Tools And Libraries Associated With Natural Language Processing

NLP

Natural Language Processing allows us the have a clearer understanding of the functions of language as well as providing valuable comprehension. Within the world of business, it can be used for data analytics, value proposition and user interface optimization.

It wasn’t until the late 90s that NLP software started to make an appearance. Several various custom text analytics showed up on the Internet and that is when people started to appreciate their use. Today there are vast amounts of NLP software and they can be used for different reasons.

While it is difficult to decide which NLP tool is going to be most suitable for your project, we are going to take a look at 8 of the best NLP tools and libraries to show you their use cases and features.

NLTK- Natural Language Toolkit

This is a great piece of software for beginners as it is an entry-level open-sourced NLP. It offers a basic set of tools for operations related to texts and is powered with Python NLP libraries. Its main purpose is for education and research. NLKT features include:

  • Text classification
  • Part of speech tagging
  • Entity extraction
  • Tokenization
  • Parsing
  • Stemming
  • Semantic reasoning

This is an ideal source for understanding customer activities, opinions and/or feedback due to interfaces including text corpora. In addition, there are lexical resources like Penn Treebank Corpus, Open Multilingual Wordnet, Problem Report Corpus and Lin’s Dependency Thesaurus.

While NLTK is great for straightforward text analysis, you may want to try other options if you have huge amounts of data.

Stanford CoreNLP- Data and Sentiment analysis plus Conversational UI

This NLP is a multi-purpose tool. Stanford CoreNLP is similar to NLTK as it offers numerous natural language processing software but with Sanford, you can add more by using custom modules.

Stanford CoreNLP is better when it comes to scalability so it will handle large quantities of information and you can run complex operations on it. Because of this, Stanford CoreNLP is great for:

  • Information scraping from open sources such as social media
  • Sentiment analysis from customer support
  • Conversational interfaces with chatbots
  • Text processing and generation, useful for customer service and e-commerce

You will like this tool for its ability to take out such a variety of information and its simple corrections of terms and sentences.

Apache OpenNLP- Data and Sentiment analysis

If you are looking for a tool for the long run, accessibility is going to be crucial. This is a difficult feat when it comes to NLP open source tools because software may have all the necessary features but then become too complicated for use.

Apache OpenNLP is for those who prefer accessibility and practical use. It is an open source library and similarly to Stanford CoreNLP, it operates with Java NLP libraries with Python decorators. It may not have as many additional features as Stanford or NLTK but this makes using it very easy while still being a handy tool. You are able to customize OpenNLP to eliminate anything you don’t find necessary. Here is why OpenNLP is good for you:

  • Named Entity Recognition
  • Sentence Detection
  • POS tagging
  • Tokenization

Open NLP works wonders on text data analysis as well as operations involving sentiment analysis. It can be used to prepare text corpora for text generators and chatbots (conversational interfaces).

SpaCy- Data Extraction, Data and Sentiment Analysis, Text Summarization

Spacy is another open-source NLP but aimed more specifically at businesses. It is perfect for comparing customer or product profiles and text documents and it does so in a smooth, quick and efficient way. It is great at deep text and sentiment analysis.

We like how Spacy can analysis syntactic to give the user more insight into sentiment analysis and conversational user interface optimization. The advantages continue with its named-entity recognition and compatibility with word2vec and doc2vec.

Unlike the previous NLPs we have mentioned. Spacy has the functions combined which means you don’t have to choose modules by yourself. You simply design your frameworks from building blocks already in place.

AllenNLP- Text and Sentiment analysis

AllenNLP is more advanced than others mentioned so far due to the fact that it is built on PyTorch tools and libraries and works very well at data research and business applications. While AllenNLP controls most of its own processes, it does use Spacy open-source library for data pre-processing.

This is a perfect tool for those who don’t have much experience because it is simple and you won’t feel like you are drowning in data output.

