Stanford CoreNLP

Ingest customer support ticket text.

Use CoreNLP to perform sentiment analysis and entity recognition.

Categorize tickets based on sentiment (e.g., negative tickets get higher priority).

Identify key entities (e.g., product names) to route tickets to the appropriate support team.

Scrape financial news articles.

Use CoreNLP to perform named entity recognition to identify companies and organizations.

Use dependency parsing to extract relationships between entities (e.g., mergers, acquisitions).

Use sentiment analysis to gauge market sentiment towards specific companies.

Ingest patient medical record text.

Use CoreNLP to perform named entity recognition to identify medical conditions, medications, and procedures.

Use coreference resolution to link mentions of the same entity (e.g., patient name).

Create a structured summary of the patient's medical history.

Ingest legal document text.

Use CoreNLP to perform named entity recognition to identify relevant parties and locations.

Use dependency parsing to extract clauses related to obligations and liabilities.

Highlight potential risks based on identified clauses.

Collect social media posts mentioning the brand.

Use CoreNLP to perform sentiment analysis on each post.

Use named entity recognition to identify related entities (e.g., competitors, products).

Aggregate sentiment and entity information to understand brand perception.