Sentence-Level Detection Method

• Sentence-level detection is a fine-grained NLP approach that classifies individual sentences using detailed annotations and contextual embeddings.
• It employs diverse methodologies, including BERT-based models, Transformers, and CRF-integrated architectures to enhance classification accuracy.
• Applications in satire detection, AI-generated text segmentation, and content moderation highlight its practical value in modern automated text analysis.

A sentence-level detection method refers to computational approaches for determining specific properties, categories, or classes of individual sentences within a broader textual context, such as news articles, narratives, or multi-sentence reports. Sentence-level detection spans a variety of tasks, including satire recognition, subjectivity, event and relation extraction, authorship identification, and the detection of anomalies such as incoherence or AI-generated content. These methods are central to fine-grained natural language understanding where document-level granularity is insufficient. Current research demonstrates that sentence-level detection often benefits from context modeling, advanced neural architectures, and tailored training/evaluation regimes.

1. Dataset Construction and Annotation in Sentence-Level Detection

Sentence-level detection methods are predicated on the availability of high-quality, sentence-annotated corpora capturing the target phenomenon. Construction protocols typically entail automated sentence extraction, annotation by multiple human raters, and careful split strategies to prevent information leakage.

• Granular Annotation: In the "SeLeRoSa" dataset for Romanian satire detection, sentences were manually labeled as "satirical," "regular," or "uncertain" by three annotators. Only those achieving a majority label entered the final dataset, with ambiguous cases discarded, yielding a set of 13,873 sentences spanning several domains and 20 BERTopic-detected subtopics (Smădu et al., 31 Aug 2025).
• Class Distribution and Splitting: Statistical balance and rigor in train/validation/test splitting are crucial. For instance, SeLeRoSa split by source article (not random sentences) to avoid leakage and reported domain distributions and median sentence lengths per split.
• Fine-Grained and Structured Tagging: For tasks such as sustainability initiative detection, both binary and structured IOBES tagging were used to handle singleton and multi-sentence span annotation at the sentence level (Hirlea et al., 2021).
• Quality Control and Cross-Linguality: In subjectivity detection, objective guidelines and adjudication reduced ambiguity. Cross-lingual datasets (e.g., NewsSD-ENG and re-annotated Italian corpus) facilitate transfer experiments and cross-lingual robustness (Antici et al., 2023).

These rigorous annotation protocols are foundational, impacting both the granularity and validity of subsequent model training and evaluation.

2. Core Architectures and Modeling Approaches

Diverse neural architectures and processing paradigms are adapted for sentence-level detection, with specialization according to the task and available context.

Task	Typical Backbone	Context Utilization	Structured Output
Satire/Subjectivity	BERT, RoGPT2, LLMs	Sentence-only or document	Classification head
Event/Relation	BERT/RoBERTa + GAT	Entity markers, dependency	Softmax over relation classes
Sustainability Initiatives	BERT/RoBERTa + CRF	k-sentence window	IOBES sequence tags + CRF
Speaker Change	Hierarchical LSTM + Attn	Bi-level (utterance/context)	Pairwise+contextual binary score
AI-Text Detection	DeBERTa + BiGRU + CRF	Global (all-sentence input)	Token-level sequence labeling
Incoherence Detection	Sentence-level Transformer	Global sentence seq	Sigmoid via MLP per slot

Key paradigm distinctions:

• Pure Sentence-Local: Models like BERT are fine-tuned on single-sentence inputs with softmax classification, achieving up to 80.7% F1 in fine-tuned settings for satire (Smădu et al., 31 Aug 2025).
• Contextual/Hierarchical Architectures: Many tasks benefit from contextual modeling (neighboring sentences, document context). For example, Context-LSTM-CNN explicitly encodes left/right context using FOFE, while sustainability detection uses a Transformer with CRF over sliding sentence windows (Hirlea et al., 2021, Song et al., 2018).
• Sequence Labeling with Structured Decoding: For applications such as boundary detection in hybrid human/AI texts, architectures combine Transformers with BiGRU/BiLSTM and add a CRF to model structured label dependencies and jointly optimize prediction of sentence-level boundaries (Teja et al., 22 Sep 2025).

3. Mathematical Formulation and Training Protocols

The backbone of sentence-level detection is the standard cross-entropy loss for classification:

$$L = -\frac{1}{N} \sum_{i=1}^N \sum_{c} y_{i,c} \log p_{i,c}$$

where $c$ indexes classes (e.g., "satirical"/"regular"), and typically includes regularization terms, such as AdamW weight decay, and model-specific regularizers (e.g., LoRA dropout for LLM adapters, CRF likelihood for sequence tagging).

Structured sequence labeling introduces Conditional Random Field (CRF) objective functions:

$$P(y\mid x) = \frac{\exp(s(x, y))}{Z(x)},\quad s(x, y) = \sum_t E_{t, y_t}(x) + \sum_t A_{y_t, y_{t+1}}$$

Models may also integrate auxiliary objectives, such as semantic matching losses in incoherence detection (cosine distance between predicted and ground-truth sentence embeddings) (Cai et al., 2020). Training protocols feature careful batch sizing, learning rate strategies (including layer-wise decay), and curriculum learning for handling easy-to-hard progression (Park et al., 2021).

