Which Languages and Content Types Can TextRazor Analyze?

Updated

TextRazor supports 19 languages and is built to analyze text from documents, tweets, and web pages. The same API that extracts entities, keyphrases, topics, and relations in one language can be pointed at content in any of the supported languages, and the service is offered as either a cloud API or self-hosted infrastructure. The practical limits are less about content type and more about how cleanly the text can be passed to the API and how well the knowledgebase covers the entities you care about.

Languages: 19, with language-specific reference material

TextRazor states that its entity extraction, disambiguation and linking, keyphrase extraction, and automatic topic tagging and classification all work across 19 languages. The site does not publish the full list on the page reviewed here, so if your project depends on a specific language, confirm it against the documentation before committing.

What the site does make clear is that client support is language-specific in a different sense: there are separate reference guides for Python, PHP, Java, and the REST API. That means you can call the service from most stacks, but the examples and method names you follow will differ depending on which reference you use. Pick the reference that matches your stack first, then check that your content language is among the 19.

Content types: documents, tweets, and web pages

The site names three content types explicitly:

  • Documents — long-form text such as reports, articles, or internal files.
  • Tweets — short, informal social posts.
  • Web pages — content pulled from the web.

The demo text on the site is a news-style passage about Barclays, Sheikh Mansour, and Abu Dhabi investment, which shows the kind of prose the entity and relation extraction is designed around. Short tweets and web pages are supported, but they behave differently: a tweet gives the model very little context, and a web page may include navigation, ads, or boilerplate that you should strip before sending text for analysis.

What the analysis actually returns

Across those languages and content types, TextRazor extracts:

Capability What it gives you
Entity extraction, disambiguation, linking Identifies people, organizations, places, and links them to knowledgebase entries
Keyphrase extraction Pulls out the main phrases in the text
Topic tagging and classification Assigns topics using the full IAB Content and IPTC Media Topic taxonomies
Relations and typed dependencies Captures how words and entities relate to each other
Synonyms Supports context-aware semantic applications

The knowledgebase is described as a comprehensive set of real-life facts, which is what allows disambiguation — deciding, for example, which "Barclays" or which "Abu Dhabi" entity a mention refers to. That knowledgebase is the main reason results vary by content: well-known entities resolve cleanly, while obscure or very new names may not.

Choosing based on your content

  • If your text is long-form English or another major language, the standard extraction pipeline is the natural fit; the demo passage is representative of what it handles well.
  • If you are processing social posts, expect to send shorter text and treat entity linking as less reliable, since there is less surrounding context to disambiguate with.
  • If you are scraping web pages, clean the text first so navigation and boilerplate do not pollute keyphrases and topics.
  • If you need a specific language outside the common ones, verify it against the documentation before building around it, because the site confirms the count of 19 but not the list.
  • If data residency matters, the self-hosted option is the relevant choice; the cloud API is the default path.

To try it against your own content, the site offers a demo and a free API key, and the getting-started tutorials plus the Python, PHP, Java, and REST references cover the integration step.