Dezambiguarea

Dezambiguarea is the process of determining the intended meaning of an expression that has multiple possible interpretations within a given context. It is a central task in natural language processing, information retrieval, and knowledge-based systems, and it is particularly relevant for Romanian-language processing as well as multilingual applications.

There are several forms of dezambiguare. Word sense disambiguation (WSD) aims to identify which sense of a polysemous word is present in a sentence. Named entity disambiguation (also called entity linking) connects ambiguous mentions to proper entities in a knowledge base. Toponym disambiguation resolves location references, and broader disambiguation tasks address multiword expressions and conceptual ambiguity across domains.

Techniques for dezambiguarea range from rule-based approaches that use dictionaries and linguistic rules to knowledge-based methods that rely on structured resources such as WordNet, Wikidata, or DBpedia. Statistical and machine learning approaches use annotated corpora to learn context-sensitive cues, while modern methods leverage contextual embeddings and neural networks to model meaning directly from text. Hybrid systems combine multiple signals, including syntax, semantics, and world knowledge, to improve accuracy.

Applications span search engines, question answering, machine translation, text analytics, and digital assistants. Effective dezambiguarea improves relevance, accuracy, and user experience by ensuring that retrieved information and generated responses reflect the intended meaning.

Challenges include resolving sense granularity, handling domain shifts, resource limitations for less-studied languages, and efficiently disambiguating in real time. Ongoing research focuses on multilingual and cross-lingual disambiguation, integration with knowledge graphs, and creating robust evaluation benchmarks.