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Natural language processing (NLP) is now at the core of artificial intelligence developments, with its fast progress and potential for daily applications. With the launch of ChatGPT in November this year, the term “NLP” again comes under the spotlight. This blog aims to introduce you to all the basic things you need to know about NLP using the simplest possible words.
Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human (natural) languages. You can think of it as developing algorithms and models that can communicate and process information just like humans. One of the main goals of NLP is to enable computers to generate and understand human language as ‘naturally’ as possible so that you will not be able to differentiate whether you are talking to a human or a machine.
One of the main challenges in NLP is dealing with the variability of human language. The variability of languages can be in the form of different languages, dialects, distinctive writing styles, and local slang which means even within a single language, there can be a wide range of variations. This makes it difficult for NLP systems to accommodate all possible variations in order to accurately understand and interpret language.
For example, there are almost 40 different dialects in the UK that sound totally different from each other, and in many cases use different spellings and word structures.
Language is often used in context, and the meaning of words can change depending on the context in which they are used. For example, the word "bat" can refer to a baseball bat or a flying mammal, depending on the context.
These are easy for humans to understand because we read the context of the sentence and our brain can process it by referring to our understanding of all of the different definitions within seconds. And, while NLP language models may have learned all of the definitions, differentiating between them in context can present problems.
Over years of development, the ecosystem of NLP applications has been built quite completely for the most common, widely used languages. However, many languages, especially those spoken by people with less access to technology often go overlooked and under-processed. The lack of data in these languages limits the extent to which machines can process information and data to “learn”. This is exactly why many major chatbots in the market underperform in multilingualism while Algomo seeks to change the landscape by including up to 109 languages in its chatbot to increase coverage of underserved communities.
However, there are emerging new techniques, like multilingual transformers (using Google’s BERT “Bidirectional Encoder Representations from Transformers”) and multilingual sentence embeddings that aim to identify and leverage universal similarities that exist between languages to make things easier.
A rule-based approach is a method of analyzing and understanding the text in which a set of explicit rules is used to identify and extract specific pieces of information or to perform specific tasks. These rules may be based on the structure of the language, such as syntax or grammar, or they may be based on the meaning of the words and phrases in the text, such as their definitions or relationships to other words and concepts.
One common use involves regular expressions, which are a set of patterns that can be used to match and extract specific parts of a text string. For example, a regular expression could be used to identify all the email addresses in a document or to extract all the dates mentioned in the text.
Another example of a rule-based approach is the use of dictionaries and lexicons, which are collections of words and their meanings. A rule-based system might use a dictionary to look up the definitions of words in a text and then use those definitions to determine the overall meaning of the text.
Overall, rule-based approaches to NLP can be effective for extracting specific pieces of information from text or performing part-of-speech tagging. However, they can be limited by the specific rules that have been defined and may not be able to handle more complex language tasks.
A statistical approach is a method of analysing and understanding texts that relies on statistical models and algorithms to identify patterns and relationships in the data. This approach is based on the idea that the meaning and structure of language can be captured and understood through the analysis of large amounts of text data.
One common example of a statistical approach is the use of machine learning algorithms. For example, a machine learning algorithm might be trained to classify text documents as spam or not spam based on the words and phrases they contain, or to identify the sentiment (positive, neutral, or negative) of a piece of text.
Another example is the use of n-gram models, which analyze the frequency and co-occurrence of groups of words in a text to identify patterns and predict the likelihood of certain words or phrases occurring together.
Statistical approaches to NLP can be highly effective for a wide range of tasks, including language translation, text classification, and sentiment analysis. However, they tend to require large amounts of data and computational power to train and execute, which are not readily available for some languages or dialects.
Chatbots use NLP to simulate conversations with human users through text or voice interactions since they are designed to understand and interpret the user's input and provide an appropriate response or carry out a requested action. The best chatbots also learn to recognize contextual clues about human requests and use them to provide even better interaction experiences or options over time, just like how Algomo chatbot improves its responses continuously.
