On the Relevance of Syntactic and Discourse Features for Author
            Profiling and Identification

Soler, Juan Cerezo; Wanner, Leo

doi:10.18653/v1/e17-2108

Cited by 19 publications

(12 citation statements)

References 15 publications

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“…Sundararajan et al argue that, although syntax can be helpful for cross-genre authorship attribution, combining syntax and lexical information can further boost the performance for cross-topic attribution and single-domain attribution [51]. Furthermore, it has been shown that syntactic dependency and discourse features play a significant role in the task of gender and author identification and author verification [47]. Schwartz et al combine lexical and syntactic features and use a linear classifier for writing style detection [42].…”

Section: Traditional Methodsmentioning

confidence: 99%

Unifying Lexical, Syntactic, and Structural Representations of Written Language for Authorship Attribution

Jafariakinabad

Hua

2021

SN COMPUT. SCI.

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Writing style in written language is a combination of consistent decisions associated with a specific author at different levels of language production, including lexical, syntactic, and structural. The recent work in neural network based style analysis mainly lacks the multi-level modeling of writing style. In this paper, we introduce a style-aware neural model to encode document information from three stylistic levels and evaluate it in the domain of authorship attribution. First, we propose a simple way to jointly encode syntactic and lexical representations of sentences. Subsequently, we employ an attention-based hierarchical neural network to encode the syntactic and semantic structure of sentences in documents while rewarding the sentences which contribute more in capturing the writing style. Our experimental results, based on four benchmark datasets, reveal the benefits of encoding document information from all three stylistic levels when compared to the baseline methods in the literature. Additionally, We adopt a transfer learning approach and use deep contextualized word representation (ELMo) in our model to measure the impact of lower level linguistic representations versus higher level linguistic representations of ELMo in the task of authorship attribution. According to our experimental results, lower level linguistic representations which mainly carry syntactic information demonstrate better performance in authorship attribution task when compared to higher level linguistic representations which mainly carry semantic information.

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Section: Traditional Methodsmentioning

confidence: 99%

Unifying Lexical, Syntactic, and Structural Representations of Written Language for Authorship Attribution

Jafariakinabad

Hua

2021

SN COMPUT. SCI.

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“…Punctuation is a core part of language that functions quite unlike words; punctuation groups words together or separates them, and contributes to the overall structure and meaning of a phrase or sentence. Punctuation plays an important role in dis- tinguishing between different types of text, such as texts by different authors (Soler-Company and Wanner, 2017) or texts produced by different Twitter communities (Tatman and Paullada, 2017). Embeddings are used to generate punctuation for text that is lacking punctuation, such as recorded transcripts (Yi and Tao, 2019).…”

Section: Punctuationmentioning

confidence: 99%

Using Paraphrases to Study Properties of Contextual Embeddings

Burdick¹,

Kummerfeld²,

Mihalcea³

2022

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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We use paraphrases as a unique source of data to analyze contextualized embeddings, with a particular focus on BERT. Because paraphrases naturally encode consistent word and phrase semantics, they provide a unique lens for investigating properties of embeddings. Using the Paraphrase Database's alignments, we study words within paraphrases as well as phrase representations. We find that contextual embeddings effectively handle polysemous words, but give synonyms surprisingly different representations in many cases. We confirm previous findings that BERT is sensitive to word order, but find slightly different patterns than prior work in terms of the level of contextualization across BERT's layers.

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Section: Related Workmentioning

confidence: 99%

A Self-supervised Representation Learning of Sentence Structure for Authorship Attribution

Jafariakinabad¹,

Hua²

2020

Preprint

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Syntactic structure of sentences in a document substantially informs about its authorial writing style. Sentence representation learning has been widely explored in recent years and it has been shown that it improves the generalization of different downstream tasks across many domains. Even though utilizing probing methods in several studies suggests that these learned contextual representations implicitly encode some amount of syntax, explicit syntactic information further improves the performance of deep neural models in the domain of authorship attribution. These observations have motivated us to investigate the explicit representation learning of syntactic structure of sentences. In this paper, we propose a self-supervised framework for learning structural representations of sentences. The self-supervised network contains two components; a lexical sub-network and a syntactic sub-network which take the sequence of words and their corresponding structural labels as the input, respectively. Due to the 𝑛-to-1 mapping of words to their structural labels, each word will be embedded into a vector representation which mainly carries structural information. We evaluate the learned structural representations of sentences using different probing tasks, and subsequently utilize them in the authorship attribution task. Our experimental results indicate that the structural embeddings significantly improve the classification tasks when concatenated with the existing pre-trained word embeddings.CCS Concepts: • Computing methodologies → Discourse, dialogue and pragmatics.

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On the Relevance of Syntactic and Discourse Features for Author Profiling and Identification

Cited by 19 publications

References 15 publications

Unifying Lexical, Syntactic, and Structural Representations of Written Language for Authorship Attribution

Unifying Lexical, Syntactic, and Structural Representations of Written Language for Authorship Attribution

Using Paraphrases to Study Properties of Contextual Embeddings

A Self-supervised Representation Learning of Sentence Structure for Authorship Attribution

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