Timeline-Based Sentence Decomposition

• Timeline-based sentence decomposition is a method that partitions natural language into temporally anchored substructures to clearly map events to time points.
• It leverages a range of models—from O(n)-time neural architectures to LLM-guided in-context approaches—to improve precision in temporal fact extraction.
• This methodology underpins applications such as knowledge graph construction, video moment localization, and robust temporal reasoning.

Timeline-based sentence decomposition is a family of methodologies for partitioning natural-language sentences into temporally grounded substructures—typically, sub-sentences or interval representations—such that each event and fact is explicitly associated with precise temporal anchors. This decomposition is foundational for temporal information extraction, event structure modeling, and temporally aware knowledge graph construction. Recent advances span from direct O(n)-complexity timeline construction in temporal relation extraction to LLM-based in-context sentence decomposition for temporal fact mapping, as well as tree-structural sentence decomposition for temporally compositional video-text grounding.

1. Formal Problem Definition and Motivations

The principal objective of timeline-based sentence decomposition is to resolve the multi-mapping ambiguity between events/facts and their temporal referents, especially in sentences containing multiple, interleaved time anchors and implicit event references. Formally, given an input sentence $s = \langle x_1, \ldots, x_n \rangle$, the task is to identify all time expressions $T = (t_1 \prec t_2 \prec \ldots \prec t_k)$ and construct a mapping:

$D : T \rightarrow \{\text{Subsentences } d_i \}$

where each $d_i$ contains, and only contains, those events in $s$ occurring at $t_i$. The mapping must cover all fact-events in $s$ without misassignment of events to times (Chen et al., 16 May 2024).

This decomposition is essential for temporal fact extraction, where the goal is to derive structured fact quintuples $(e_\text{head}, r, e_\text{tail}, q, t)$ linking arguments and relations to granular time values. In practice, timeline-based decomposition improves both recall and precision for downstream temporal reasoning, knowledge base completion, and temporally conditioned inference (Chen et al., 16 May 2024).

2. Architectures and Algorithms for Timeline Construction

Current techniques for timeline-based decomposition can be categorized into three primary paradigms:

A. Direct O(n)-Time Timeline Models (S-TLM, C-TLM)

Given pre-tagged event and temporal entity mentions (typically via TimeML-style recognition), linear-time neural models assign each event a start ($\hat{s}_i$) and end ($\hat{e}_i$) coordinate on a unified, relative time axis without explicit pairwise temporal relation classification (Leeuwenberg et al., 2018):

• S-TLM (Simple Time-Line Model): Per-entity features from word, POS-tag, entity-type, and aspect/modality embeddings are projected to $\hat{s}_i$ and $\hat{d}_i$ (duration), with $\hat{e}_i = \hat{s}_i + \max(\hat{d}_i, d_{\min})$.
• C-TLM (Contextual Time-Line Model): The sentence is globally encoded with two BiLSTMs (one for start, one for duration), with start and duration predicted from token-level hidden states at each entity position. Complexity remains linear in event count.

Model parameters are optimized via hinge-based losses encoding TimeML interval constraints, thus ensuring temporal consistency. All events, including temporal expressions and modal/hypothetical references, are projected onto the timeline. For example, C-TLM achieves up to 58.4 F₁ (TempEval-3 metric) while guaranteeing global consistency (Leeuwenberg et al., 2018).

B. In-Context LLM-Guided Decomposition (TSD)

For temporal fact extraction from complex sentences, a timeline-based sentence decomposition (TSD) approach uses a large LLM (e.g., ChatGPT3.5) prompted with task instructions, few-shot positive/negative examples, and human feedback. The LLM partitions the original sentence into sub-sentences, each aligned to exactly one time anchor—enabling accurate mapping of implicit/explicit facts to times.

A temporal tagger (e.g., SUTime) first extracts and normalizes all time values to yield $T$. The LLM, in-context, generates:

$D = \{ (t_i, d_i) \}$

where each $d_i$ is a natural-language statement of the events at $t_i$. Precision and recall of these assignments can surpass 94% on held-out complex examples (Chen et al., 16 May 2024).

C. Compositional Tree-Attention Decomposition (TCMN)

In the context of video moment localization, the Temporal Compositional Modular Network (TCMN) decomposes a query sentence using a tree-LSTM-attention mechanism into soft phrase embeddings representing the main event, context event, and temporal signal. This enables compositional, segment-wise alignment in cross-modal retrieval tasks, particularly for natural-language queries that contain explicit or implicit temporal sequencing (Zhang et al., 2019).

