Putting Context in Context: the Impact of Discussion Structure on Text Classification (2402.02975v1)

Abstract: Current text classification approaches usually focus on the content to be classified. Contextual aspects (both linguistic and extra-linguistic) are usually neglected, even in tasks based on online discussions. Still in many cases the multi-party and multi-turn nature of the context from which these elements are selected can be fruitfully exploited. In this work, we propose a series of experiments on a large dataset for stance detection in English, in which we evaluate the contribution of different types of contextual information, i.e. linguistic, structural and temporal, by feeding them as natural language input into a transformer-based model. We also experiment with different amounts of training data and analyse the topology of local discussion networks in a privacy-compliant way. Results show that structural information can be highly beneficial to text classification but only under certain circumstances (e.g. depending on the amount of training data and on discussion chain complexity). Indeed, we show that contextual information on smaller datasets from other classification tasks does not yield significant improvements. Our framework, based on local discussion networks, allows the integration of structural information, while minimising user profiling, thus preserving their privacy.

• The paper demonstrates that integrating discussion structure significantly enhances model performance in text classification on large-scale datasets.
• Experiments reveal that structural cues outperform temporal features, highlighting the value of participant interaction patterns.
• The study maintains ethical standards by using local discussion IDs to safeguard privacy while delivering robust classification insights.

Impact of Discussion Structure on Text Classification

Introduction to Contextual Information in Classification

Text classification is a fundamental task in NLP, with applications like sentiment analysis and stance detection. While existing models typically focus on textual content, integrating discussion context—a mix of linguistic and extra-linguistic elements—can provide additional insight. However, until recently, the multi-party and multi-turn nature of conversations and their structural elements have been largely overlooked in classification models.

Evaluating Context Integration in Classification Frameworks

Researchers have conducted experiments using a large dataset for stance detection to gauge the effectiveness of incorporating different types of context, such as linguistic, structural, and temporal, into transformer-based models. The paper also explored varying training data volumes and analyzed local discussion networks to examine the influence of structural information on classification results.

Key Experimental Results

The findings indicate that structural context can significantly augment text classification. Nevertheless, this advantage manifests under specific conditions, such as when dealing with sophisticated datasets. Structural information did not show marked improvements in performance for smaller datasets from other classification tasks, emphasizing the importance of dataset size in leveraging contextual features. This supports the premise that the utility of contextual information is closely tied to data volume.

Context's Complex Role in Classification Effectiveness

The experiments affirmed that context could indeed enhance model performance. Yet, two crucial takeaways emerged:

1. Dataset Dependency: Substantial gains were observed on a large dataset used for stance detection. Here, leveraging the discussion's intricate structure provided a clear benefit. Conversely, smaller datasets saw no significant improvement, highlighting a threshold of data necessity for context to play a transformative role.
2. Structural Over Temporal: Structural context outperformed temporal context in terms of improving classification results. The research suggests that understanding the interactions between participants within a discussion chain is more valuable than the timing of the comments.

Privacy Considerations and Methodological Robustness

One notable aspect of the research was the commitment to privacy. By using local discussion IDs instead of global user identifiers, the risk of user profiling across multiple discussions was eliminated, preserving individual privacy. Additionally, this approach ensured that the improvements in classification were not just achieved through a potentially ethically dubious exploitation of user data.

Implications and Future Directions

The paper's implications for the design of NLP systems are vast. It implies that for optimal text classification, especially on platforms with structured discussions, integrating contextual information is key. However, understanding the mechanics of discussion structure and the limitations imposed by dataset size is crucial for effectively implementing these insights.

Overall, the paper forms a convincing argument for the inclusion of contextual information in classification tasks. As NLP moves forward, such considerations will undeniably play an increasingly significant role in model development and performance optimization.