Main Approaches to NLP
In order to comprehend and interpret human language, NLP adopts 3 essential approaches to execute NLP tasks. All the 3 approaches are well-recognized and are widely used across numerous segments. The evolutionary approaches have standalone benefits and continue to aid NLP tasks to deliver the best results.
This system is flexible to develop and can always allow you to verify whether a query can be processed or how can it be done. For instance; by using an extension of translation rules and a good synonym’s base, it can easily be updated to perform with new functions and data types; with no major change to the core system.
A rule-based approach is perfect to acquire a specific language phenomenon: it efficiently decodes the linguistic relationship between words to translate the sentence.
• It is easily achieved through a focus on pattern-matching or parsing. • It can be counted as the ‘fill in the blanks’ method.
• It offers high-performance cases when used specifically but fails to impress when generalized.
Hence, it’s vital to make a good choice for query analysis that’s meant to perform.
The machine learning approach comprises probabilistic modeling, likelihood maximization as well as linear classifiers. As the results are based on probability phenomena, it holds fewer guarantees which cannot observe complicated text completely and struggles through the butterfly effect i.e. A very small change or a small amount of new data can drastically alter or result in a different outcome, leading to the system to act unpredictably even to its author. It’s ideal for a task-based activity like document classification or word clustering which has a lot of data points and helps machines to learn from its statistical clues of the word given in the task itself.
• Feature engineering – capitalized, singulars, word type, surrounding words, etc.
• A general ML system of a training set or training a model on defined parameters, followed by fitting on test data. • Applying model to test data or inference which is characterized by finding most probable words, next word, best category, etc.
• ‘Semantic slot filling’ tags the words or tokens which carry meaning to the sentences or translate utterances to logical form.
Both approaches are widely used but have limitations and drawbacks too. The rule-based approach needs experts to create the flexi rules and operate while the probabilistic method and inference-driven model require better training corpus to enhance its knowledge and deliver steadfastly.
Here’s is a list of the pros and cons of both approaches that have led to a hybrid approach for better results.
Machine learning is based on predictions and past observations while deep learning works on correctly representing the data to predict upon. Basically, it involves different approaches. The human visual system is undoubtedly one of the wonders of the world. It is incredible at making sense of what our eyes show us as a supercomputer in our brain does most of the processing unconsciously. The difficulty in visual pattern recognition like deciphering a handwritten digit becomes apparent if you attempt to write a computer algorithm as intuition is not as easy to express as precise rules. Neural networks approach the problem differently. It takes a large pool of handwritten numbers, called training examples, and then builds a system that can learn from those training examples.
To put it simply, the neural network uses the training examples to automatically infer rules for the recognition of handwritten digits. Also, by increasing the quantity or sheer volume of numbers for creating training examples, the network can learn more about handwritten digits, improving their accuracy. For e.g., if the sample is just 100 training digits first, perhaps one can create a better handwriting recognizer by using tens, thousands, or even billions of training examples.
A neural network is like machine learning in myriad ways.
• One of the major benefits for neural networks is that feature engineering is generally skipped, as networks will learn crucial features.
• Instead, streams of raw parameters (‘words’ – vector representations of words) without engineered features are fed into neural networks.
• It has a massive training corpus or training examples that assure better accuracy specific neural networks of use in NLP include Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs).
CNN has a dedicated architecture that surpasses at extracting local patterns in the data which are sensitive to the word order. These can be used in identifying indicative phrases of words or idioms of up to a fixed length in long sentences/documents.
For sequential data, RNN is the specialized model. It is employed as an input transformer which is trained to produce informative representation for the CNN to operate on top of it. They allow the abandoning of assumptions by taking into consideration entire sentences while taking word order into account when it is needed. This capability leads to extraordinary gains in language modeling as well as predicts the probability of the next word in the sequence. Recursive networks extend recurrent networks from sequence to trees. Such models form the core of state-of-the-art translations.
Today, the task of language predictions is interconnected. Growth in technology helps to predict efficiency and flawlessness, from word to sequence. Tree with neural network approach provides infinite possibilities to explore and grow adept in being more human in language processing.
The 3 essential approaches are listed in the succeeding paragraphs.
1. Rule-Based Approach
It is the oldest, tried, and tested approach that has proved its efficiency through its results. Aligning with the standard grammar rules, it’s a handcrafted system of rules based on linguistic structures that echo the pattern of language formation or grammar usage by humans. When rules are applied to text, it can offer a lot of insights: think what one can decipher about random text by simply figuring out which of the words are nouns, or what verbs end in ‘ing’, or whether one can identify a pattern recognizable as Python code.This system is flexible to develop and can always allow you to verify whether a query can be processed or how can it be done. For instance; by using an extension of translation rules and a good synonym’s base, it can easily be updated to perform with new functions and data types; with no major change to the core system.