You can use AllenNLP for:

  • Advanced conversational interface
  • Customer support
  • Lead generation via website chat

You are promised clear and comprehensive text generation with its textual entailment model which can be used for multi-source text summation or even just straightforward user-bot interaction. If your area if marketing and promotion, you will love Event2Mind, a model that lets you explore consumer behavior.

GenSim- Document Analysis, Semantic Search, Data Exploration

Gensim is the ideal tool if you need to find out specific business insights. It has been created with the idea of exploring documents and topic modeling, assisting you to steer your way through multiple databases and documents.

It is an open-source library and the main focus here is GenSim classifying content of documents based on the vectors and clusters it sees. It handles a large amount of data with ease.

The principal use cases fro GenSim are:

  • Data analysis
  • Semantic search applications
  • Text generation applications like chatbox or text summarization

TextBlob Library- Conversational UI, Sentiment analysis

TextBlob is based on NLTK but is the fastest NLP tool. Again, it is open-sourced and can be enhanced with additional features if you are looking for more detailed text analysis.

By using conversational interfaces, you can analysis sentiment to look for where the customer’s interest is captured, then design a model with utmost verbal skills. It also has language text corpora so you can use it for translation.

Other features include basic NP text analysis, event extraction and intent analysis features. As part of the sentiment analysis, TextBlob has different levels so you can work using timelines to see things as they progress.

Intel NLP Architect- Data Exploration, Conversational U13

This might be the baby of the group but it is as equally productive as the others. Python libraries are used for deep learning, It can also be used for text generation and summarization. Intel NLP will focus on certain aspects while analyzing sentiments and not to mention conversational interfaces like chatbots.

Something that will get you motivated is the Machine Reading Comprehension, looking at the text as multi-layered, adapting the style and format to make it suitable depending on the input information. This makes your service more personal.

Architect NLP is more advanced than other NLPs and the Term Set Expansion helps! Term Set Expansion or TSE will add on other relevant search options. If you search Siri, it will automatically add Cortana or Amazon Echo to your list.

Conclusion

Natural Language Processing tools are there to analyze texts and allow you to use the information to make useful business decisions.

There are so many different ones available but it is important that you choose one that will suit your next project- not your business. It is the details of the project that need to suit the NLP.

Tools And Libraries Associated With Natural Language Processing

Natural Language Processing allows us the have a clearer understanding of the functions of language as well as providing valuable comprehension. Within the world of business, it can be used for data analytics, value proposition and user interface optimization.

It wasn’t until the late 90s that NLP software started to make an appearance. Several various custom text analytics showed up on the Internet and that is when people started to appreciate their use. Today there are vast amounts of NLP software and they can be used for different reasons.

While it is difficult to decide which NLP tool is going to be most suitable for your project, we are going to take a look at 8 of the best NLP tools and libraries to show you their use cases and features.

NLTK- Natural Language Toolkit

This is a great piece of software for beginners as it is an entry-level open-sourced NLP. It offers a basic set of tools for operations related to texts and is powered with Python NLP libraries. Its main purpose is for education and research. NLKT features include:

  • Text classification
  • Part of speech tagging
  • Entity extraction
  • Tokenization
  • Parsing
  • Stemming
  • Semantic reasoning

This is an ideal source for understanding customer activities, opinions and/or feedback due to interfaces including text corpora. In addition, there are lexical resources like Penn Treebank Corpus, Open Multilingual Wordnet, Problem Report Corpus and Lin’s Dependency Thesaurus.

While NLTK is great for straightforward text analysis, you may want to try other options if you have huge amounts of data.

Stanford CoreNLP- Data and Sentiment analysis plus Conversational UI

This NLP is a multi-purpose tool. Stanford CoreNLP is similar to NLTK as it offers numerous natural language processing software but with Sanford, you can add more by using custom modules.

Stanford CoreNLP is better when it comes to scalability so it will handle large quantities of information and you can run complex operations on it. Because of this, Stanford CoreNLP is great for:

  • Information scraping from open sources such as social media
  • Sentiment analysis from customer support
  • Conversational interfaces with chatbots
  • Text processing and generation, useful for customer service and e-commerce

You will like this tool for its ability to take out such a variety of information and its simple corrections of terms and sentences.

