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.