semantiline

Semantiline is a representation framework in linguistics and artificial intelligence that encodes semantic information in a linear sequence. It is designed to complement unitary textual data by attaching semantic annotations directly to a running stream of tokens, facilitating processing by sequential models such as recurrent or transformer architectures.

In Semantiline, semantic relations, roles, and operators are encoded as tokens or markers interleaved with surface text, or as a compact annotation sequence attached to the sentence. The goal is to capture predicate-argument structure in a way that preserves order information while remaining compatible with standard tokenization.

Formalization and variants: Some approaches treat Semantiline as a linearized semantic graph where nodes correspond to concepts and edges to relations, flattened via a predefined order. Others use labeled brackets or special symbols to indicate roles like agent, patient, and modifier. The exact syntax varies by framework, but the underlying principle is a single pass representation suitable for sequence models.

Applications and evaluation: It has been proposed for semantic role labeling, machine translation pretraining, and information extraction. Proponents say it simplifies integration with neural models and reduces the need for separate graph parsers, while critics note potential loss of explicit nonlocal relations and the need for robust linearization schemes.

Status: Semantiline remains primarily a topic of research and methodological exploration rather than a standard. It is used in some experimental pipelines and comparative studies of representation approaches. Related concepts include AMR linearization, semantic parsing, and representation learning.