Explicit Linguistic Feature Sets

• Explicit linguistic feature sets are precisely defined collections of linguistic properties used to systematically represent and quantify language for comparative analysis and model evaluation.
• They combine handcrafted, task-specific, and neural-derived features to ensure interpretability, transparent provenance, and scalable extraction across diverse NLP tasks.
• Applications include diagnostic classification, sentiment analysis, machine translation, and typological assessments, demonstrating significant empirical improvements and domain versatility.

An explicit linguistic feature set is a precisely defined collection of linguistic properties, either observed in data or constructed as annotation targets, that enables systematic representation, quantification, and computational modeling of language. These sets serve as interpretable, human-inspectable variables for comparative analysis, diagnostic probing, feature-based learning, and evaluation in NLP, typology, and cognitive modeling. Modern research employs explicit feature sets ranging from shallow surface statistics and handcrafted indicators, to deep typological dimensions, to semantically or morphologically structured tags, and even interpretable encodings extracted directly from neural networks or LLMs.

1. Formal Composition and Categorization of Explicit Feature Sets

Explicit linguistic feature sets are built to capture distinct aspects of language through well-specified, operationalized features. Their design is grounded in either linguistic theory (e.g., phonology, morphology, syntax, semantics, pragmatics) or data-driven annotation frameworks. Representative instantiations include:

• Handcrafted stylistic and structural features: As implemented in the LFTK toolkit, over 220 features are grouped into four major branches—Surface (e.g., word/sentence/character counts, syllable statistics), Lexico-semantic (e.g., type-token ratios, age-of-acquisition aggregates), Syntax (POS distributions, dependency relations), and Discourse (named entity densities, readability formulas) (Lee et al., 2023).
• Task-specific compact sets: For clinical assessment, 15-dimensional sets include visual-augmented LLM-based topic hit rates, BLEU/METEOR content coverage, TF–IDF similarity/keyword hit rates, and classical syntactic/fluency metrics (Li et al., 28 Nov 2024).
• Deep neural model-derived features: Sparse auto-encoders can induce bases corresponding to phonetic, morphological, syntactic, semantic, and pragmatic distinctions, such as sibilant/vowel detection, past tense inflection, possession, causality, adversativity, discourse markers, and politeness (Jing et al., 27 Feb 2025).
• Typological and data-driven features: By leveraging resources like WALS and URIEL, explicit feature sets encode properties such as word order, morphological synthesis, case marking, or mean word length as high-dimensional binary or numeric vectors (Samardzic et al., 6 Mar 2024, Gutkin et al., 2020).
• Aspect-based sentiment and genre analysis: Sentence-level sets may comprise counts of nouns, verbs, adjectives, adverbs, named entities, presence of negation, aspect POS type, synset ambiguity, and length (Chifu et al., 5 Feb 2024); genre separation exploits syntactic tree depth, metaphor rates, and prosodic/meter vectors (Shi et al., 4 Dec 2025).

These sets are engineered for interpretability, ease of feature selection, domain specificity, and extensibility across tasks.

2. Methodologies for Feature Extraction, Representation, and Integration

Feature extraction pipelines range from rule-based/statistical computation over text to sophisticated neural mechanisms:

• Manual and toolkit-based extraction: LFTK, spaCy, and external resources support deterministic computation of surface, POS, NER, and derived metrics with clear provenance (e.g., t_word = token count; a_char_pw = average character per word) (Lee et al., 2023).
• LLM and IR-augmented features: Visual-augmented extraction partitions visual scenes with multimodal LLM prompting, aggregates keywords, and computes hit rates, while TF–IDF similarities and keyword hit rates quantify group-specific lexical patterns (Li et al., 28 Nov 2024).
• Sparse auto-encoder factorization: Auto-encoders decompose hidden states into k-sparse feature vectors, where each base aligns mono-semantically with a canonical linguistic property; necessity and sufficiency are quantified via Feature Representation Confidence (FRC), and causal manipulability via Feature Intervention Confidence (FIC) (Jing et al., 27 Feb 2025).
• Typological vectorization: Feature databases (WALS, URIEL) supply binary and multi-valued feature matrices, patched for missingness via knn-imputation or normalized binning. Text features like mean word length supplement expert features for low-resourced languages (Samardzic et al., 6 Mar 2024).
• Joint embedding and concatenation: In neural MT and interpretable classifiers, feature sets (POS, morphology, dependencies, NER, word/document vectors) are concatenated or summed into dense input vectors for optimization and downstream prediction (Sennrich et al., 2016, Zhang et al., 27 Jun 2025).