Apache OpenNLP- Data and Sentiment analysis

If you are looking for a tool for the long run, accessibility is going to be crucial. This is a difficult feat when it comes to NLP open source tools because software may have all the necessary features but then become too complicated for use.

Apache OpenNLP is for those who prefer accessibility and practical use. It is an open-source library and similarly to Stanford CoreNLP, it operates with Java NLP libraries with Python decorators. It may not have as many additional features as Stanford or NLTK but this makes using it very easy while still being a handy tool. You are able to customize OpenNLP to eliminate anything you don’t find necessary. Here is why OpenNLP is good for you:

  • Named Entity Recognition
  • Sentence Detection
  • POS tagging
  • Tokenization

Open NLP works wonders on text data analysis as well as operations involving sentiment analysis. It can be used to prepare text corpora for text generators and chatbots (conversational interfaces).

SpaCy- Data Extraction, Data and Sentiment Analysis, Text Summarization

Spacy is another open-source NLP but aimed more specifically at businesses. It is perfect for comparing customer or product profiles and text documents and it does so in a smooth, quick and efficient way. It is great at deep text and sentiment analysis.

We like how Spacy can analysis syntactic to give the user more insight into sentiment analysis and conversational user interface optimization. The advantages continue with its named-entity recognition and compatibility with word2vec and doc2vec.

Unlike the previous NLPs we have mentioned. Spacy has the functions combined which means you don’t have to choose modules by yourself. You simply design your frameworks from building blocks already in place.

AllenNLP- Text and Sentiment analysis

AllenNLP is more advanced than others mentioned so far due to the fact that it is built on PyTorch tools and libraries and works very well at data research and business applications. While AllenNLP controls most of its own processes, it does use Spacy open-source library for data pre-processing.

This is a perfect tool for those who don’t have much experience because it is simple and you won’t feel like you are drowning in data output.

You can use AllenNLP for:

  • Advanced conversational interface
  • Customer support
  • Lead generation via website chat

You are promised clear and comprehensive text generation with its textual entailment model which can be used for multi-source text summation or even just straightforward user-bot interaction. If your area if marketing and promotion, you will love Event2Mind, a model that lets you explore consumer behavior.

GenSim- Document Analysis, Semantic Search, Data Exploration

Gensim is the ideal tool if you need to find out specific business insights. It has been created with the idea of exploring documents and topic modeling, assisting you to steer your way through multiple databases and documents.

It is an open-source library and the main focus here is GenSim classifying content of documents based on the vectors and clusters it sees. It handles a large amount of data with ease.

The principal use cases fro GenSim are:

  • Data analysis
  • Semantic search applications
  • Text generation applications like chatbox or text summarization

TextBlob Library- Conversational UI, Sentiment analysis

TextBlob is based on NLTK but is the fastest NLP tool. Again, it is open-sourced and can be enhanced with additional features if you are looking for more detailed text analysis.

By using conversational interfaces, you can analysis sentiment to look for where the customer’s interest is captured, then design a model with utmost verbal skills. It also has language text corpora so you can use it for translation.

Intel NLP Architect- Data Exploration, Conversational U13

This might be the baby of the group but it is as equally productive as the others. Python libraries are used for deep learning, It can also be used for text generation and summarization. Intel NLP will focus on certain aspects while analyzing sentiments and not to mention conversational interfaces like chatbots.

Something that will get you motivated is the Machine Reading Comprehension, looking at the text as multi-layered, adapting the style and format to make it suitable depending on the input information. This makes your service more personal.

Architect NLP is more advanced than other NLPs and the Term Set Expansion helps! Term Set Expansion or TSE will add on other relevant search options. If you search Siri, it will automatically add Cortana or Amazon Echo to your list.

Conclusion

Natural Language Processing tools are there to analyze texts and allow you to use the information to make useful business decisions.

There are so many different ones available but it is important that you choose one that will suit your next project- not your business. It is the details of the project that need to suit the NLP.