Beyond Similarity - Dimensions of Semantics and How to Detect them: Unterschied zwischen den Versionen
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|kurzfassung=Semantic similarity estimation is a widely used and well-researched area. Current state-of-the-art approaches estimate text similarity with large language models. However, semantic similarity estimation often ignores fine-grain differences between semantic similar sentences. This thesis proposes the concept of semantic dimensions to represent fine-grain differences between two sentences. A workshop with domain experts identified ten semantic dimensions. From the workshop insights, a model for semantic dimensions was created. Afterward, 60 participants decided via a survey which semantic dimensions are useful to users. Detectors for the five most useful semantic dimensions were implemented in an extendable framework. To evaluate the semantic dimensions detectors, a dataset of 200 sentence pairs was created. The detectors reached an average F1 score of 0.815. | |||
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Aktuelle Version vom 2. Januar 2023, 11:12 Uhr
| Vortragende(r) | Felix Pieper | |
|---|---|---|
| Vortragstyp | Masterarbeit | |
| Betreuer(in) | Sophie Corallo | |
| Termin | Fr 13. Januar 2023 | |
| Vortragsmodus | in Präsenz | |
| Kurzfassung | Semantic similarity estimation is a widely used and well-researched area. Current state-of-the-art approaches estimate text similarity with large language models. However, semantic similarity estimation often ignores fine-grain differences between semantic similar sentences. This thesis proposes the concept of semantic dimensions to represent fine-grain differences between two sentences. A workshop with domain experts identified ten semantic dimensions. From the workshop insights, a model for semantic dimensions was created. Afterward, 60 participants decided via a survey which semantic dimensions are useful to users. Detectors for the five most useful semantic dimensions were implemented in an extendable framework. To evaluate the semantic dimensions detectors, a dataset of 200 sentence pairs was created. The detectors reached an average F1 score of 0.815. | |