Natural Language Processing- How different NLP Algorithms work
Natural Language Processing (NLP) is an area in computer science that studies the interactions between computers and human languages. It is the technology behind search engines such as Google.
The analysis of language can be done manually, and it has been done for centuries. But technology continues to evolve, which is especially true in natural language processing (NLP).
The Machine and Deep Learning communities have been actively pursuing Natural Language Processing (NLP) through various techniques. Some of the techniques used today have only existed for a few years but are already changing how we interact with machines. Natural language processing (NLP) is a field of research that provides us with practical ways of building systems that understand human language. These include speech recognition systems, machine translation software, and chatbots, amongst many others. But many different algorithms can be used to solve the same problem. This article will compare four standard methods for training machine-learning models to process human language data.
How are NLP Algorithms categorized?
There are different types of NLP (natural language processing) algorithms. They can be categorized based on their tasks, like Part of Speech Tagging, parsing, entity recognition, or relation extraction.
Tagging:Part of Speech Tagging algorithms produce tags that indicate the function of certain elements in a sentence. For example, verb, noun, and preposition are all common tags for words in English sentences.
Parsing:Parsing is the task of taking a sentence and putting it into grammatical components (phrase structure), which usually means dividing the sentence into phrases and parts of speech, which trees can represent — hence “parsing.”
Entity Recognition:Named Entity recognition discovers named entities within a text, such as people, organizations, or locations.
Relation extraction:Relation extraction is a sub-task of semantic analysis. The idea behind relation extraction is that if one entity mentioned in a sentence can be linked with another using an auxiliary verb (e.g., ‘is,’ ‘was’ etc.) or some other marker word (e.g., ‘to,’ ‘from’), then there must be a relationship between these two entities.
Most used NLP algorithms.
Many NLP algorithms are designed with different purposes in mind, ranging from aspects of language generation to understanding sentiment.
The most commonly used algorithms are-
Sentimental analysis:
Sentiment analysis is one way that computers can understand the intent behind what you are saying or writing. Sentiment analysis is technique companies use to determine if their customers have positive feelings about their product or service. Still, it can also be used to understand better how people feel about politics, healthcare, or any other area where people have strong feelings about different issues. This article will overview the different types of nearly related techniques that deal with text analytics.
Sentiment analysis determines whether someone has good or bad feelings about something based on the words they use and social media posts and comments they make. Sentiment analysis is critical because it helps companies respond to unhappy customers better. Let’s say you own a restaurant, and someone writes on Twitter that they don’t like the food there; sentiment analysis will help you find this post (which most likely will contain hashtags like #food or something similar) and focus your efforts on that customer first. Sentiment analysis is also vital for understanding how people feel about different topics such as politics or healthcare reform, just by using the data posted on social media. Sentiment analysis can be used in many different fields where natural language processing, computational linguistics, and machine learning are applied:
- Marketing
- Customer service/call centers
- Market research
- Social media
Named entity recognition
Named entity recognition is often treated as text classification, where given a set of documents, one needs to classify them such as person names or organization names. Machine learning algorithms usually process this task. There are several classifiers available, but the simplest is the k-nearest neighbor algorithm (kNN).
This simple algorithm is based on the assumption that if a document contains a specific entity mentioned in it, many words close to it are also part of this entity. So for every name mentioned — e.g., Jack Sparrow, CEO of Google — one counts how many words were between this name and the previous one. For example: “Jack Sparrow was born in…” or “CEO of Google is Jack Sparrow.” Then these numbers are used as hyper-parameters in the model’s training procedure. This means that after all documents have been classified with the kNN algorithm, one counts how many times a particular name was mentioned in one category and how many times it was mentioned in the other. Finally, these numbers are plugged into the model’s equation where there is a hyper-parameter that considers that Jack Sparrow is CEO of Google. Finally, the result will show that depending on this information, “Jack Sparrow” is more likely to be a person than an organization.
Text summarization
Text summarization is a text processing task, which has been widely studied in the past few decades.
The basic idea of text summarization is to create an abridged version of the original document, but it must express only the main point of the original text.
Text summarization can be divided into two categories: extractive and abstractive. Extract text summarization is also known as keyphrase extraction; in this approach, sentences are selected from the source documents based on some statistical measures related to centrality (i.e., how important they are), such as frequency words, TF-IDF (term frequency-inverse document frequency) score, etc. Abstractive text summarization generates new sentences that include information not present in the original text. For instance, abstractive summarization can employ paraphrasing techniques to create a summary that contains the same information in different words.
Abstractive text summarization has been widely studied for many years because of its superior performance compared to extractive summarization. However, extractive text summarization is much more straightforward than abstractive summarization because extractions do not require the generation of new text.
Aspect mining
Aspect mining is a type of natural language processing. Aspect mining finds the different features, elements, or aspects in text. Aspect mining classifies texts into distinct categories to identify attitudes described in each category, often called sentiments. Aspects are sometimes compared to topics, which classify the topic instead of the sentiment. Depending on the technique used, aspects can be entities, actions, feelings/emotions, attributes, events, and more.
Aspect Mining tools have been applied by companies to detect customer responses. Aspects can be valuable factors in Artificial Intelligence research and development for new technologies such as chatbots that use artificial intelligence programs to respond with appropriate answers based on aspects mined from text conversations that they analyze for patterns and connections between queries and responses. Aspect mining is often combined with sentiment analysis tools, another type of natural language processing to get explicit or implicit sentiments about aspects in text. Aspects and opinions are so closely related that they are often used interchangeably in the literature. Aspect mining can be beneficial for companies because it allows them to detect the nature of their customer responses.
Statistical language Modeling
Statistical language Modeling is language generation in which we want to predict the next word (or character) based on the previous and generate an utterance for an audience. When we use this model, we try to infer the next word by looking at how likely it is within our vocabulary. For example: when I say “I like cheese.”, there’s nothing in my language model that suggests I might instead say “I like jellybeans.” Words often depend not just on the words before them but on all words in an entire sentence or even on some in nearby sentences. Another essential language model is language modeling: we want to predict the next language item based on previously seen language items and a language corpus (i.e., all language ever written).
Machine Translation
Machine Translation (MT) automatically translates natural language text from one human language to another. With these programs, we’re able to translate fluently between languages that we wouldn’t otherwise be able to communicate effectively in — such as Klingon and Elvish.
The machine translation system calculates the probability of every word in a text and then applies rules that govern sentence structure and grammar, resulting in a translation that is often hard for native speakers to understand. In addition, this rule-based approach to MT considers linguistic context, whereas rule-less statistical MT does not factor this in.
This particular category of NLP models also facilitates question answering — instead of clicking through multiple pages on search engines, question answering enables users to get an answer for their question relatively quickly.
Learn the most in-demand techniques in the industry.
Over 80% of Fortune 500 companies use natural language processing (NLP) to extract text and unstructured data value.
NLP is used for a wide variety of applications, including search, information extraction, machine translation, and summarization
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Take Away
There are many algorithms to choose from, and it can be challenging to figure out the best one for your needs. Hopefully, this post has helped you gain knowledge on which NLP algorithm will work best based on what you want trying to accomplish and who your target audience may be. If not, please reach out! Our Industry expert mentors will help you understand the logic behind everything Data Science related and help you gain the necessary knowledge you require to boost your career ahead.
