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However, researchers recognized several challenges in developing fixed sets of rules for expressions respectably. Much of the challenges in rule development stems from the nature of textual information. Six challenges have been recognized by several researchers: 1) metaphorical expressions, 2) discrepancies in writings, 3) context-sensitive, 4) represented words with fewer usages, 5) time-sensitive, and 6) ever-growing volume.

However, researchers recognized several challenges in developing fixed sets of rules for expressions respectably. Much of the challenges in rule development stems from the nature of textual information. Six challenges have been recognized by several researchers: 1) metaphorical expressions, 2) discrepancies in writings, 3) context-sensitive, 4) represented words with fewer usages, 5) time-sensitive, and 6) ever-growing volume.

然而，研究人员认识到在为表达分类制定一套固定的规则集方面存在一些挑战。规则开发中的大部分挑战源于文本信息的性质。一些研究人员已经认识到了六个挑战： 1)隐喻性的表达，2)写作中的差异，3)上下文敏感性，4)时间敏感性，5)代表性词用法较少以及6)不断增长的数量。

然而，研究人员认识到在为表达方式分类制定一套固定的规则集方面存在一些挑战。规则开发中的大部分挑战源于文本信息的性质。一些研究人员已经认识到了六个挑战： 1)隐喻性的表达，2)写作中的差异，3)上下文敏感性，4)时间敏感性，5)代表性词用法较少以及6)不断增长的数量。

# Metaphorical expressions. The text contains metaphoric expression may impact on the performance on the extraction.<ref name=":13">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=July 2011|title=Finding Mutual Benefit between Subjectivity Analysis and Information Extraction|url=https://ieeexplore.ieee.org/document/5959154|journal=IEEE Transactions on Affective Computing|volume=2|issue=4|pages=175–191|doi=10.1109/T-AFFC.2011.19|s2cid=16820846|issn=1949-3045}}</ref> Besides, metaphors take in different forms, which may have been contributed to the increase in detection.

# Metaphorical expressions. The text contains metaphoric expression may impact on the performance on the extraction.<ref name=":13">{{Cite journal|last1=Wiebe|first1=Janyce|last2=Riloff|first2=Ellen|date=July 2011|title=Finding Mutual Benefit between Subjectivity Analysis and Information Extraction|url=https://ieeexplore.ieee.org/document/5959154|journal=IEEE Transactions on Affective Computing|volume=2|issue=4|pages=175–191|doi=10.1109/T-AFFC.2011.19|s2cid=16820846|issn=1949-3045}}</ref> Besides, metaphors take in different forms, which may have been contributed to the increase in detection.

Previously, the research mainly focused on document level classification. However, classifying a document level suffers less accuracy, as an article may have diverse types of expressions involved. Researching evidence suggests a set of news articles that are expected to dominate by the objective expression, whereas the results show that it consisted of over 40% of subjective expression.<ref name="Wiebe 2005 486–497"/>

Previously, the research mainly focused on document level classification. However, classifying a document level suffers less accuracy, as an article may have diverse types of expressions involved. Researching evidence suggests a set of news articles that are expected to dominate by the objective expression, whereas the results show that it consisted of over 40% of subjective expression.<ref name="Wiebe 2005 486–497"/>

以往的研究主要集中在文档级别的分类上。然而，文档级别的分类准确性较低，因为一篇文章可能涉及不同类型的表达方式。研究证据表明，一组新闻文章被期望以客观表达为主，而研究结果表明，这组新闻文章占主观表达的40% 以上。

现有的研究主要集中于篇章级的分类。然而，篇章级分类的准确性常常较低。这是因为一篇文章可能涉及不同类型的表达方式。研究数据表明，一组预计以客观表达为主的新闻文章的分类结果显示，这组新闻文章的主观表达占40% 以上。

To overcome those challenges, researchers conclude that classifier efficacy depends on the precisions of patterns learner. And the learner feeds with large volumes of annotated training data outperformed those trained on less comprehensive subjective features. However, one of the main obstacles to executing this type of work is to generate a big dataset of annotated sentences manually.&nbsp;The manual annotation method has been less favored than automatic learning for three reasons:

To overcome those challenges, researchers conclude that classifier efficacy depends on the precisions of patterns learner. And the learner feeds with large volumes of annotated training data outperformed those trained on less comprehensive subjective features. However, one of the main obstacles to executing this type of work is to generate a big dataset of annotated sentences manually.&nbsp;The manual annotation method has been less favored than automatic learning for three reasons:

为了克服这些挑战，研究人员得出结论，分类效能取决于模式学习者的精确度。而且，带有大量注释的训练数据的学习者饲料表现优于那些不太全面的主观特征的训练者。然而，执行此类工作的主要障碍之一是手动生成大量带注释的句子数据集。手动注释方法不如自动学习方法受欢迎，原因有三:

为了克服这些挑战，研究人员总结认为，分类效力取决于模式学习者的精确度。而用大量的注释数据训练的学习者比那些用不太全面的主观特征训练的学习者表现得更好而且。然而，执行此类工作的主要障碍之一是需要手动生成一个大体量的带注释的句子数据集。与自动学习相比，手动注释的方法不那么受欢迎，原因主要有三个:

# Variations in comprehensions. In the manual annotation task, disagreement of whether one instance is subjective or objective may occur among annotators because of languages' ambiguity.  

# Variations in comprehensions. In the manual annotation task, disagreement of whether one instance is subjective or objective may occur among annotators because of languages' ambiguity.