# Stanford CoreNLP Reviews **Vendor:** Stanford NLP Group **Category:** [Natural Language Understanding (NLU) Software](https://www.g2.com/categories/natural-language-understanding-nlu) **Average Rating:** 4.3/5.0 **Total Reviews:** 10 ## About Stanford CoreNLP Stanford CoreNLP provides a set of natural language analysis tools that can give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, and mark up the structure of sentences in terms of phrases and word dependencies, indicate which noun phrases refer to the same entities, indicate sentiment, extract open-class relations between mentions, etc. ## Stanford CoreNLP Reviews ### 1. Develop a Working Understanding of Natural Langauge Processing **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Online Media | Small-Business (50 or fewer emp.) **Reviewed Date:** April 25, 2019 **What do you like best about Stanford CoreNLP?** The Stanford Parser is an easy introduction to natural language processing (NLP). The program uses a combination of approaches to identify and tag both the individual components (syntax) within a sentence and to accurately assign the relationship between the words (semantics). Users can download the Java based version of the program, or experiment with it on their website. **What do you dislike about Stanford CoreNLP?** The Stanford Parser is one of many natural language parsers available on the market. I prefer Stanford for its ease and accessibility. The use of a recurrent neural network may produce greater results for someone working in a highly technical and linguistically complex environment, where immediacy and accuracy are equally weighted. **What problems is Stanford CoreNLP solving and how is that benefiting you?** Developing an interactive knowledge base that exceeds the limits of a preset FAQ is essential for customer engagement. The Stanford Parser provides the foundation for generating the framework for that interaction. ### 2. Natural Language parser with an ivy league touch **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Management Consulting | Mid-Market (51-1000 emp.) **Reviewed Date:** January 30, 2019 **What do you like best about Stanford CoreNLP?** The Stanford Parser is a natural Language parser that doesn't require a ivy league degree to use; plus it is free; which is a huge plus; I use it surprising more than you would think, as i am currently trying to use it to feed langue into a Machine Learning Data Structure; with the ultimate goal of creating a better chat bot **What do you dislike about Stanford CoreNLP?** Although built on solid foundations; the User Interface is very 1990's / Early 2000's; If the GUI was re-designed or even updated to modern standards, i feel it would benefit greatly. **Recommendations to others considering Stanford CoreNLP:** Just use it; its free to use and it helps immensely just to jump right in and being playing around with it; this will get you accumulated to what the program can do, as well as what you can apply it to in your business. I would say that even if you don't think you need a program like this; download it; or use the web interface; and try it. You might be surprised to see its application is vast and extremely useful. **What problems is Stanford CoreNLP solving and how is that benefiting you?** Building algorithms, Machine Learning or otherwise, is extremely complex; Stanford made this resource available to all which is great, We are able to use this program in a surprising amount of ways; from your typical neural network / learning algorithms; to speech analysis and even analyzing your own emails or proposals before sending out to clients to create a more efficient response. ### 3. Excellent, easy to use POS tagger **Rating:** 5.0/5.0 stars **Reviewed by:** Saurabh J. | Print Server Manager, Mid-Market (51-1000 emp.) **Reviewed Date:** April 08, 2019 **What do you like best about Stanford CoreNLP?** The amount of options that Stanford NER provides means you'll never go anywhere else for any kind of NER tasks **What do you dislike about Stanford CoreNLP?** The lack of good support of non-English languages **Recommendations to others considering Stanford CoreNLP:** Get started with the online demo to determine whether it's for you **What problems is Stanford CoreNLP solving and how is that benefiting you?** We've used Stanford NER to mine and tag incoming textual data to generate media rich representations of the input. This has led to a more sophisticated interface and taken us one step closer to making our platform smart ### 4. The simplest tokenizer to implement for NLP problems **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Computer Software | Enterprise (> 1000 emp.) **Reviewed Date:** February 01, 2019 **What do you like best about Stanford CoreNLP?** Ease of use and implementation and works effectively in most cases. Open source license and straightforward algorithm. **What do you dislike about Stanford CoreNLP?** There are more powerful tools out there like spaCy which use deep learning techniques to identify more information like context in a sentence. **What problems is Stanford CoreNLP solving and how is that benefiting you?** Tokenize OCR data to pre-process and pass to machine learning models. Works fast and is accurate for real time applications. ### 5. Java implemented NLP API for Named Entity Recognization by Stanford!! **Rating:** 3.5/5.0 stars **Reviewed by:** umesh s. | Software Engineer, Information Technology and Services, Small-Business (50 or fewer emp.) **Reviewed Date:** May 14, 2018 **What do you like best about Stanford CoreNLP?** it's an open source and very easy to use this library in java , it splits the sentence and gives the words(entity) as a result which actually makes sense like person,location etc , for using it into the java, 1) we need to import edu.stanford.nlp.* and then 2) we have to set all the properties which we want to list. 3) then we have to create text document and pass it to the StanfordCoreNLP's annote () method. and you'll get all the Entities present in your text or document **What do you dislike about Stanford CoreNLP?