Fine-tuning large LLMs frequently utilizes parameter-efficient methods (e.g., QLoRA with low-rank adaptation and quantization), which enables their use even with large-scale models in the sentence classification regime (Smădu et al., 31 Aug 2025).

4. Evaluation Metrics, Comparative Results, and Ablation

Evaluation in sentence-level detection is typically performed with micro-averaged accuracy and F1-score for binary/multiclass tasks, and with span-level, token-level, or boundary-specific metrics in structured settings.

• Accuracy/F1, Confusion Metrics: On SeLeRoSa, BERT-base-Romanian achieved 76.6% accuracy and 70.8% F1, whereas fine-tuned large LLMs reached up to 80.7% F1. LLMs in zero-shot mode exhibited high false-positive rates (32–52%), indicating over-prediction of satire unless fine-tuned (Smădu et al., 31 Aug 2025).
• Span/BIO-based Metrics: In initiative-span detection, both min-match and exact-match F1 were reported, showing substantial gains (+3–5 points) from IOBES tags and inclusion of context (Hirlea et al., 2021).
• Specialized Task Metrics: AI-generated text segmentation employs F1@K for boundary detection and MAE for offset prediction. CRF-based models attained F1@All=.806 (TriBERT) and MAE=8.47 (M4GT), surpassing all baselines (Teja et al., 22 Sep 2025).
• Generalization and Robustness: Sentence-level detectors such as SeqXGPT generalize across domains and generation models in AI-text detection, maintaining macro-F1 >95 on out-of-distribution domains, and outperforming both threshold-based and standard sequence models (Wang et al., 2023).

Ablation analyses consistently demonstrate the importance of sequence-structuring components (e.g., CRF layers, dynamic dropout), advanced optimization protocols, and labeled context for high performance, especially in nuanced tasks like boundary or error detection.

5. Limitations, Error Modes, and Open Challenges

Current sentence-level detection methods face task-specific and general challenges:

• Zero-Shot/Few-Shot Weakness: LLMs under zero-shot conditions frequently default toward over-predicting rare or salient classes ("satirical"), and are highly sensitive to prompt and label order (Smădu et al., 31 Aug 2025).
• Context Insufficiency: Sentence-only cues are often inadequate in domains requiring cross-sentence or discourse-informed inference. Models that ignore context regularly perform worse, particularly on tasks like sustainability initiative detection or narrative incoherence spotting (Hirlea et al., 2021, Cai et al., 2020).
• Class and Topic Imbalance: Domain/topic skew and class-imbalance degrade recall or introduce bias, warranting strategies such as targeted data augmentation or cost-sensitive objectives.
• Generalization to Unseen Domains: Questions remain regarding transferability to out-of-domain topics, languages, or adversarial input (e.g., stylometric obfuscation in AI-authorship detection).

Recognized error types include confusion caused by subtle rhetorical or figurative language, over-reliance on surface cues for subjectivity or satire, and boundary ambiguity in structural sequence labeling. Improvement directions include hierarchical and discourse-aware models, auxiliary linguistic feature integration, targeted prompt engineering, and exploration of multitask or cross-lingual learning frameworks.

6. Applications and Future Research Directions

Sentence-level detection methods underpin a spectrum of upstream and downstream applications:

• Fine-Grained Content Moderation and Verification: Satire and subjectivity detection aid misinformation management and nuanced content filtering (Smădu et al., 31 Aug 2025, Antici et al., 2023).
• Document Summarization and Structuring: Detection of events, relations, and initiatives at the sentence-level enhances automatic summarization, knowledge extraction, and policy/ESG analysis (Hirlea et al., 2021, Ling et al., 2023, Park et al., 2021, Marujo et al., 2014).
• Hybrid and Collaborative Content Provenance: Segmenting AI- and human-authored spans within documents is essential in forensic linguistics, educational assessment, and provenance tracking (Teja et al., 22 Sep 2025, Wang et al., 2023).
• Cognitive and Psycholinguistic Modeling: Predicting human comprehension or processing cost from sentence-level metrics informs both neuroscientific studies and LLM interpretability (Sun et al., 23 Mar 2024).
• Efficient Self-Supervised Learning: Training encoders on fake sentence detection or related auxiliary tasks yields efficient, high-quality representations for broad NLP utility (Ranjan et al., 2018).

Emerging areas include adaptive context windows for coreference/discourse reasoning, advanced adversarial and watermark-aware AI-authorship segmentation (Zhang et al., 24 Apr 2025), and integrative approaches leveraging both context and structural sequence dependencies. Future research is focused on improving robustness, scaling to low-resource and multilingual contexts, and reconciling sentence-level detection with higher-level inference and generation tasks.