Implement Natural Language Processing Solutions
Prebuilt Azure AI Language features
- Language detection returns a language code and score.
- Sentiment analysis gives document-level and sentence-level scores for positive, neutral and negative plus the dominant label; documents containing both strong positive and negative sentences return mixed. Opinion mining (aspect-based sentiment) is an option on top of sentiment, not a separate call.
- Key phrase extraction, entity recognition (including PII and health domains), entity linking to a knowledge base, and summarization split into extractive (selects source sentences) and abstractive (generates new text).
- PII detection returns entities with categories and a redaction policy you control (mask, character, no masking).
Custom language models
CLU (conversational language understanding) maps utterances to intents and extracts entities; training needs labelled utterances per intent, with entity components (learned, list, regex, prebuilt). Custom text classification labels documents (single or multi label). Custom NER extracts domain entities. Question answering is different: it is not trained on utterances but on a knowledge base of question/answer pairs, optionally imported from documents or URLs, with follow-up prompts and chit-chat.
Choosing an API
Intent routing from free-form user speech → CLU. FAQ lookup over curated answers → question answering. Routing a ticket by topic → custom text classification.
Traps and rule of thumb
Exams blur CLU with question answering, and assume sentiment is always document-only. Also watch for the difference between entity recognition (typed mentions) and entity linking (resolves to a Wikipedia-style identity).
Rule of thumb: utterances with examples → CLU; questions with answers → question answering; documents with labels → text classification.