectionable web pages without analyzing their content. In practice, this is complementary to the existing content analysis from users' behavioral perspectives.

Lin, W.-C.; Chang, Y.-C.; Chen, H.-H.: Integrating textual and visual information for cross-language image retrieval : a trans-media dictionary approach (2007) 0.01

0.0063588563 = product of:
  0.025435425 = sum of:
    0.025435425 = product of:
      0.05087085 = sum of:
        0.05087085 = weight(_text_:processing in 904) [ClassicSimilarity], result of:
          0.05087085 = score(doc=904,freq=2.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.26835677 = fieldWeight in 904, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046875 = fieldNorm(doc=904)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Source: Information processing and management. 43(2007) no.2, S.488-502

Chen, H.-H.; Lin, W.-C.; Yang, C.; Lin, W.-H.: Translating-transliterating named entities for multilingual information access (2006) 0.01

0.0055514094 = product of:
  0.022205638 = sum of:
    0.022205638 = product of:
      0.044411276 = sum of:
        0.044411276 = weight(_text_:22 in 1080) [ClassicSimilarity], result of:
          0.044411276 = score(doc=1080,freq=2.0), product of:
            0.16398162 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046827413 = queryNorm
            0.2708308 = fieldWeight in 1080, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1080)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date: 4. 6.2006 19:52:22

Tsai, M.-.F.; Chen, H.-H.; Wang, Y.-T.: Learning a merge model for multilingual information retrieval (2011) 0.01

0.005299047 = product of:
  0.021196188 = sum of:
    0.021196188 = product of:
      0.042392377 = sum of:
        0.042392377 = weight(_text_:processing in 2750) [ClassicSimilarity], result of:
          0.042392377 = score(doc=2750,freq=2.0), product of:
            0.18956426 = queryWeight, product of:
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.046827413 = queryNorm
            0.22363065 = fieldWeight in 2750, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.048147 = idf(docFreq=2097, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2750)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

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

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

0.0047583506 = product of:
  0.019033402 = sum of:
    0.019033402 = product of:
      0.038066804 = sum of:
        0.038066804 = weight(_text_:22 in 4436) [ClassicSimilarity], result of:
          0.038066804 = score(doc=4436,freq=2.0), product of:
            0.16398162 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046827413 = queryNorm
            0.23214069 = fieldWeight in 4436, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=4436)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

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
```
0.0039652926 = product of:
  0.01586117 = sum of:
    0.01586117 = product of:
      0.03172234 = sum of:
        0.03172234 = weight(_text_:22 in 2938) [ClassicSimilarity], result of:
          0.03172234 = score(doc=2938,freq=2.0), product of:
            0.16398162 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046827413 = queryNorm
            0.19345059 = fieldWeight in 2938, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2938)
      0.5 = coord(1/2)
  0.25 = coord(1/4)
```
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.

Search (9 results, page 1 of 1)

Authors

Years

Themes