Nowadays computers are excellent at working with structured and standardized data like financial records and data tables. They are experts in handling data more quickly than humans. Usually, humans are communicated using unstructured data with the help of words. But computers don’t interact with unstructured data as can communicate with structured data by using the binary language.
Regrettably, computer systems suck at working with unstructured data because there are no uniform techniques to handle it. We can operate computer systems by structured programming languages like Java, C, C++, Python etc. with natural languages, humans communicate with computers.
Natural language processing
Natural language processing (NPL) is an area of artificial intelligence and computer science concerned with the interaction between humans and computers. The primary goal of NLP is to allow computers to understand language like humans. It is the super official power behind the features like speech recognition, machine translation, virtual assistants, automatic text summarization, sentiment analysis, etc. There is a broad variety of open source tools for NLP. In this article, we are going to discuss the Top Open source tools for Natural language processing.
Python tools
Natural Language Toolkit (NLTK)
Natural language toolkit is one of the full-featured ones, which appliances all types of NLP components required like tokenization, tagging, classification, stemming, semantic reasoning and parsing. And for each one having more than one implementation, we can elect the exact methodology and algorithm you’d like to use, and it supports different languages. It represents the data in the form of strings. Though it is best for simple constructs but is not suitable for advanced functionality. The library of NLTk is also a bit slow compared with others. Entirely this is the best toolkit for exploration, experimentation, and application that requires a specific mixture of algorithms.
Textacy
Textacy is one of the python tools used for performing natural language processing tasks, and it builds on the high-performance python library, i.e., Spacy library. In addition to essentials like part of speech tagging, POS-tagged, tokenization, parsing- uploaded to other library and is mainly focuses on the availability of tokenized tasks and parsed text- readability statistics, quotation attribution, key term extraction, emotional valence analysis, etc.
SpaCy
Spacy is one of the free open source tools for natural language processing in Python. NLTK is the primary opponent to the SpaCy library. In most of the cases SpaCy is faster, but it has a unique execution in every NLP components, illustrates everything as an object instead of the string, and It simplifies the interact of building applications. Additionally, it helps in integrating the other data science tools and frameworks. Thus, allowing you to work and have an excellent knowledge of your text data. Though it doesn’t support as many languages as NLTK and it has an easy interface with documentation and set of choices along with a lot of neural models for different components of analysis and natural language processing. Overall, this is a beautiful tool for the latest applications that need to be efficient in production and doesn’t require any specific algorithm.
# pip install space
import spacy
# Load English parser,word vectors,tagger,NER and tokenizer
min = spacy.load("en_core_web_sm")
# Process whole documents
Topic = ("Mindmajix is one of the worlds largest IT online training provider which connects students and corporates with trainers across the globe. It provides online training of more than 500 courses. Mindmajix provides all the facilities you need to get a job, right from the training to other kinds of support such as resume preparation, providing training materials, on job support. Mindmajix is the one stop solution to get into the IT field.")
rec = min(topic)
# Analyze syntax
print("Noun phrases in chunk:", [chunk.text for rec.noun_chunks])
print("Verbs in token:", [token.lemma_ for rec if token.pos_ == "VERB"])
# Finding concepts, named entities and phrases
for entity in rec.ents:
print(entity.label_, entity.topic)
TextBlob
TextBlob is a python library tool and extension of NLTK. It provides a simple API approach to its methods and executes a large number of NLTK functions, and it also includes the pattern library functionality. You are just at the beginning, this might be an excellent tool to learning, and we can use it in applications production those don’t require heavy performant. TextBlob libraries are similar to python strings, so we can quickly transform and play similarly we performed in python. Finally, TextBlob is used in everywhere, and it is best suitable for smaller projects. See here some basic tasks, gives an idea about how TextBlob is related to python strings.
String1 = TextBlob(“Mindmajix”) String1[1:4] ### extracting 1 to 4 letters TextBlob(“ind”) String1. upper() ## to upper case the entire text TextBlob(“MINDMAJIX”) String2 = TextBlob(“Technologies”) ## concat two sentences similar as python String1 + “ “ + string2 TextBlob(“Mindmajix Technologies”)
PyTorch-NLP
PyTorch-NLP is an excellent python library for quick prototyping. It’s updated with the latest research, researchers and apex companies have released many tools to perform total sorts of amazing processing, like image conversations. Usually, PyTorch is aimed at researchers, but it should also be used for workload production and prototypes with most sophisticated algorithms. This library is used primarily for neural network layers, datasets, and text processing modules.
E.g., apply neural network layer for simple recurrent unit
From torchnlp.in import torch import SRU entry_= torch.autograd.Variable(torch.randn(4, 1, 8)) su = SRU(10, 20) # Apply SRU to `entry_` su(entry_) # RETURNS: ( # output [torch.FloatTensor (4x1x20)], # hidden_state [torch.FloatTensor (1x3x20)] # )
Node Tools
Retext
Retext is one of the Node Tools, and is a part of a unified collective. It is an interface that permits multiple plugins and tools to incorporate and collaborate effectively. The unified tool uses various syntaxes- Retext is one of them. The others are Rehype for HTML and Remark for markdown. Retext doesn’t show a lot of fundamental techniques, and preferably uses plugins to achieve the outcomes for NLP. It is easy to execute things like detecting sentiment, spelling checking, fixing typography. Overall, it is a superb tool if you need to obtain something done without having any knowledge of the underlying process.
Natural
Natural is also one of the most popular Node tools; it includes many functions you might expect in NLP. It mainly focuses on English along with it have been contributed to some other languages also. It supports phonetics, tokenizing, WordNet, string similarity, stemming, classification, frequency document, and some inflexions. It is very similar to NLTK and tries to include all in a single package, and it is effortless to use. Finally it is a vast library but still, it is a development stage, and it requires some extra knowledge for implementation of the underlying process.
Java tools
OpenNLP
Apache OpenNLP library is hosted by Apache foundation, which is an open source Java tool, used to handle the Natural Language Processing(NLP). It provides various kind of services like speech tagging, tokenization, chunking, named entity, sentence segmentation, and reference solutions. We can easily connect OpenNLP with other Apache tools like Apache NiFi, Spark and Apache Flink. It is one of the general NLP tools and is used to cover the standard processing components, and we can use it as a library in an application or used as a command line. It is one of the powerful libraries in Java tools, and it supports many languages.
CogCompNLP
CogCompNLP is one of the Java tools developed by Illinois University. It is the same as python library with similar functionality. With these, we can process the text, whether it is local or remote systems, with this process local devices are burden free. It offers processing functions like lemmatization, speech tagging, tokenization, dependency parsing, chunking, entity name tagging, semantic role labeling, and constituency parsing. For research, this is one of the greatest, and we can explore a lot of components from this tool.
Conclusion
The above-listed libraries are the leading open source tools for Natural Language Processing. The user using these tools can communicate with computers through speech recognition, virtual assistants, machine translation, sentiment analysis, automatic text summarization, etc. Additionally, there are distinct numbers of other libraries designed to solve distinct NLP problems and to interact with computers.