An Introduction to Natural Language Processing

Natural Language Processing (NLP) is the branch of AI focused on the processing and understanding of text by machines. Whilst large language models have raised significant awareness of textual analysis and conversation AI, the field of NLP has been around since the 1940s. This article dives into the key aspects of natural language processing and provides an overview of different NLP techniques and how businesses can embrace it.

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What is Natural Language Processing (NLP)?

Natural language processing (NLP) is a subfield of artificial intelligence that is tasked with understanding, interpreting, and generating human language. Interestingly, natural language processing algorithms are additionally expected to derive and produce meaning and context from language. There are many applications for natural language processing across multiple industries, such as linguistics, psychology, human resource management, customer service, and more. NLP can perform key tasks to improve the processing and delivery of human language for machines and people alike.

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Applications of NLP in Everyday Life: Examples

As mentioned above, NLP is used in more settings than artificial intelligence or computer science. Recently, translation headphones have entered the market for people who want to communicate with others who speak a different language than they do!

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In this instance, the NLP present in the headphones understands spoken language through speech recognition technology. Once the incoming language is deciphered, another NLP algorithm can translate and contextualise the speech. This single use of NLP technology is massively beneficial for worldwide communication and understanding.

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Key Components of NLP

As with many AI systems, there are several key components of NLP:

Tokenization

Tokenization involves breaking text into smaller chunks, such as words or parts of words. These chunks are called tokens, and tokens are less overwhelming for processing by NLP.

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Part-of-Speech Tagging

Part-of-speech tagging involves assigning grammatical values to all text, which will help the NLP AI figure out sentence flow. Parts of speech, such as nouns, verbs, adjectives, and more are used by NLP to identify sentences.

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Named Entity Recognition

Named entity recognition (NER) is similar to part-of-speech tagging, but this time, named entities (people, topics, events, and more) are being identified and tagged in text. Knowledge graphs and ontologies are a great way of modelling and storing entities for NER purposes.

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Sentiment Analysis

Sentiment analysis is the process of finding the emotional meaning or the tone of a section of text. This process can be tricky, as emotions are regarded as an innately human thing and can have different meanings depending on the context. However, NLP combines machine learning and linguistic knowledge to determine the meaning of a passage.

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Machine Translation

Lastly, machine translation uses computational algorithms to directly translate a section of text into another language. Relying on neural networks and other complex strategies, NLP can decipher the language being spoken, translate it, and retain its full meaning.

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NLP Techniques

NLP techniques leverage the previous NLP concepts to solve specific tasks. The main natural language processing techniques are the following:

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Rule-Based Approaches

Rule-based approaches are most often used for sections of text that can be understood through patterns.

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For example, language translation technologies use rule-based approaches to decipher grammar, spelling, and other clear-cut rules of speaking.

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Statistical Models

Statistical models in NLP are commonly used for less complex, but highly regimented tasks.

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For instance, a common statistical model used is the term “frequency-inverse document frequency” (TF-IDF), which can identify patterns in a document to find the relevance of what is being said.

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Machine Learning in NLP

Machine learning has been applied to NLP for a number of intricate tasks, especially those involving deep neural networks. These neural networks capture patterns that can only be learned through vast amounts of data and an intense training process. Machine learning and deep learning algorithms are not able to process raw text natively but can instead work with numbers. Once text has been tokenized, it can then be mapped to numerical vectors for further analysis. Different vectorization techniques exist and can emphasise or mute certain semantic relationships or patterns between the words.

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Deep Learning and Neural Networks

As mentioned above, deep learning and neural networks in NLP can be used for text generation, summarisation, and context analysis. Large language models are a type of neural network which have proven to be great at understanding and performing text based tasks. Vault is TextMine’s very own large language model and has been trained to detect key terms in business critical documents.

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NLP in Text Processing

NLP is incredibly useful for text-processing tasks, such as classifications, information retrieval, summarisation, and content generation.

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Text Classification

NLP can classify text based on its grammatical structure, perspective, relevance, and more. This is often used when processing large amounts of spoken text.

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Information Retrieval

NLP can perform information retrieval, such as any text that relates to a certain keyword.

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For example, CTRL+F allows computer users to find a specific word in a document, but NLP can be prompted to find a phrase based on a few words or based on semantics.

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Text Summarisation

Documents that are hundreds of pages can be summarised with NLP, as these algorithms can be programmed to create the shortest possible summary from a big document while disregarding repetitive or unimportant information.

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Text Generation

Using neural networking techniques and transformers, generative AI models such as large language models can generate text about a range of topics.

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Bringing NLP into the business

Organisations are sitting on huge amounts of textual data which is often stored in disorganised drives. Due to a lack of NLP skills, this textual data is often inaccessible to the business. Large language models have introduced a paradigm shift because this information is now readily accessible. Business critical documents can now be searched and queried at scale using Vault, a proprietary large language model which is able to classify a document based on its type and extract key data points.

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Building our Large Language Model

TextMine’s large language model has been trained on thousands of contracts and financial documents which means that Vault is able to accurately extract key information about your business critical documents. TextMine’s large language model is self-hosted which means that your data stays within TextMine and is not sent to any third party. Moreover, Vault is flexible meaning it can process documents it hasn’t previously seen and can respond to custom queries.

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About TextMine

TextMine is an easy-to-use data extraction tool for procurement, operations, and finance teams. TextMine encompasses 3 components: Legislate, Vault and Scribe. We’re on a mission to empower organisations to effortlessly extract data, manage version controls, and ensure consistency access across all departments. With our AI-driven platform, teams can effortlessly locate documents, collaborate seamlessly across departments, making the most of their business data.

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