Chatbots can be used in a variety of applications, such as customer service, information retrieval, and entertainment. This is because they are extremely helpful and effective in handling routine tasks and providing information to users, freeing up human staff to deal with more complex tasks.
The effectiveness of a chatbot depends largely on the quality of its NLP algorithms and the amount of training data it has been exposed to. With advances in machine learning, chatbots are becoming increasingly sophisticated and are able to understand and respond to increasingly complex and nuanced inputs.
Machine translation involves the development of algorithms and systems that can automatically translate between languages. Same as the two general approaches of NLP, there are mainly rule-based machine translation and statistical machine translation. Google Translate is one of the most well-known examples of machine translation.
Higher-order translation involves more than just replacing words in one language with words in another. It also requires accurately capturing the meaning and tone of the input language and translating it to text with the same meaning and desired impact in the output language. One way to test the accuracy of a machine translation tool is to translate text to one language and then back to the original language.
Machine translation is useful for a variety of applications, such as translating websites and documents, enabling real-time translation of conversations, and facilitating communication between speakers of different languages. It has the potential to break down language barriers and facilitate the exchange of information and ideas globally.
Sentiment analysis involves analyzing text data to determine the sentiment or emotion expressed by the writer, which can be either positive, negative, or neutral. This can be useful for a wide range of applications, such as evaluating customer feedback, analyzing social media posts, or detecting sentiment in news articles.
It is easy to see that this will be extremely challenging for machines since the only input for them to interpret is text while in daily interactions, humans can observe facial expressions, body language as well as environmental context to sense the sentiments behind words.
For example, irony and sarcasm can be difficult for artificial intelligence to detect, as they often involve using words or phrases that are meant to convey the opposite of their literal meaning. To help models recognize irony and sarcasm, they can be trained using certain cues, such as certain words or phrases that are commonly used in these forms of expression, and by using word embeddings that group words with similar meanings together. However, accurately detecting irony and sarcasm can still be a challenging process, given the huge body of expressions that can be interpreted with these sentiments.
NLP is used to build systems that can detect spam emails by analyzing the language used in the emails and identifying patterns that are characteristic of spam. Examples of common spam indicators are overuse of financial terms, characteristic bad grammar, inappropriate urgency, misspelt company names, and more.
Although this application is considered by some experts as the ‘most solved’, drawing on your own email experience, you may be suspicious about this comment since you must have encountered situations when Gmail misclassify spam and normal mail.
Text summarization is the process of condensing a large piece of text into a shorter, more concise form while still retaining the most important information. The goal of text summarization is to create a summary that is as short as possible while still conveying the main points of the original text. There are several different types of text summarization, including extractive summarization, which involves selecting important sentences or phrases from the original text and piecing them together to form a summary, and abstractive summarization, which involves generating a new summary from scratch using natural language generation techniques.
Text summarization has a wide range of applications, including information retrieval, content recommendation, and document management. It can be useful for busy individuals who do not have time to read long documents, or for organizations that need to quickly process and understand large volumes of text data. Text summarization can also be used to identify the main topics or themes discussed in a piece of text, which can be useful for tasks such as topic modelling and text classification.
Entity extraction, also known as named entity recognition, involves identifying and extracting specific entities from a piece of text. Entities can be people, organizations, locations, dates, or any other real-world objects or concepts that are mentioned in the text.
For example, if given the following sentence: "Barack Obama was the President of the United States from 2009 to 2017.", entity extraction would identify "Barack Obama" as a person entity, "United States" as a location entity, and "2009 to 2017" as a date entity.
Entity extraction is often used in information extraction and information retrieval systems to extract important information from text data. It can also be used in tasks such as event extraction, where the goal is to identify and extract specific events or actions from text data.
NLP is the key concept that you should understand in order to better understand what is happening in today’s technology world. Overall, the goal of NLP is simple: to make machines speak like humans. However, this is much more difficult than it sounds given the nuances and complexity of human languages. There are some breakthroughs towards the end goal, including NLP applications in chatbots which have great potential in improving business efficiency.
