Having used the parser for an introduction to natural language processing, this reviewer appreciates its ease of use and accessibility. The reviewer notes that for more complex linguistic tasks, a recurrent neural network might be more effective.
View Full ReviewStanford CoreNLP Reviews (10)
Stanford CoreNLP Reviews (10)
What do users say?
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 Review collected by and hosted on G2.com.
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. Review collected by and hosted on G2.com.
The amount of options that Stanford NER provides means you'll never go anywhere else for any kind of NER tasks Review collected by and hosted on G2.com.
The lack of good support of non-English languages Review collected by and hosted on G2.com.
Ease of use and implementation and works effectively in most cases. Open source license and straightforward algorithm. Review collected by and hosted on G2.com.
There are more powerful tools out there like spaCy which use deep learning techniques to identify more information like context in a sentence. Review collected by and hosted on G2.com.

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 Review collected by and hosted on G2.com.
this project is evolving right now so it's not true that you'll get accurate result for every scenario all the time!! Review collected by and hosted on G2.com.
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. Review collected by and hosted on G2.com.
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. Review collected by and hosted on G2.com.
It has the most common, and even some uncommon, algorithms implemented. And the best part is, they are in Java! Review collected by and hosted on G2.com.
I think documentation can be a little difficult to use. But still much better than many other ML libraries. Review collected by and hosted on G2.com.
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. Review collected by and hosted on G2.com.
It converts bracket to other symbols e.g. LCB-, -LRB-, -RCB-, -RRB which sometimes require extra processing later. Review collected by and hosted on G2.com.
Its level of ease, the code, and clear new product to make known to the general public and productive. Review collected by and hosted on G2.com.
The file extraction is rather slow. Review collected by and hosted on G2.com.
I've used the Stanford NLTK parse is various natural language processing projects. It works very well, documentation is good enough. Review collected by and hosted on G2.com.
There is nothing I would specifically call out about the Stanford NLP Parsing tools. Review collected by and hosted on G2.com.
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