首页> 外文OA文献 >Figure 2: Performances for the models based on the word vector with words of two to four letters long (shown in circle, triangle and star, respectively) derived from the influenza protein dataset.

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Figure 2: Performances for the models based on the word vector with words of two to four letters long (shown in circle, triangle and star, respectively) derived from the influenza protein dataset.

机译：图2：基于单词向量的模型的性能，单词为两到四个字母长（分别以圆形，三角形和星星）源自流感蛋白质集。

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1. Using Communities of Words Derived from Multilingual Word Vectors for Cross-Language Information Retrieval in Indian Languages [J] . Bhattacharya Paheli, Goyal Pawan, Sarkar Sudeshna ACM transactions on Asian language information processing . 2019,第1期

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2. Classification of finitely based words in a class of words over a -letter alphabet [J] . Li Jian-Rong, Luo Yan-Feng Semigroup Forum . 2015,第1期

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4. Automatic Labelling of Topic Models Using Word Vectors and Letter Trigram Vectors [C] . Wanqiu Kou, Fang Li, Timothy Baldwin Asia information retrieval societies conference . 2015

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5. Building a Corpus-Based Instructional Vocabulary Model: Interdisciplinary Academic Words in University Reading Support Courses [D] . Nelson, Timothy S. 2019

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6. EEG Correlates of Old/New Discrimination Performance Involving Abstract Figures and Non-Words [O] . Monika Toth, Anke Sambeth, Arjan Blokland 2021

机译：EEG相关的旧的/新的歧视性能涉及抽象数字和非单词
7. Figure 4: Comparison of overall accuracies for the models based on word vectors with words of two to four letters long (shown in circle, triangle and star, respectively) derived from the influenza DNA dataset (red line) and influenza protein dataset (blue line). [O] . -1

机译：图4：基于Word Vectors的模型的整体精度比较，单词载有两到四个字母长（分别以圆形，三角形和星，分别示出）来自流感DNA数据集（红线）和流感蛋白质数据集（蓝线）。

Figure 2: Performances for the models based on the word vector with words of two to four letters long (shown in circle, triangle and star, respectively) derived from the influenza protein dataset.

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