3. Temporal Models and Representations

Timeline-based decompositions produce representations that can be broadly grouped as:

• Continuous Interval Models: Each event $e_i$ is assigned $\hat{s}_i \in \mathbb{R}$ and $\hat{d}_i \in \mathbb{R}$ with minimum duration $d_{\min}$, yielding a global axis where event intervals may overlap, nest, or abut. Constraints from TimeML or Allen interval algebra are encoded via pointwise hinge losses (Leeuwenberg et al., 2018).
• Graph-Structured Event-Relation Models: For decompositional semantics, induced event types, participants, roles, and temporal relations (before, contains, identical, etc.) are collectively represented in a directed graph $G = (V, E_\text{event}, E_\text{time})$, with start/end points normalized to $[0,1]$ (Gantt et al., 2021).
• Time-Anchored Subsentence Collections: In TSD, each subsentence is anchored to a unique time value (date/time expression), forming a partition suitable for accurate temporal fact extraction (Chen et al., 16 May 2024).

4. Applications: Temporal Fact and Structure Extraction

Timeline-based decomposition is now central to several high-precision temporal reasoning pipelines:

• Temporal Fact Extraction and Knowledge Graph Construction: TSD enables fine-grained mapping from complex sentences to temporal fact quintuples, supporting temporally scoped RDF triples and n-ary event models for knowledge graphs (Chen et al., 16 May 2024).
• Temporal Event Structure Analysis: Joint induction of event types, roles, and relations (e.g., Vendlerian aspectual classes, protoroles, temporal point bounds) produces event-annotated graphs for document-level natural language understanding (Gantt et al., 2021).
• Video Moment Localization: Tree-structured timeline decomposition facilitates compositional matching between natural-language event sequences and video segments, leveraging both visual (RGB, flow) and linguistic modalities (Zhang et al., 2019).
• TimeML and Temporal Relation Extraction: Direct timeline construction models outperform indirect O(n²) pairwise relation-extraction pipelines, guaranteeing global consistency and improved F-score under temporal closure metrics (Leeuwenberg et al., 2018).

5. Empirical Evaluation and Benchmarks

Timeline-based decomposition models are evaluated with both decomposition quality metrics and final downstream extraction accuracy:

Method	HyperRED-T F₁	ComplexTRED F₁	TempEval-3 F₁
TSDRE w/ Flan-T5	66.7	42.6	—
Flan-T5-Large (baseline)	63.7	40.3	—
S-TLM / C-TLM	—	—	58.4
CubeRE	52.3	33.4	—

• TSDRE (PLM fine-tuning with LLM-based decomposition) sets new state of the art on both temporal fact extraction datasets (Chen et al., 16 May 2024).
• C-TLM achieves F₁ up to 58.4 on temporally annotated event graphs (Leeuwenberg et al., 2018).
• Decomposition precision/recall for TSD (LLM in-context) exceeds 94% with human feedback in the prompt (Chen et al., 16 May 2024).
• In decompositional event structure modeling, event-type and relation-type micro-F₁ scores reach 75% and 0.79, respectively; temporal point RMSE is 0.12 on normalized scales (Gantt et al., 2021).

6. Limitations, Error Modes, and Future Directions

Current limitations include the reliance on closed-source LLMs (e.g., ChatGPT3.5) for decomposition routines (open-source LLMs exhibit lower partition accuracy), input length constraints for document-level decomposition, and dataset noise from distant supervision in large corpus construction (Chen et al., 16 May 2024). Error analysis reveals that time-to-fact assignment is highly accurate post-decomposition, but NER and relation misclassifications still dominate error cases. Consistent with this, time selection error rates are as low as 2–6% under TSDRE (Chen et al., 16 May 2024).

Ongoing research seeks to (1) generalize timeline-based decomposition to handle inferred or relative time expressions (“three days later”), (2) unify decomposition and structured extraction within a single neural architecture, and (3) extend LLM-guided decomposition to non-sentential and cross-document scenarios.

7. Connections to Related Temporal and Event Decomposition Work

Timeline-based decomposition complements and extends classical temporal information extraction, including direct timeline modeling (Leeuwenberg et al., 2018), decompositional semantics (Gantt et al., 2021), and compositional video-language alignment (Zhang et al., 2019). The paradigm of LLM-based, in-context, prompt-driven decomposition is demonstrated to outperform both direct LLM extraction and classical rule/pattern-based methods for temporal fact mapping in complex, multi-event sentences (Chen et al., 16 May 2024). Methods from event structure induction, tense/aspect modeling, and interval algebra remain central for ensuring that decompositions respect fine-grained temporal semantics and support compositional downstream inference.