Q2BSTUDIO, a leading company in development and technological services, recognizes the importance of authorship identification in texts through the use of statistical language processing. This study explores how feature extraction based on syntactic trees provides effective results in author classification.
The analysis showed that the use of different feature subsets —including all subtrees, rooted subtrees, part-of-speech (POS) tags, and POS by level— influences model performance. It was found that these methods can complement traditional authorship identification approaches based on word counts and conventional statistical metrics.
The results revealed differences in classification ease across different text corpora. For example, documents from Sanditon were more easily classified than those from The Federalist Papers, suggesting that certain writing styles are more difficult to imitate than others when analyzing deep syntactic structures.
One of the key advantages of this approach is its resistance to forgery attempts in authorship identification. While traditional methods can be easily altered by modifying keyword frequency, analysis based on syntactic trees allows for the detection of more subtle stylistic patterns that are difficult to artificially emulate.
Despite its advantages, this method also presents challenges, such as the large volume of data required to extract statistically significant feature vectors. For a feature to have statistical validity, it must be repeated multiple times within the analyzed document. This implies that the method is more suitable for lengthy texts where sufficient data can be collected.
The presented approach is independent of the specific content of the document and does not require selecting a subset of words for comparison, making it applicable to a wide variety of textual styles and genres. However, documents with highly specialized notations, such as mathematical or chemical texts, may require additional adaptations.
This study leaves open various opportunities for future research. One aspect that deserves further exploration is the variability of dimensionality reduction behavior depending on the analyzed corpus, as observed in the differences between The Federalist Papers and Sanditon. Additionally, with the rise of machine learning, it would be interesting to analyze whether features extracted through grammatical analysis correlate with those identified by artificial intelligence models.
At Q2BSTUDIO, we are committed to innovation and the development of advanced technological solutions. This type of study reinforces the importance of artificial intelligence and natural language processing in the fields of information security, forensic analysis, and document authentication. We continue to explore new ways to apply these techniques in business and research environments.