** this project is evolving right now so it's not true that you'll get accurate result for every scenario all the time!! **Recommendations to others considering Stanford CoreNLP:** It's highly trained and very easy to use and little bit less accurate Named Entity recognition API for java. **What problems is Stanford CoreNLP solving and how is that benefiting you?** we use it for Spam email classification on the emails which are being sent on our companies helpline email id and we need to process 10 to 20 emails every minute!! ### 6. Useful tool for social scientists assessing textual topic models **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Public Policy | Small-Business (50 or fewer emp.) **Reviewed Date:** June 17, 2018 **What do you like best about Stanford CoreNLP?** Ability to train a topic model since most textual analysis programs I have used does not have the utility to train a program to be specific to a particular dataset. **What do you dislike about Stanford CoreNLP?** Not as effective for small sample sized texts. Since the program's primary focus is on training topic models, there is not an effective amount of analysis on smaller documents, which makes programs with built in textual analysis (such as sentiment based, natural language processing) more useful. **Recommendations to others considering Stanford CoreNLP:** This is particularly useful for large text documents so please ensure that your data set is large and has a big textual component **What problems is Stanford CoreNLP solving and how is that benefiting you?** The STM toolbox has allowed to me to perform analyses on data sets that are specific to that data. It is different than other textual analysis programs in that inferences can be made depending on the topic learning model used for the specific topic model. It also has a lot of built in functions that make cutting a data set, such as removing empty documents, that make performing analyses easier and less work for the user. Thus, this is particular useful for data sets with a large text component. ### 7. A pretty good collection of NLP tools and libraries **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Internet | Enterprise (> 1000 emp.) **Reviewed Date:** October 17, 2018 **What do you like best about Stanford CoreNLP?** It has the most common, and even some uncommon, algorithms implemented. And the best part is, they are in Java! **What do you dislike about Stanford CoreNLP?** I think documentation can be a little difficult to use. But still much better than many other ML libraries. **Recommendations to others considering Stanford CoreNLP:** Definitely give it a try before implementing algorithms yourself. You can generally find a good approximation of what you are searching. If not, it still has some good modules to get you started. **What problems is Stanford CoreNLP solving and how is that benefiting you?** I used it for corpus analysis, text classification, and recommendations. ### 8. favorite tokenizer **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Research | Small-Business (50 or fewer emp.) **Reviewed Date:** August 14, 2018 **What do you like best about Stanford CoreNLP?** I have been using Stanford tokenizer for six years and I love it. It's easy to integrate with any application and can recognize special character like ",", "$" etc. It also has the functionality of removing token matched with some regex. It also has a variety of configuration according to the user's requirements. **What do you dislike about Stanford CoreNLP?** It converts bracket to other symbols e.g. LCB-, -LRB-, -RCB-, -RRB which sometimes require extra processing later. **What problems is Stanford CoreNLP solving and how is that benefiting you?** NLP related problems. ### 9. To discover **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in E-Learning | Small-Business (50 or fewer emp.) **Reviewed Date:** June 21, 2018 **What do you like best about Stanford CoreNLP?** Its level of ease, the code, and clear new product to make known to the general public and productive. **What do you dislike about Stanford CoreNLP?** The file extraction is rather slow. **Recommendations to others considering Stanford CoreNLP:** I strongly recommend Stanford TokensRegex to all enthusiasts of business structure. **What problems is Stanford CoreNLP solving and how is that benefiting you?** The ease of time, I lose less time working with Stanford TokensRegex ### 10. The go to NLP Parser **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Civic & Social Organization | Small-Business (50 or fewer emp.) **Reviewed Date:** May 12, 2018 **What do you like best about Stanford CoreNLP?** I've used the Stanford NLTK parse is various natural language processing projects. It works very well, documentation is good enough. **What do you dislike about Stanford CoreNLP?** There is nothing I would specifically call out about the Stanford NLP Parsing tools. **What problems is Stanford CoreNLP solving and how is that benefiting you?** Parsing and understand text and words in their context. - [View Stanford CoreNLP pricing details and edition comparison](https://www.g2.com/products/stanford-corenlp/reviews?section=pricing&secure%5Bexpires_at%5D=2026-06-01+02%3A04%3A07+-0500&secure%5Bsession_id%5D=d36d6d73-1855-43df-91b9-9f9d14d00f7c&secure%5Btoken%5D=deba116a1db4e5499c501c529d03fe0381e2c5d0f3b784a2467d5368e8f46589&format=llm_user) ## Stanford CoreNLP Features **Algorithm** - Part of Speech Tagging - Summarization - Named Entity Recognition - Sentiment Analysis - Emotion Detection - Language Detection **System** - Data Ingestion & Wrangling - Programming Language Support - Drag and Drop - Pre-Built Algorithms - Customizable Models ## Top Stanford CoreNLP Alternatives - [openNLP](https://www.g2.com/products/opennlp/reviews) - 4.0/5.0 (11 reviews) - [NLTK](https://www.g2.com/products/nltk/reviews) - 4.4/5.0 (46 reviews) - [Google Cloud Translation API](https://www.g2.com/products/google-cloud-translation-api/reviews) - 4.4/5.0 (325 reviews)