NLP still faces many challenges. Human speech is irregular and often ambiguous, with multiple meanings depending on context. Yet, programmers have to teach applications these intricacies from the start. Homonyms and syntax can confuse data sets. And even the best sentiment analysis cannot always identify sarcasm and irony. It takes humans years to learn these nuances — and even then, it's hard to read tone over a text message or email, for example. Text is published in various languages, while NLP models are trained on specific languages. Prior to feeding into NLP, you have to apply language identification to sort the data by language. Unspecific and overly general data will limit NLP's ability to accurately understand and convey the meaning of text. For specific domains, more data would be required to make substantive claims than most NLP systems have available. Especially for industries that rely on up to date, highly specific information. New research, like the ELSER – Elastic Learned Sparse Encoder — is working to address this issue to produce more relevant results. Processing people's personal data also raises some privacy concerns. In industries like healthcare, NLP could extract information from patient files to fill out forms and identify health issues. These types of privacy concerns, data security issues, and potential bias make NLP difficult to implement in sensitive fields.