Identify names of persons, locations, organizations, as well as money amounts, time and date expressions in English texts automatically.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Named entity recognition service for Twitter data. Identifies person,location, organization etc. and also performsnormalization of abbreviations and common shorthands ("brb", "gr8", "2day",etc.).
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Collect tweets, view tweet statistics, and store results in your dashboard for further analysis.
GBP0.05 / CPU hour
Identifies named entities of various types in English documents, and disambiguates them against DBpedia.
800 free requests / day
Batch processing not available
Identifies named entities of various types in German documents, and disambiguates them against DBpedia.
800 free requests / day
Batch processing not available
Identifies named entities of various types in French documents, and disambiguates them against DBpedia.
800 free requests / day
Batch processing not available
Identifies named entities of various types in Spanish documents, and disambiguates them against DBpedia.
800 free requests / day
Batch processing not available
Identifies biomedical named entities of various types, and disambiguates them against UMLS (Medical Subject Headings subset only).
800 free requests / day
Batch processing not available
Identifies biomedical named entities of various types, and disambiguates them against UMLS (Snomed subset only).
800 free requests / day
Batch processing not available
Identifies biomedical named entities of various types, and disambiguates them against UMLS.
800 free requests / day
Batch processing not available
A named entity recognition service for documents in German.
Based on ANNIE, it identifies names of persons, locations,
and organizations.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Service to identify the languages of tweets.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Identifies words and punctuation in tweets
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
A named entity recognition service for documents in French.
Based on ANNIE, it identifies names of persons, locations,
and organizations.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
A service that tags tweets with part-of-speech information, e.g. nouns, verbs.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
A named entity recognition service for documents in the Romanian language.
Based on ANNIE, it identifies names of persons, locations,
organizations, as well as money amounts, time and date
expressions.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
A named entity recognition service for documents in the Russian language.
Based on ANNIE, it identifies names of persons, locations,
organizations, as well as money amounts, time and date
expressions.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
A named entity recognition service for documents in the Russian language.
Based on ANNIE, it identifies names of persons, locations,
organizations, as well as money amounts, time and date
expressions.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
The CYMRIE named entity recognition service for Welsh text. Identifies namesof persons, locations, organizations, as well asmoney amounts, time and date expressions.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Analyse French tweets for names of persons, locations and organizations. Also performs normalization of abbreviations and common Twitter slang.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Analyse German tweets for names of persons, locations and organizations. Also performs normalization of abbreviations and common Twitter slang.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Annotates tokens and sentences in English text, adding part-of-speech and morphological root and affix to each token.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Base Noun Phrase Chunker, producing NounChunk annotations.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Annotates numbers and measurement expressions, with their
normalised values in SI units.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Annotates named entities (person, location,
organization, date) as well as numbers and
measurement expressions.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Recognises opinionated sentences in English text and classifies
them as positive or negative. It also indicates emotion type, author and target of the opinion,
average sentiment, nd some sentence types.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
The DecarboNet environmental annotation service. Identifies named entities, environmental terms, linguistic features and sentiment in social media texts.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
The DecarboNet environmental annotation service. Identifies named entities, environmental terms, and sentiment in German language social media texts.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Recognises opinionated sentences in English tweets and classifies
them as positive or negative. It also indicates emotion type, author and target of the opinion,
and some sentence types.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Classifies tweets based on whether the claims they make are true, false or unverifiable.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Estimates the probability that a document could be classed as "hyper-partisan".
600 free requests / day
Larger batches GBP0.80 / CPU hour
Annotates descriptions of violations against journalists such as killings, threats etc. Identifies key information about the event and people involved.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Annotates URLs to highlight the credibility of the source at which they ultimately point.
50 free requests / day
Batch processing not available
This pipeline identifies URLs in the input text and tries to process them with optical character recognition to identify text in images.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Classifies a reply to a tweet based on it's stance (support, deny, query, or comment) to the original
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
A service that tags abusive utterances. It will also tag UK members of parliament from 2015 to 2020, a range of politically relevant topics, and entities such as persons, locations, organizations and Twitter entities such as hashtags and user mentions.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Assigns COVID-19 misinformation to one of several categories, including government actions, impact on community, medical advice, conspiracy theories, vaccine, etc.
600 free requests / day
Larger batches GBP0.80 / CPU hour
A service for analysing tweets about the UK's referendum on EU membership. It identifies topics, hashtags, user mentions, and voting intention (where possible).
1,200 free requests / day
Batch processing not available
A service for analysing tweets about UK politics. It identifies topics, politicians, sentiment, abuse terms and more.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Multiparadigm indexing server - index the results of your annotation jobs for rapid retrieval.
GBP0.50 / CPU hour
The SUMMA Text Summarization (EN) uses the SUMMA toolkit developed by Horacio Saggion to provide a generic English document summarizer.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
The SUMMA Text Summarization (ES) uses the SUMMA toolkit developed by Horacio Saggion to provide a generic Spanish document summarizer.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
The English tokeniser, sentence splitter, POS tagger,
phrase chunker and named-entity recogniser from
Apache OpenNLP.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
The German tokeniser, sentence splitter and POS tagger
from Apache OpenNLP.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
The Dutch tokeniser, sentence splitter, POS tagger,
phrase chunker and named-entity recogniser from
Apache OpenNLP.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Web-based collaborative corpus and document annotation tool
GBP4.99 + GBP0.15 / CPU hour
A service to classify the author of a tweet as either a person, organization or location, of various sub-types. Note: can only operate on Twitter JSON documents.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Universal Dependencies POS tagger for en / English
based on a simple window-based maximum entropy model.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Universal Dependencies POS tagger for bg / Bulgarian based on a simple window-based maximum entropy model.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Universal Dependencies POS tagger for cs / Czech based on a simple window-based maximum entropy model.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Universal Dependencies POS tagger for pl / Polish based on a simple window-based maximum entropy model.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Universal Dependencies POS tagger for ca / Catalan
based on a simple window-based maximum entropy model.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Universal Dependencies POS tagger for da / Danish
based on a simple window-based maximum entropy model.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Universal Dependencies POS tagger for el / Greek
based on a simple window-based maximum entropy model.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Universal Dependencies POS tagger for es / Spanish
based on a simple window-based maximum entropy model.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Universal Dependencies POS tagger for et / Estonian
based on a simple window-based maximum entropy model.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Universal Dependencies POS tagger for eu / Basque
based on a simple window-based maximum entropy model.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Universal Dependencies POS tagger for fi / Finnish
based on a simple window-based maximum entropy model.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Universal Dependencies POS tagger for fr / French
based on a simple window-based maximum entropy model.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Universal Dependencies POS tagger for hr / Croatian
based on a simple window-based maximum entropy model.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Universal Dependencies POS tagger for id / Indonesian
based on a simple window-based maximum entropy model.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Universal Dependencies POS tagger for lv / Latvian
based on a simple window-based maximum entropy model.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Universal Dependencies POS tagger for nl / Dutch
based on a simple window-based maximum entropy model.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Universal Dependencies POS tagger for pt / Portugese
based on a simple window-based maximum entropy model.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Universal Dependencies POS tagger for ro / Romanian
based on a simple window-based maximum entropy model.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Universal Dependencies POS tagger for ru / Russian
based on a simple window-based maximum entropy model.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Universal Dependencies POS tagger for sk / Slovak
based on a simple window-based maximum entropy model.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Universal Dependencies POS tagger for sl / Slovenian
based on a simple window-based maximum entropy model.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
Universal Dependencies POS tagger for sv / Swedish
based on a simple window-based maximum entropy model.
1,200 free requests / day
Larger batches GBP0.80 / CPU hour
