Hybrid Approach In Natural Language Processing
Language is a tricky thing. And it’s everywhere. Humans and businesses, both run on language. It can be found in a variety of places, including emails, corporate documents, blogs like this one, and more. Not only is there a great amount of language data available to and stored within businesses, but the volume of information is also exponentially expanding. The importance of language in the enterprise ecosystem cannot be overstated. Organizations, however, struggle to leverage the value of linguistic data, despite its pervasiveness in the industry. Moreover, the most common type of data generated by humans is unstructured text. Rich insights can often be found buried inside this unstructured data, which can be used to make better business decisions, shape product strategy, and improve consumer experiences.
Organizations want a mechanism for utilizing the vast amounts of unstructured data at their disposal. And NLP is where the enterprise can make a meaningful difference. Almost half of firms now use natural language processing (NLP) applications, and one-fourth of businesses aim to start employing NLP technology in the coming months, according to IBM’s Global AI Adoption Index 2021. However, because of the intrinsically unique (and sometimes chaotic) character of human language, the notion of starting a new project or expanding an existing Natural Language Processing project can feel overwhelming. So, let’s dig deeper and learn a little more about NLP and its approaches.
What is NLP?
NLP is an area of artificial intelligence that aids computers in comprehending, interpreting, and simulating human speech. It can be explained as the ability of machines to study and detect human language. Text mining, sentiment analysis, and machine translation are some of the applications of Natural Language Processing (NLP). We can extract essential features, classifications, summaries, and relational concepts from text data using NLP. Even massive amounts of data can be simplified using NLP solutions because their applications allow for faster processing and the use of business models to extract human language insights. It gives structure to unstructured data, allowing organizations to scale the human act of reading, organizing, and quantifying text data so that it can be more easily analyzed – resulting in faster time to insights and, ultimately, faster evidence-based decisions. To bridge the gap between human communication and computer understanding, NLP uses Machine Learning and human-generated linguistic rules.
Approaches in NLP
There are two main approaches to NLP – Rule-based and Machine Learning.
1. Rule-based Approach
A rule-based approach extracts categorizes, and analyses text using linguistic rules. It is a human-driven system based on linguistic conventions that have already been established. This approach examines linguistic and semantic linkages to comprehend language and its parts using linguistic rules and knowledge inherent in the knowledge graph (e.g., grammar, sentence structure, etc.).
It is vital to have a subject matter expert (SME) and/or linguist involved in the process. With this technique, there is generally a high level of control and the capacity to alter regulations as needed. This method is best suited for task-oriented interactions or search queries. The focus of rule-based approaches is usually on pattern matching or parsing. The most obvious downside of the rule-based method is that it necessitates the use of qualified experts. Rules must be manually developed and improved on a regular basis. Overall, a rule-based system is effective at capturing a certain linguistic phenomenon.
2. Machine learning Approach
NLP makes extensive use of machine learning (ML). This method relies on algorithms that understand language without having to be explicitly programmed. This is accomplished by the application of statistical methods, in which the system analyses the training set in order to generate its own knowledge, rules, and classifiers. This approach can be further classified into Unsupervised and Supervised ML approaches. Both methods may necessitate a large amount of training data. When good training data sets are available, the introduction of machine learning algorithms can greatly speed up the development of the capability of some NLP systems. In practice, however, things aren’t often so straightforward.
Unsupervised machine learning models work with unlabeled input that the algorithm tries to make sense of on its own by extracting features and patterns. This method gives you less control over the model and introduces more unknowns, making it more difficult to intervene if something goes wrong or has to be altered. Supervised ML models need preexisting labels such as sentiment or categories. The model’s accuracy improves over time as a result of human training and SMEs. It may be a suitable fit for conversational experiences due to the combination of human training and machine learning.
A hybrid approach in NLP
NLP, ML, and human input are all part of the hybrid approach, which combines the best of rule-based and machine learning approaches. Human experience guides accurate analysis, but machine learning makes that analysis scale easily. Organizations can utilize both ML and rule-based approaches in tandem in a hybrid manner, allowing them to reap the benefits of both. The hybrid approach in NLP gives you a number of options for determining the optimum path for modeling your text and making faster business decisions. Using a hybrid strategy to analyze unstructured text data is generally a smart practice, depending on your goal.
By using semi-supervised learning to automate the tagging of data based on human input to your training data, machine learning can assist to reduce the human model building work even further. This method does not even require a significant amount of training data. It increases system openness to ensure a positive customer experience and the ability to track against the KPIs. In addition, this approach provides greater flexibility, iterability, and speed, resulting in less load on resources. It is appropriate for both conversational and task-oriented projects.
Human language is a complex problem for which businesses have been searching for an optimal solution for a long time. This can finally be solved with a mixed NLP approach. Hybrid techniques can help us improve the results of our NLP applications in various cases. For example, if we’re creating a grammar checker, we’ll need a module that recognizes multiword expressions like kick the bucket, as well as a rule-based module that detects the incorrect pattern and generates the correct one. This is an example of a hybrid strategy.
Given the vast volumes of data available now, NLP is essentially statistical. We utilize NLP on a regular basis as consumers, from our first Google search of the day to our curated daily news items, our online buying experience and reading reviews, and our conversational assistants like OK Google, Alexa, and Siri. NLP is ingrained in our daily life. While the traditional approaches to NLP continue to prevail, each of those approaches comes with its set of cons. The only way to handle the inherent limits of each technique while also reaping the benefits of each is to use a hybrid technique.
Also Read: A Guide To The 5 NLP Phases