Remember the last time you interacted with Alexa? You may have asked, “Alexa, where’s my order?” And Alexa has given you specific updates on your latest Amazon frenzy.
Programs like Alexa, which can understand human language, form a special area of artificial intelligence (AI) called natural language processing (NLP). NLP is broadly defined as the automatic manipulation of natural language, that is, speech and text transmitted by humans, by software. NLP allows computers to communicate with humans in their own language.
In the AI world, NLP falls under a field called machine learning (ML), which deals with educating a computer to learn and improve just like the human brain. Programming a computer for machine learning tasks such as NLP is done with software and training data.
For nearly five decades since the inception of AI in the 1950s, Lisp was almost the only programming language used for ML software. At the turn of the century, first Java, then Python, became firmly established in ML programming.
Over the past decade, Python has emerged as the programming language of choice for machine learning. Factors that have helped Python become the leader in the ML package include simplicity of syntax, flexibility, and platform independence. Also, since Python code reads like plain English, Python’s learning curve is not as steep as that of Lisp. While Lisp is still widely used for ML projects, it now plays the role of second violin behind Python.
Coding for Natural Language Processing (NLP)
Whether you are a beginner in ML or an expert ML programmer, you want to adopt Python as the programming language for your future ML projects. Python provides access to an array of excellent libraries and frameworks for AI and ML. A few such libraries are listed below.
- NumPy – used for scientific computing
- PyTorch – used to create deep learning projects
- Scikit-Learn – used for data mining and analysis
- SciPy – used for advanced computing
The aforementioned libraries can easily work with built-in AI frameworks like TensorFlow, Flask, CNTK, and Apache Spark.
All of these libraries and frameworks come with great online support on the Python discussion boards. Python Forum is an example of a very active Python community, while Kaggle is a popular forum for Python discussions related to NLP.
NLP projects aim to enable the interaction between computers and humans in human language. NLP projects therefore need software programmers to write code that guides the computer in how to interpret and respond to human-supplied text. Using a programming language like Python allows you to enjoy the benefits of built-in libraries with high quality ML algorithms that can greatly reduce your NLP coding, saving you time and energy.
Programming NLP technologies
Natural language processing works on deep learning, a machine learning model that uses artificial neural networks (ANNs) to mimic the workings of the human brain. Deep learning is necessary for NLP because it is impossible to preprogram a computer to process the responses for every possible set of input text. Instead, the computer must learn to determine the context of words and assess the feeling and intention of the human user.
More than other types of programming, working on an NLP application is an iterative task that you must delve into repeatedly to improve the program until it can interpret and manipulate human language well enough for the chosen task. This means repeatedly writing additional code, rewriting existing code, and training your deep learning model with better data sets.
Developing new NLP technology from scratch is prohibitively expensive. In addition to using predefined modules offered by programming languages, you can speed things up with an existing language model capable of interpreting and generating text.
Generative Pre-trained Transformer 3 (or GPT-3) is the cutting edge language model that has been trained with 175 billion parameters, and it interprets and generates text incredibly well. You can manage the operationalization of GPT-3 with Spell.
When choosing a programming language for your NLP project, keep the following points in mind:
Python offers immediate support for NLP. Two NLP-specific Python libraries are NTLK and SpaCy. NLTK is a good choice for learning and exploring NLP concepts, but it is slow and not suitable for production. SpaCy is a newer NLP library designed to be fast and production ready. Python also has the advantage of being more user-friendly and requiring fewer lines of code compared to Java. The disadvantages of Python include:
- Slow execution (since it is an interpreted language).
- Dynamic variables (which can generate runtime errors).
- Access layers to primitive databases.
Java package Apache OpenNLP, a library for natural language word processing, and Java Machine Learning Library (JavaML), a collection of machine learning algorithms. Java has a very rich API and, in general, offers better security than Python. A significant disadvantage of Java is the length and complexity of the code. The object-oriented nature of Java increases the complexity of the code, forcing you to go through many layers of code when trying to understand features or debug an error.
Lisp is the robust AI workhorse that has been used to solve many NLP problems. But Lisp’s learning curve is steep, in part because the semantics and syntax of the language are not user-friendly at all. Lisp also lacks the range of libraries for NLP that Python and Java offer.
NLP is considered one of the most difficult problems in computing and should always be used with human coders. This is mandatory because the combinations of all words in human language and the context of each word in each of these combinations lead to infinite meanings, feelings and intentions. Only a human programmer can adequately judge the performance of an NLP application.
NLP processes are centered around selective algorithms and training data sets, both of which are subject to iterative improvements. If an NLP program produces errors in tests, it is up to the programmer to decide whether the training dataset needs to be changed or if the algorithms used need to be re-encoded.
Problems specific to NLP programming abound. No machine learning program can handle ambiguity or sarcasm. If a Briton says “Great game! Referring to England’s defeat in the 2021 European Football Cup final, the program will take the words literally. NLP coding also suffers from a lack of visual context. A simple sentence like “Take that”. will not make sense for an NLP program because it cannot distinguish whether “it” refers to a glass of wine or a slap in the face.
The last hurdle that NLP encoding has to overcome, unlike normal encoding, is the need for a huge amount of computing power. The amount of data needed to perform NLP at the human level perfectly requires almost infinite memory space and processing capacity. Such resources remain beyond even today’s most powerful supercomputers.
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