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- openNLP-vs-Stanford CoreNLP
Compare Stanford CoreNLP and openNLP 
- Users report that openNLP excels in its ease of use, with a G2 rating of 8.3, which matches the ease of use rating of Stanford CoreNLP, but reviewers mention that openNLP's user-friendly interface makes it more accessible for small businesses.
- Reviewers mention that Stanford CoreNLP has a slight edge in Named Entity Recognition, with a G2 rating of 9.2 compared to openNLP's 8.3, highlighting its superior accuracy in identifying entities in text.
- G2 users report that openNLP's quality of support is rated at 7.0, which is higher than Stanford CoreNLP's 6.7, indicating that users find openNLP more responsive and helpful when issues arise.
- Users on G2 mention that Stanford CoreNLP offers better sentiment analysis capabilities, with reviewers noting its advanced algorithms that provide more nuanced insights compared to openNLP.
- Reviewers say that openNLP shines in its customizable models, allowing users to tailor the software to their specific needs, while Stanford CoreNLP is noted for its robust pre-built algorithms that save time for users who prefer out-of-the-box solutions.
- Users report that both products have similar ease of setup ratings, but Stanford CoreNLP's 8.5 rating suggests it may be slightly easier to implement for users who are less technically inclined.
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...
There are more powerful tools out there like spaCy which use deep learning techniques to identify more information like context in a sentence.
Was working on a basic question-answering system using basic NLP methods compared to the Neural Nets. OpenNLP made my process much simpler with its wide variety of functions that are easy to use and when in trouble there is always an easily accessible...
Maybe the part of part-of-speech tagging or chunking and parsing it could be better.
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...
Was working on a basic question-answering system using basic NLP methods compared to the Neural Nets. OpenNLP made my process much simpler with its wide variety of functions that are easy to use and when in trouble there is always an easily accessible...
There are more powerful tools out there like spaCy which use deep learning techniques to identify more information like context in a sentence.
Maybe the part of part-of-speech tagging or chunking and parsing it could be better.