This architecture supports transparent modeling, scalable extraction, multilingual generalization, and integration with deep representational spaces.

3. Application Domains and Task-Specific Implementations

Explicit linguistic feature sets are deployed across multiple NLP and cognitive domains:

• Diagnostic classification: For Alzheimer's disease screening, a compact feature set statistically outperforms traditional high-dimensional baselines with >37.5% dimensional reduction and +10% accuracy improvement, directly tracing clinical phenomena such as topic omission, disfluency, and syntactic collapse (Li et al., 28 Nov 2024).
• Truthfulness detection: 220 handcrafted features (LFTK) feeding SVMs enable robust in-domain classification of LLM-generated texts, with cross-model robustness but poor cross-dataset transfer, pinpointing superficial stylistic correlates of truthfulness (Lee et al., 2023).
• Sentiment analysis and difficulty prediction: Nine linguistically motivated features explain, but do not robustly predict, sentence-level ABSA difficulty, suggesting that basic POS/entity/ambiguity metrics underperform relative to task complexity (Chifu et al., 5 Feb 2024).
• Machine translation: Explicit morphological, POS, dependency, and lemma features, appended to subword representations, consistently improve BLEU and chrF3 scores by up to +1.5 BLEU in EN-DE and +1.0 BLEU in EN-RO translation, supporting transfer and regularization (Sennrich et al., 2016).
• Multilingual diversity quantification: High-dimensional typological and text-based feature sets enable rigorous assessment of dataset coverage, automatically identifying missing structural bins—such as synthetic morphology in major NLP corpora—using adapted Jaccard indices (Samardzic et al., 6 Mar 2024, Gutkin et al., 2020).
• Genre and structural probing: Syntactic tree depth/ratio, metaphor rates, and prosodic meter vectors aid binary classification tasks, revealing the high impact of phonetic features (metre) in poetry detection, minor gains from syntax and metaphor, and cross-linguistic variability (Shi et al., 4 Dec 2025).
• Morphological generalization: Probed feature sets (e.g., Person, Tense, Mood, Aspect) adjoined to sequence modeling data splits reveal sharp distinctions between agglutinative and fusional generalization, with ensemble models exploiting compositional morpheme structures (Kodner et al., 2023).
• Representation analysis in neural models: MLEM-based studies precisely attribute the degree and layer-wise ordering of feature encoding in transformer networks, showing that distinct linguistic features are activated, hierarchically organized, and disentangled in mid-layers (Jalouzot et al., 18 Feb 2024).

These applications illustrate the versatility and discriminative power of explicit feature sets in both shallow and deep modeling contexts.

4. Interpretability, Dimensionality, and Feature Selection

Explicit linguistic features are constructed for interpretability, compactness, and human-legibility. Key principles include:

• Dimensional reduction and compact design: Feature engineering reduces model input space by summarizing complex distributions or group patterns (e.g., replacing a 4 K term TF–IDF with three scalars, BLEU/METEOR with five values) (Li et al., 28 Nov 2024).
• Traceability and transparency: Each feature in interpretable sets has a defined algorithmic provenance (LLM output, TF–IDF, parse depth, count ratio), enabling clinical or linguistic audit (Li et al., 28 Nov 2024).
• Composite modeling and correlation analysis: Systematic examination of feature value distributions, ablation, and correlation with target labels guides feature selection and task adaptation; high correlation of specific features (adjective variation, unique word counts, type-token ratios) is repeatedly demonstrated as informative in essay scoring and readability assessment (Lee et al., 2023).
• Multilingual and cross-domain extension: Modular extraction pipelines permit adaptation to multilingual corpora (with limitations on syllable or external-rating features), and guide selective feature use depending on downstream task and domain (Lee et al., 2023, Samardzic et al., 6 Mar 2024).
• Error diversity and orthogonality: Feature interaction studies confirm that diverse feature types (lexical, syntactic, entity, semantic) make nonredundant errors, supporting integration for improved performance and disambiguation (Zhang et al., 27 Jun 2025).

