textlikhet

Textlikhet refers to the measure of how similar two text fragments are, and is a central concept in natural language processing, information retrieval, and text mining. It encompasses lexical, syntactic, and semantic aspects, ranging from surface-level character matches to deeper meaning and context.

Approaches to textlikhet range from rule-based to statistical and learning-based methods. Rule-based methods include string similarity measures such as Levenshtein distance or Jaro-Winkler. Token- and bag-of-words approaches use metrics like cosine similarity on TF-IDF vectors or Jaccard similarity on word sets. More advanced methods use embeddings that capture semantic information, such as word or sentence embeddings, with cosine similarity or learned scoring functions. In recent years, transformer-based models (for example, BERT and its variants) produce contextual representations that enable high-quality semantic comparisons, often via fine-tuned classifiers or similarity scorers.

Applications of textlikhet include plagiarism and duplicate detection, paraphrase and entailment tasks, information retrieval and search ranking, document clustering, and evaluation of machine translation or summarization quality. It also plays a role in question answering systems and conversational agents, where determining whether different queries or responses convey the same intent is useful.

Common datasets and benchmarks cover both monolingual and cross-lingual settings, such as Semantic Textual Similarity tasks, paraphrase corpora (MRPC, QQP, Quora datasets), and related STS benchmarks. Evaluation typically uses correlation with human judgments (Pearson or Spearman) for semantic measures, or accuracy and F1 for classification tasks.

Challenges include handling polysemy and negation, capturing nuanced paraphrase relationships, domain adaptation, multilingual and cross-language scenarios, and computational efficiency at scale. Interpretability of similarity decisions and robustness to adversarial paraphrasing are ongoing research areas.

See also: text similarity, semantic similarity, plagiarism detection.