Semantic Understanding
Sentiment analysis, semantic similarity, and textual entailment.
Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) goes beyond document-level opinion mining to identify specific aspects of entities and the sentiment expressed toward each, enabling fine-grained understanding of opinions like “The food was great but the service was terrible.”
Commonsense Reasoning
Commonsense reasoning is the ability to draw on implicit world knowledge that humans take for granted – physical intuitions, social conventions, and causal expectations – to understand and reason about language.
Natural Language Inference
Natural language inference (NLI) classifies the relationship between a premise and hypothesis as entailment, contradiction, or neutral, serving as both a core semantic reasoning benchmark and a versatile tool for zero-shot NLP.
Negation and Speculation Detection
Negation and speculation detection identifies negated and uncertain statements in text – determining not just what is said, but what is denied or merely hypothesized – a capability critical for biomedical NLP, information extraction, and sentiment analysis.
Semantic Similarity
Semantic similarity measures the degree of meaning overlap between two linguistic units – words, sentences, or documents – providing a graded, continuous score rather than a categorical judgment.
Temporal Reasoning
Temporal reasoning is the ability to identify, interpret, and reason about time expressions, event ordering, and temporal relationships in text, enabling systems to construct timelines and answer when-questions.
Textual Entailment
Textual entailment is the task of determining whether the meaning of one text (the hypothesis) can be logically inferred from another text (the premise), forming the foundation of computational semantic inference.
Word Sense Disambiguation
Word sense disambiguation (WSD) is the task of determining which meaning of a polysemous word is intended in a given context, resolving one of the oldest and most fundamental ambiguities in natural language processing.