What represents “style” in authorship attribution?
What represents “style” in authorship attribution?
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发表时间：
2018-08
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通讯作者：
Kalaivani Sundararajan;D. Woodard
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作者：
Kalaivani Sundararajan;D. Woodard
作者归属通常使用代表内容和风格的所有信息，而仅基于风格方面的归属在跨域设置中可能是鲁棒的。本文分析了不同的语言方面，可能有助于代表风格。具体来说，我们研究的作用，句法和词汇（名词，动词，形容词和副词）在代表风格。我们使用一个纯句法语言模型来研究句子结构在单域和跨域归因（即跨主题和跨体裁归因）中的意义。我们发现，句法可能有助于跨体裁归因，而跨主题归因和单域可能受益于额外的词汇信息。此外，纯语法模型本身可能并不有效，需要与其他鲁棒模型结合使用。为了研究词语选择的作用，我们通过掩蔽所有与名词、动词、形容词和副词相对应的词或特定主题词来进行归因。使用单域数据集，IMDB1M评论，我们证明了普通名词和专有名词在归因中的重大影响，从而突出主题干扰。使用跨域Guardian10数据集，我们表明，一些常见的名词，动词，形容词和副词可能有助于风格归因，如掩蔽与这些词性对应的主题词所示。正如预期的那样，观察到专有名词受内容的影响很大，跨域归因将受益于完全掩盖它们。
Authorship attribution typically uses all information representing both content and style whereas attribution based only on stylistic aspects may be robust in cross-domain settings. This paper analyzes different linguistic aspects that may help represent style. Specifically, we study the role of syntax and lexical words (nouns, verbs, adjectives and adverbs) in representing style. We use a purely syntactic language model to study the significance of sentence structures in both single-domain and cross-domain attribution, i.e. cross-topic and cross-genre attribution. We show that syntax may be helpful for cross-genre attribution while cross-topic attribution and single-domain may benefit from additional lexical information. Further, pure syntactic models may not be effective by themselves and need to be used in combination with other robust models. To study the role of word choice, we perform attribution by masking all words or specific topic words corresponding to nouns, verbs, adjectives and adverbs. Using a single-domain dataset, IMDB1M reviews, we demonstrate the heavy influence of common nouns and proper nouns in attribution, thereby highlighting topic interference. Using cross-domain Guardian10 dataset, we show that some common nouns, verbs, adjectives and adverbs may help with stylometric attribution as demonstrated by masking topic words corresponding to these parts-of-speech. As expected, it was observed that proper nouns are heavily influenced by content and cross-domain attribution will benefit from completely masking them.