Explicit feature sets thus directly support model interpretability, efficient computation, systematic feature-selection, and domain transfer.

5. Evaluation Metrics, Empirical Outcomes, and Critical Insights

Performance evaluation of explicit feature sets relies on multi-modal metrics and robust statistical testing:

• Classification accuracy, F1, and statistical significance: Compact explainable feature sets yield 85.4% accuracy (RF) and 83.3% (XGBoost) for AD screening, surpassing traditional baselines (75.0%, 72.9%), with ablations confirming unique contribution of blocks (Li et al., 28 Nov 2024). In genre classification, phonetic metre improves F1 by 2–7%, with syntax and metaphor giving contextually variable gains (Shi et al., 4 Dec 2025).
• Metric-based representation assessment: FRC and FIC index feature necessity/sufficiency and causal manipulability; high FRC (>90%) for canonical features demonstrates that sparse auto-encoders recover genuine linguistic substructures (Jing et al., 27 Feb 2025).
• Layer-wise and hierarchical probing: MLEM encoding shows linguistic features are systematically ordered and clustered in BERT representations, with mid-layers exhibiting disentangled, specialist units for distinct grammatical properties (Jalouzot et al., 18 Feb 2024).
• Typological coverage via Jaccard index: Explicit feature binning directly quantifies syntactic/morphological diversity, guiding dataset design and identifying structural gaps (e.g., synthetic morphology missing from >90% major benchmarks) (Samardzic et al., 6 Mar 2024).
• Multi-label prediction and model generalization: CNN–LSTM architectural studies demonstrate nontrivial predictability of typological features from byte-level input, but also reveal limited performance ceilings and uneven coverage, especially for morphology (Gutkin et al., 2020).
• Cross-model and cross-dataset generalization: Handcrafted feature-based classifiers generalize robustly across LLM model scales but poorly across distinctive question/genre types, cautioning against naive transfer (Lee et al., 2023).

The accumulated empirical evidence underscores that explicit linguistic feature sets, when carefully engineered and validated, offer interpretable, compact, and discriminative representations that are essential for understanding, evaluating, and improving models across NLP and cognitive science.

6. Future Directions and Recommendations

Continued development of explicit linguistic feature set methodologies is recommended along several axes:

• Feature enrichment and hybridization: Integrate structural, discourse, sentiment, and semantic role predictors; combine handcrafted and neural features for deeper predictive power (Chifu et al., 5 Feb 2024).
• Multitask and auxiliary supervision: Use feature sets as auxiliary inputs in multi-task models to enable joint learning of linguistic difficulty or structure-sensitive prediction (Chifu et al., 5 Feb 2024).
• Typological and cross-linguistic expansion: Apply mean word length and typological binning approaches to under-represented language families, improve coverage in dataset benchmarks, and guide sampling for maximal diversity (Samardzic et al., 6 Mar 2024).
• Interpretable neural factorization: Leverage sparse auto-encoders and metric encoding to extract and intervene on latent linguistic dimensions within LLMs, supporting causal analysis and representational control (Jing et al., 27 Feb 2025, Jalouzot et al., 18 Feb 2024).
• Task-oriented selection and pipeline adaptation: Use task-specific correlation analysis and ablation to guide practitioner selection, balancing coverage and computational budget; extend extraction systems for multilingual and multimodal settings (Lee et al., 2023).
• Scalable, open-source implementation: Maintain and expand open toolkit resources (e.g., LFTK) for transparent, reproducible feature computation, supporting research and applied needs (Lee et al., 2023).

Explicit linguistic feature sets—by virtue of their operational clarity, empirical rigor, and interpretable design—remain foundational both to NLP system diagnostics and to formal theory-driven language modeling.