Text enrichment
Enrich your text data through state-of-the-art NLP and machine learning models: sentiment, topics, concepts, translation, and more.
By Zafer and 1 other2 authors15 articles
- Coreference resolutionFinds all expressions that refer to the same entity in a text.
- Entity extraction & linkingDetects entities and links them to entities stored in knowledge bases.
- Score textAssigns numerical values to texts based on syntactic or semantic similarity
- Training and deployment of custom text classifiers with Dcipher AnalyticsTrain custom text classifiers on unlabeled or partially labeled data using the Active Learning approach.
- Classify text (zero-shot)Predicts labels using BART-based zero-shot classifiers without requiring training a text classifier beforehand.
- Tag by ruleTags values in the selected field based on the defined keyword-based boolean criteria.
- Sentiment analysisThe Analyze Sentiment operation analyzes the sentiment of input text through deep learning or sentiment lexicons.
- EmojizationThe Emojize operation interprets the text and outputs the emojis that best capture the emotional nuances expressed.
- Topic modelingUse Detect Topics to identify topics in large volumes of unlabeled text.
- Concept detectionThe Detect Concepts operation finds the underlying concepts in the input text.
- Language detectionThe Detect Language operation detects the language of each input text and outputs the corresponding ISO 639-1 (2-letter) language code.
- Text-level machine translationThe Translate Text operation uses Google Translate to machine translate the input text.
- Word-level machine translationTranslate Words translates between any pair of 58 available languages, enabling multi-language analysis without full text-level translation.
- Knowledge extractionEnrich your data by extracting knowledge in the form of triplets
- Third-party NLP servicesProvides access to third-party text enrichment services such as categorization and emotion recognition.