The Same Semantic Relations Link Structurally Different Realizations of Concepts
Bibliographic record
Abstract
To make sense of an utterance, people identify in its linear linguistic expression the concepts and the connections between them. A concept normally has a lexical realization; connections between concepts often do not, but they are perceived even without the benefit of lexical cues. Making these connections – called semantic relations in the field of natural language processing – relies on the form and structure of linguistic expressions, and the concepts these expressions evoke. This implies two levels: the level of the text, the linguistic expression with its form and (grammatical) structure, and the level of the concepts which the speaker wants to convey. An overview of the literature shows that semantic relations are, for pragmatic reasons, a means to an end – extract information, explain the links between the head of a phrase and its arguments, and so on – and that is why they are analyzed from the perspective of what they link. At the text level, the process of semantic relation analysis is informed by syntactic elements – noun phrases, verbs and their arguments, clauses and so on – thus differentiating semantic relations based on the complexity of the syntactic constructions in which their arguments appear. At the conceptual level, the same semantic relation is assigned to pairs of concepts, regardless of their surface expression. The process can be said to disregard the implications of having syntactic constructions of various complexity correspond to the concepts linked. In this article, we propose to put semantic relations first: analyze them, determine what constraints they place on the concepts they connect, and how those concepts can be lexicalized. Lexicalization takes place via expressions of increasing syntactic complexity: phrases, clauses and multi-clause sentences. Next, we show how the linguistic phenomena involved in producing different lexicalizations explain – in a systematic manner – how semantic relations can have instances in syntactic constructions of various complexity. We focus on binary semantic relations between concepts/textual elements within sentences. This kind of analysis leads to a better understanding of the relations themselves and to a systematic account of phenomena related to their occurrence in texts. It reveals some of the assumptions and linguistic gaps people fill when they recognize relations in text. From the computational point of view of text processing, such a solid basis of the analysis of semantic relations adds consistency. Evidence for a particular relation can come from all its instances in a text, regardless of the syntactic form of the concepts it connects. Knowledge of the expected concepts and their syntactic realization may signal the presence of covert or implied information, which we can then work to retrieve. Assigning a semantic relation should be a conscious choice, with the understanding of what implications such a tag has both for the implicitly and explicitly expressed elements of a concept.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.007 | 0.023 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".