NLTK Reviews & Product Details

It has added more support for NLP and made it more powerful by integrating third-party libraries. The main issue it has fixed for me is text processing. Review collected by and hosted on G2.com.

NLTK is a great tool for Natural Language Processing tasks. The documentation provided is easy to understand with ample of examples. I would certainly recommend it to the beginners who are starting with their Machine Learning journey, as well as the experienced folks in ML industry. Review collected by and hosted on G2.com.

I have used this library primarily for text cleaning in the pre-processing stage and feature extraction before building ML model. While working on Hackathon problems, I tend to experiment with various functionalities of NLTK toolkit for effective processing of the text data. The best part is most of these tools offered by NLTK is quiet easy to implement, mostly can be achieved with a single line of code. Whenever I am stuck, I refer the official NLTK website and public forums and I always able to find the workaround. Review collected by and hosted on G2.com.

- I use it for reading data from a webhook call and figuring out the keywords after parsing using NLTK. Storing these keywords in MongoDB later using them for targeting and analysing. Review collected by and hosted on G2.com.

I used it when I was working for my fiver client in an image recognition project to preprocess and get sample test cases. Review collected by and hosted on G2.com.

Its solving out language proceaaing problem that we have faced before python comes in phase. Review collected by and hosted on G2.com.

I used it in my deep learning project where I needed NLTK for some preprocessing like tokenization , parking striling etc. Review collected by and hosted on G2.com.

I recommend the use of NTLK everytime you have to deal with text and you have to perform every type of natural language processing related analysis. It offers easy APIs to deal with every language. You can leverage wordnet api's to calculate synsets distances and related measures. It supports also part of speech tagging , machine translation , dependency parsing and sentiment analysis API's. You can also use neural networks for some specific tasks . Review collected by and hosted on G2.com.

I'm dealing with general natural language processing tasks , such as text mining , text classification and topic modeling.

I used nltk textcat to identify and split my data according to detected languages . Then the whole pre processing pipeline was implemented in NLTK. I used wordnet api in order to instantiate a wordnet lemmatizer and used it in conjuction with the pos tag api in order to use lemmatization enriched by pos tagging . Review collected by and hosted on G2.com.

Before using nltk, you should check if all the functionalities required for you are available for your language. The language with most applications is English, so you need to know if you can apply similar processing in your language.

On the other hand, the way of calling to some functionalities depends on the language because some models are more effective for some language than for another.

I also recommend checking the performance of the functionality you want to use. That is, as it is already known in natural language processing, the precision in some tasks is close to 100, while in others it is lower. Review collected by and hosted on G2.com.

I have used nltk to apply previous processing to input texts. For example, nltk helps me to remove stop words, in any language, as well as obtaining lemmans and stems from every word. Besides, I can used also ntlk for name entity recognition in several languages. Thus, I can obtain several features from a single text.

One of the main benefits of using nltk is the fact that the processing is quite fast. Moreover, I like the fact that I can find lots of information on the Internet, with several examples. This helps me a lot when I want to process texts using nltk. Review collected by and hosted on G2.com.

Yes I would totally recommend NLTK for others want to get into natural language processing as it is fairly easy to setup and learn. Review collected by and hosted on G2.com.

The main benefit I have realized is quick prototyping with help of NLTK. It helps me to give actual shape to my projects in no time.

Another benefit for NLTK is it is very easy and quick to learn so that I can bring my whole team in same page very fast and that really helps in quick research and development. Review collected by and hosted on G2.com.

Spacy is also one of the good packages for processing and performing NLP task Review collected by and hosted on G2.com.

I used NLTK majorly for pre-processing in Natural Language Processing tasks such as review classification then each review is in the raw form. Hence we have to perform processing and make it a useful vector.

Perfect library for basic processing on the text like stop-word removal, tokenization, etc. Review collected by and hosted on G2.com.