Towards Identifying Objectivity in Short Informal Text
Short informal texts are increasingly prevalent in modern communication, often containing fragmented grammar, personal opinions, and limited context. Traditional NLP tasks for the texts ordinarily focus on the subjective aspect learning, such as sentiment analysis and polarity classification. The st...
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MDPI AG
2025-05-01
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| author | Chaowei Zhang Cheng Zhao Zewei Zhang Yuchao Huang |
| author_facet | Chaowei Zhang Cheng Zhao Zewei Zhang Yuchao Huang |
| author_sort | Chaowei Zhang |
| collection | DOAJ |
| description | Short informal texts are increasingly prevalent in modern communication, often containing fragmented grammar, personal opinions, and limited context. Traditional NLP tasks for the texts ordinarily focus on the subjective aspect learning, such as sentiment analysis and polarity classification. The study of learning objectivity from the texts is similarly significant, which can benefit many real-world applications including information filtering, content verification, etc. Unfortunately, this study is not being explored. This paper proposes a novel task that aims at identifying objectivity in short informal texts. Inspired by the characteristics of objective statements that normally need complete syntax structures for knowledge expression and delivery, we try to leverage the viewpoint of subjects (U), the tense of predicates (V), and the viewpoint of objects (O) as critical factors for objectivity learning. Upon that, we further propose a two-stage objectivity identification approach: (1) a UVO quantification module is implemented via a proposed OpenIE and large language model (LLM)-based triple feature quantification procedure; (2) an objectivity identification module employs pre-trained base models like BERT or RoBERTa that are constrained with the quantified UVO. The experimental result demonstrates our approach can outperform the base models up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>5.91</mn><mo>%</mo></mrow></semantics></math></inline-formula> in objective-F1 and up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>6.97</mn><mo>%</mo></mrow></semantics></math></inline-formula> in accuracy. |
| format | Article |
| id | doaj-art-c432b8ba933f45d8a3a8b35159a523cd |
| institution | Kabale University |
| issn | 1099-4300 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Entropy |
| spelling | doaj-art-c432b8ba933f45d8a3a8b35159a523cd2025-08-20T03:27:18ZengMDPI AGEntropy1099-43002025-05-0127658310.3390/e27060583Towards Identifying Objectivity in Short Informal TextChaowei Zhang0Cheng Zhao1Zewei Zhang2Yuchao Huang3The Department of Information Engineering, Yangzhou University, Yangzhou 225012, ChinaThe Department of Information Engineering, Yangzhou University, Yangzhou 225012, ChinaThe Department of Computer Science and Software Engineering, Auburn University, Auburn, AL 36849, USAThe Future Design Laboratory of Zhejiang University, Hangzhou 310027, ChinaShort informal texts are increasingly prevalent in modern communication, often containing fragmented grammar, personal opinions, and limited context. Traditional NLP tasks for the texts ordinarily focus on the subjective aspect learning, such as sentiment analysis and polarity classification. The study of learning objectivity from the texts is similarly significant, which can benefit many real-world applications including information filtering, content verification, etc. Unfortunately, this study is not being explored. This paper proposes a novel task that aims at identifying objectivity in short informal texts. Inspired by the characteristics of objective statements that normally need complete syntax structures for knowledge expression and delivery, we try to leverage the viewpoint of subjects (U), the tense of predicates (V), and the viewpoint of objects (O) as critical factors for objectivity learning. Upon that, we further propose a two-stage objectivity identification approach: (1) a UVO quantification module is implemented via a proposed OpenIE and large language model (LLM)-based triple feature quantification procedure; (2) an objectivity identification module employs pre-trained base models like BERT or RoBERTa that are constrained with the quantified UVO. The experimental result demonstrates our approach can outperform the base models up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>5.91</mn><mo>%</mo></mrow></semantics></math></inline-formula> in objective-F1 and up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>6.97</mn><mo>%</mo></mrow></semantics></math></inline-formula> in accuracy.https://www.mdpi.com/1099-4300/27/6/583short informal textsobjectivity learningrelational tripleslarge language models |
| spellingShingle | Chaowei Zhang Cheng Zhao Zewei Zhang Yuchao Huang Towards Identifying Objectivity in Short Informal Text Entropy short informal texts objectivity learning relational triples large language models |
| title | Towards Identifying Objectivity in Short Informal Text |
| title_full | Towards Identifying Objectivity in Short Informal Text |
| title_fullStr | Towards Identifying Objectivity in Short Informal Text |
| title_full_unstemmed | Towards Identifying Objectivity in Short Informal Text |
| title_short | Towards Identifying Objectivity in Short Informal Text |
| title_sort | towards identifying objectivity in short informal text |
| topic | short informal texts objectivity learning relational triples large language models |
| url | https://www.mdpi.com/1099-4300/27/6/583 |
| work_keys_str_mv | AT chaoweizhang towardsidentifyingobjectivityinshortinformaltext AT chengzhao towardsidentifyingobjectivityinshortinformaltext AT zeweizhang towardsidentifyingobjectivityinshortinformaltext AT yuchaohuang towardsidentifyingobjectivityinshortinformaltext |