Core NLP Tasks: Analysis

POS tagging, NER, parsing, and text classification.

Constituency Parsing

Constituency parsing decomposes sentences into hierarchical phrase-structure trees, revealing how words group into nested constituents (noun phrases, verb phrases, etc.) according to a grammar.

Coreference Resolution

Coreference resolution determines which expressions (mentions) in a text refer to the same real-world entity, linking pronouns, names, and descriptions into coherent entity chains.

Dependency Parsing

Dependency parsing recovers the head-modifier relationships between words in a sentence, producing a tree structure that reveals grammatical dependencies without intermediate phrasal nodes.

Named Entity Recognition

Named entity recognition (NER) identifies and classifies spans of text that refer to real-world entities such as persons, organizations, locations, dates, and other domain-specific categories.

Part-of-Speech Tagging

Part-of-speech (POS) tagging assigns a grammatical category (noun, verb, adjective, etc.) to each word in a sentence, serving as a foundational sequence labeling task for downstream NLP.

Relation Extraction

Relation extraction identifies and classifies semantic relationships between entities mentioned in text, converting unstructured prose into structured knowledge triples (subject, relation, object).

Semantic Role Labeling

Semantic role labeling (SRL) identifies the predicate-argument structure of a sentence – determining who did what to whom, where, when, and how – bridging syntactic parsing and meaning representation.

Sentiment Analysis

Sentiment analysis detects subjective opinion, polarity (positive/negative/neutral), and emotion in text, operating at document, sentence, and aspect levels of granularity.

Text Classification

Text classification assigns one or more predefined category labels to a document, sentence, or passage, serving as the most widely deployed NLP capability in production systems.