A rule-based approach is perfect to acquire a specific language phenomenon: it efficiently decodes the linguistic relationship between words to translate the sentence.
• It is easily achieved through a focus on pattern-matching or parsing. • It can be counted as the ‘fill in the blanks’ method.
• It offers high-performance cases when used specifically but fails to impress when generalized.
Hence, it’s vital to make a good choice for query analysis that’s meant to perform.
2. Machine Learning or ‘Traditional’ Approach
‘Traditional’ or Machine Learning (ML) approach is an algorithm-based system that aids the machine to learn and understand the language by using statistical methods. The system here does not require explicit programming, it starts analyzing the already interpreted data or the training set (annotated corpus) to develop its own learnings, create its own framework and its own classifiers.The machine learning approach comprises probabilistic modeling, likelihood maximization as well as linear classifiers. As the results are based on probability phenomena, it holds fewer guarantees which cannot observe complicated text completely and struggles through the butterfly effect i.e. A very small change or a small amount of new data can drastically alter or result in a different outcome, leading to the system to act unpredictably even to its author. It’s ideal for a task-based activity like document classification or word clustering which has a lot of data points and helps machines to learn from its statistical clues of the word given in the task itself.
The traditional machine learning approach is described by:
• Training data or annotated corpus – one that has a corpus with mark-up.• Feature engineering – capitalized, singulars, word type, surrounding words, etc.
• A general ML system of a training set or training a model on defined parameters, followed by fitting on test data. • Applying model to test data or inference which is characterized by finding most probable words, next word, best category, etc.
• ‘Semantic slot filling’ tags the words or tokens which carry meaning to the sentences or translate utterances to logical form.
Both approaches are widely used but have limitations and drawbacks too. The rule-based approach needs experts to create the flexi rules and operate while the probabilistic method and inference-driven model require better training corpus to enhance its knowledge and deliver steadfastly.
Here’s is a list of the pros and cons of both approaches that have led to a hybrid approach for better results.
| Rule-based Grammar | Machine Learning algorithm |
|---|---|
| Advantages | Advantages |
| It’s easily adaptable | It’s can scale effortlessly |
| Simple to debug | Learnability without clear programming |
| Enormous training corpus not needed | Quick development if the dataset is available |
| Comprehends the language | High recall coverage |
| High perfection | |
| Disadvantages | Disadvantages |
| Proficient developers & linguists required | Training corpus with annotation needed |
| Slow parser development | Hard to debug |
| Moderate recall (coverage) | Zero understanding of the language |
3. Neural Network and Deep Learning Approach
Deep learning is a branch of machine learning and it is the re-branded name for neural networks – a family of learning new techniques that is inspired by the computations of the brain. It can be characterized as learning parameterized differential mathematical functions. The name deep learning comes from the fact that many layers of these differentiable functions are often chained together.Machine learning is based on predictions and past observations while deep learning works on correctly representing the data to predict upon. Basically, it involves different approaches. The human visual system is undoubtedly one of the wonders of the world. It is incredible at making sense of what our eyes show us as a supercomputer in our brain does most of the processing unconsciously. The difficulty in visual pattern recognition like deciphering a handwritten digit becomes apparent if you attempt to write a computer algorithm as intuition is not as easy to express as precise rules. Neural networks approach the problem differently. It takes a large pool of handwritten numbers, called training examples, and then builds a system that can learn from those training examples.
To put it simply, the neural network uses the training examples to automatically infer rules for the recognition of handwritten digits. Also, by increasing the quantity or sheer volume of numbers for creating training examples, the network can learn more about handwritten digits, improving their accuracy. For e.g., if the sample is just 100 training digits first, perhaps one can create a better handwriting recognizer by using tens, thousands, or even billions of training examples.
A neural network is like machine learning in myriad ways.
• One of the major benefits for neural networks is that feature engineering is generally skipped, as networks will learn crucial features.
• Instead, streams of raw parameters (‘words’ – vector representations of words) without engineered features are fed into neural networks.
• It has a massive training corpus or training examples that assure better accuracy specific neural networks of use in NLP include Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs).
CNN has a dedicated architecture that surpasses at extracting local patterns in the data which are sensitive to the word order. These can be used in identifying indicative phrases of words or idioms of up to a fixed length in long sentences/documents.
For sequential data, RNN is the specialized model. It is employed as an input transformer which is trained to produce informative representation for the CNN to operate on top of it. They allow the abandoning of assumptions by taking into consideration entire sentences while taking word order into account when it is needed. This capability leads to extraordinary gains in language modeling as well as predicts the probability of the next word in the sequence. Recursive networks extend recurrent networks from sequence to trees. Such models form the core of state-of-the-art translations.
Today, the task of language predictions is interconnected. Growth in technology helps to predict efficiency and flawlessness, from word to sequence. Tree with neural network approach provides infinite possibilities to explore and grow adept in being more human in language processing.