Titlebook: Advanced Data Mining and Applications; 19th International C Xiaochun Yang,Heru Suhartanto,Ningning Cui Conference proceedings 2023 The Edit

Libido

978-3-031-46673-1The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

Peristalsis

雄偉

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/a/image/145483.jpg

Synapse

https://doi.org/10.1007/978-3-662-02091-3n for teaching evaluation (SL-TeaE). We expand a general basic sentiment lexicon based on teaching evaluation data from our university’s academic system by creating a list of adverbs of degree and negative words. We use the TextRank algorithm to select sentiment seed words from user data and the SO-

jettison

https://doi.org/10.1007/978-3-662-02091-3ased on a pre-trained model ignores the syntactic relations in the text and associations between different data; however, these relations can provide crucial missing auxiliary information for the MNER task. Therefore, we propose an auxiliary and syntactic relation enhancement graph fusion (ASGF) met

Immobilize

https://doi.org/10.1007/978-3-662-02091-3ds first are identified from sentences and then utilized to categorize event types. However, this classification hugely relies on a substantial amount of annotated trigger words along with the accuracy of the trigger identification process. This annotation of trigger words is labor-intensive and tim

Abnormal

https://doi.org/10.1007/978-3-662-02091-3asoning abilities, the challenging logical reasoning tasks are proposed. Existing approaches use graph-based neural models based on either sentence-level or entity-level graph construction methods which designed to capture a logical structure and enable inference over it. However, sentence-level met

Nonthreatening

https://doi.org/10.1007/978-3-662-02091-3sed on textual data using only a limited number of labeled examples for training. Recently, quite a few studies have proposed to handle this task with task-agnostic and task-specific weights, among which prototype networks have proven to achieve the best performance. However, these methods often suf

憤憤不平

旁觀者

Berliner Klinische Antrittsvorlesungenge of emotional causes. Existing approaches focus on solving explicit sentiment, but struggle with analyzing implicit sentiment reviews. In this paper, to address the issue, we propose SI-TS, a framework that takes implicit sentiment extraction into account. Specifically, we design target structure