Temporal Text Classification

• Temporal text classification is a supervised task that assigns discrete time intervals to documents based on lexical, syntactic, and semantic cues.
• It encompasses task variants like absolute dating, relative ordering, and interval classification, leveraging tree-based and neural models to manage vocabulary evolution and temporal drift.
• Explainability frameworks such as SHAP provide interpretable feature attributions, ensuring model transparency and robustness across different time slices.

Temporal text classification refers to the supervised learning problem of assigning discrete time labels or intervals to natural language documents based on their lexical, syntactic, and semantic content. The task emerges in settings where explicit timestamps may be absent, corrupted, or unreliable, requiring automated inference of the likely creation or reference time. Applications span authorship profiling, document dating, longitudinal text analysis, and historical event extraction. Temporal text classification challenges standard text classification pipelines with implicit temporal drift, vocabulary evolution, and the need for explanations that can capture longitudinal language shifts.

1. Formal Problem Definition and Task Variants

In temporal text classification, the objective is to train a function $h: \mathcal{D} \to \mathcal{T}$ mapping a document $d\in\mathcal{D}$ to a label $t^*\in\mathcal{T}$, where $\mathcal{T}$ is a set of discrete chronons (years, decades, periods) or ordered time bins. Given a collection $\{(d_i, t_i)\}_{i=1}^N$, models are trained to predict $t^*$ for unseen documents with either no timestamp or hidden ground truth.

Primary task variants include:

• Absolute dating: Predicting exact publication year/decade for each document.
• Relative dating (ordering): Predicting temporal order among document pairs.
• Interval classification: Predicting which predefined interval, e.g., pre/post event, a document belongs to.

Temporal text classification differs from timestamp regression (which outputs a real-valued estimate) by treating time as a categorical variable prone to coarse quantization.

2. Lexical and Linguistic Feature Engineering

Temporal drift in natural language manifests as systematic changes in vocabulary, genre conventions, syntax, collocations, and entity usage. Feature engineering for temporal text classification targets extraction of temporally discriminative linguistic markers:

• Lexical n-grams: Surface word n-grams are often highly time-sensitive, capturing ephemeral vocabulary.
• Syntactic and morphological patterns: POS sequences and morphological variants can signal stylistic change.
• Topic models: Temporal variation in topic prevalence reveals macro-level trends.
• Domain-specific features: Named entity distributions, event mentions, and topical phrases can serve as temporal proxies.

Effective temporal feature engineering must address concerns of:

• Overfitting to idiosyncratic tokens,
• Robustness to genre and domain,
• Generalizability across time ranges.

3. Temporal Classification Algorithms and Model Selection

Standard algorithms for text classification—logistic regression, support vector machines (SVM), random forest, and neural architectures—can be adopted for temporal classification provided input features sufficiently capture time-sensitive variance. Recent work demonstrates the efficacy of LLM-generated pipelines in constructing and evaluating temporal models, including tree ensembles (Random Forest, XGBoost) and neural nets (MLP, LSTM) (Vassiliades et al., 8 Oct 2025).

• Tree-based models leverage hierarchical splits to partition on lexical indicators, providing interpretable path-based feature attributions.
• Neural models (MLP, LSTM) are capable of modeling non-linear interactions and sequential dependencies, useful for capturing complex temporal drift.
• LLMs for pipeline synthesis: Prompted LLMs can propose full end-to-end classification pipelines, including preprocessing, model selection, and evaluation stages. Their generated code, when executed, is empirically close in accuracy and explainability to manual baselines (Vassiliades et al., 8 Oct 2025).

4. Explainability: SHAP Metrics and Temporal Interpretability

A central challenge in temporal text classification is ensuring that models do not rely on spurious patterns or overfit ephemeral features, especially when evaluating on out-of-time slices. Explainability frameworks such as SHAP (SHapley Additive exPlanations) provide rigorous, additive decompositions of model predictions into per-feature attributions:

• SHAP Fidelity (Mean Squared Error) quantifies the discrepancy between the model output $f(x)$ and its SHAP surrogate $g(x)$ over the test set:

$$\text{Fidelity} = \frac{1}{|D|} \sum_{x\in D} (f(x) - g(x))^2$$

Perfect fidelity ($0$ MSE) demonstrates the additive explanation reconstructs the model output faithfully.

• SHAP Sparsity reports the mean number of features with non-negligible attribution above a threshold $\tau$:

$$\text{Sparsity} = \frac{1}{|D|} \sum_{x\in D} \left| \{i : |\phi_i(x)| > \tau\} \right|$$

Lower sparsity indicates more human-readable explanations, as fewer features are needed for each decision.

Empirical studies confirm that across binary and multilabel tasks, tree models and neural nets exhibit high-fidelity SHAP explanations, with neural networks occasionally concentrating attributions into more concise feature sets (Vassiliades et al., 8 Oct 2025).

5. Experimental Protocols and Benchmarking Metrics

Assessment of temporal text classification models incorporates both standard and explainability-centric metrics:

• Predictive performance: Accuracy, precision, recall, and F1-score evaluated on temporally held-out test splits.
• Interpretability metrics: Average SHAP fidelity and sparsity as described above (Vassiliades et al., 8 Oct 2025).
• Cross-model robustness: Consistency of SHAP feature attributions across different model classes and across LLM-generated vs manually implemented pipelines.
• Temporal generalization: Ability of the model to generalize to future or past time slices not seen during training, a critical evaluation in temporal settings.

Tables summarizing SHAP metrics for models across binary and multilabel tasks illustrate uniform high-fidelity and consistent sparsity, with deviations primarily in recurrent architectures indicative of non-additive sequential patterns (Vassiliades et al., 8 Oct 2025).

6. Implications, Limitations, and Future Directions

Empirical evidence demonstrates that temporal text classification pipelines generated by LLMs can simultaneously deliver high predictive accuracy and interpretable SHAP explanations, allowing both automated and human-auditable temporal modeling (Vassiliades et al., 8 Oct 2025). Key implications include:

• Trustworthy deployment: High SHAP fidelity and controlled sparsity are indicators that explanations are both faithful and concise, enhancing transparency essential for historical and archival text applications.
• Model class differences: Slight variations in SHAP sparsity across model types suggest that neural networks may inherently yield more concentrated, interpretable attributions in multilabel settings.
• Architectural subtleties: Sequential models (LSTM) occasionally exhibit small non-additive effects, visible as nonzero SHAP fidelity error, highlighting limitations in additive decomposition for such architectures.
• LLM prompt and model selection: Attribution patterns can be subtly affected by the choice of LLM and prompt structure; ongoing validation is necessary as LLM-driven automation becomes mainstream.

Future research should focus on (a) robustly quantifying temporal generalization, (b) integrating temporal feature selection based on SHAP into the model pipeline, and (c) extending explainability metrics to explicitly capture model stability across evolving language distributions.

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References:

• "Utilizing LLMs for Machine Learning Explainability" (Vassiliades et al., 8 Oct 2025)