CoreNLP vs UBIAI
Overview
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An advanced language model comprising 67 billion parameters
Detailed Comparison
It is an efficient and easy-to-use text annotation tool for Natural Language Processing (NLP) applications. With this, you can train an NLP model in few hours by collaborating with team members and using the machine learning auto-annotation feature. | It provides a set of natural language analysis tools written in Java. It can take raw human language text input and give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize and interpret dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases or word dependencies, and indicate which noun phrases refer to the same entities. |
Multi-format document upload: TXT, CSV , JSON , PDF, DOC, HTML;
Multilingual: English, French, German, Arabic, Spanish, etc…;
Dictionary/Regex auto-annotation: input a list of words or regex patterns along with their associated entities. The tool will automatically scan the documents and auto-annotate;
ML auto-annotation: Train an NER model to auto-annotate your documents;
Bias detection: visualize entity and word distribution across your documents to detect skewed annotation toward specific entities.
Collaboration: Share annotation tasks among team members and monitor progress;
Annotation format export: JSON, IOB, Amazon Comprehend, Stanford CoreNLP | An integrated NLP toolkit with a broad range of grammatical analysis tools;
A fast, robust annotator for arbitrary texts, widely used in production;
A modern, regularly updated package, with the overall highest quality text analytics;
Support for a number of major (human) languages;
Available APIs for most major modern programming languages
Ability to run as a simple web service |
Statistics | |
GitHub Stars - | GitHub Stars 10.0K |
GitHub Forks - | GitHub Forks 2.7K |
Stacks 2 | Stacks 19 |
Followers 11 | Followers 23 |
Votes 0 | Votes 1 |
Integrations | |
| No integrations available | |
What are some alternatives to UBIAI, CoreNLP?
rasa NLU
rasa NLU (Natural Language Understanding) is a tool for intent classification and entity extraction. You can think of rasa NLU as a set of high level APIs for building your own language parser using existing NLP and ML libraries.
SpaCy
It is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. It comes with pre-trained statistical models and word vectors, and currently supports tokenization for 49+ languages.
Speechly
It can be used to complement any regular touch user interface with a real time voice user interface. It offers real time feedback for faster and more intuitive experience that enables end user to recover from possible errors quickly and with no interruptions.
MonkeyLearn
Turn emails, tweets, surveys or any text into actionable data. Automate business workflows and saveExtract and classify information from text. Integrate with your App within minutes. Get started for free.
Jina
It is geared towards building search systems for any kind of data, including text, images, audio, video and many more. With the modular design & multi-layer abstraction, you can leverage the efficient patterns to build the system by parts, or chaining them into a Flow for an end-to-end experience.
Sentence Transformers
It provides an easy method to compute dense vector representations for sentences, paragraphs, and images. The models are based on transformer networks like BERT / RoBERTa / XLM-RoBERTa etc. and achieve state-of-the-art performance in various tasks.
FastText
It is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices.
Flair
Flair allows you to apply our state-of-the-art natural language processing (NLP) models to your text, such as named entity recognition (NER), part-of-speech tagging (PoS), sense disambiguation and classification.
Taylor AI
It is an API for high-accuracy text classification and entity extraction. We make your unstructured text as easy to work with as your tabular data.
Skimle
Skimle helps researchers, analysts and consultants turn interviews, reports and other qualitative data into structured insights. Upload your PDFs, documents, audio, video or other data and have our tool analyse them, extract themes and build a transparent and editable Skimle table showing insights for each category from each document with transparent quotes. You can edit and analyse the spreadsheet together with our AI tool and then export to your tool of choice including Word, Excel and Powerpoint slides. Skimle is used by consultants (e.g., due diligence), market researchers (e.g., group interview analysis), academics (e.g., thematic analysis), public sector (e.g., policy feedback analysis), legal professionals (e.g., litigation discovery) and other knowledge workers wanting to harness responsible AI to improve the depth and speed of their work.
