Understanding Natural Language Processing

Tokenization: An essential task in Natural Language Processing, tokenization is used to break a string of words into semantic units, known as tokens. Sentence tokenization splits words in a text generally identified by stops. Word tokenization separates words within a sentence by identifying blank spaces. For more complex structures, like-words that often go together form collocations.

Lowercasing: Words are converted into lowercase for consistency.

Removing stopwords: High-frequency stop words with no semantic value to a sentence (which, to, at, for, is) need to be filtered out to decrease the size of the dataset and the time it takes to train a model. By removing stopwords, fewer and only meaningful tokens remain, resulting in improved task performance and increased classification accuracy.
 

Words are assigned grammatical categories (e.g., noun, verb, adjective, etc.).
 

The process of extracting entities from within a text, such as names, places, organizations, email addresses and more. This automatically classifies the entity and organizes it into predefined categories, proving most useful when sorting unstructured data and detecting important information — crucial for large datasets.
 

The analysis of sentence structure to understand the relationships between words. 

Semantic analysis extracts meaning from sentence structure, word interactions and related concepts. The machine comprehends data under various logical clusters instead of preset categories, like positive or negative/conflict or neutral.

Sentiment analysis, or opinion mining, classifies text by categorizing opinions into positive, negative or neutral, and helps businesses determine and monitor brand and product perception through customer feedback, social media monitoring, market research and customer service.
 

The ability to translate text from one language to another.
 

The ability to convert speech to text.
 

Understanding and responding to questions in natural language. 

The ability to create coherent text within a relevant context.
 

Part-Of-Speech (POS) tagging, or grammatical tagging, applies labels to each token (word) in a text corpus to indicate the part of speech and other grammatical categories such as tense, number and case, for example, and identifies the relationship between words to understand the meaning of a sentence.
 

Dependency Parsing is the process of analyzing the grammatical structure of a sentence to find headwords, dependent words and how they relate to each other. Various tags represent the connection between two words in a sentence, known as dependency tags.
 

The process of analyzing sentences by breaking them down into sub-phrases, also known as constituents. Such sub-phrases belong to a specific category of grammar: noun phrases and verb phrases.
 

Humans often use an inflected form of a word or different grammatical structures to speak. Natural Language Processing uses stemming algorithms to remove prefixes and suffixes to return the word to its root, often leading to incorrect meanings and spellings. Lemmatization considers the context and converts the word to its meaningful base form, called Lemma. Sometimes, the same word can have multiple different Lemmas, so the Part Of Speech tag should identify the word in that specific context.
 

Crucial to the Natural Language Processing process, high-frequency stop words with no semantic value to a sentence (which, to, at, for, is) need to be filtered out to decrease the size of the dataset and the time it takes to train a model. By removing stopwords, fewer and only meaningful tokens remain, resulting in improved task performance and increased classification accuracy.

Depending on their context, the same word can have different meanings. This often appears subconsciously in humans; computers use two main techniques to determine which sense of a word is meant in the sentence, using either a knowledge-based approach that infers meaning by observing the dictionary definition or a supervised method that uses Natural Language Processing algorithms to apply word sense disambiguation.

Named Entity Recognition is the process of extracting entities from within a text, such as names, places, organizations, email addresses and more. This Natural Language Processing technique automatically classifies the entity and organizes it into predefined categories, proving most useful when sorting unstructured data and detecting important information - crucial for large datasets. 

The process of automatically analyzing and structuring text to understand the meaning of unstructured data. Results then get organized into predefined categories (tags), used mainly by businesses to automate processes and discover insights that lead to better decision-making. Sentiment analysis is a form of text classification.

Proving difficult for machines is deciphering as irony and sarcasm words that are, by definition, positive or negative, yet imply the opposite. Even though models are programmed to recognize cues and phrases such as 'yeah, right' or 'whatever,' humor and its context cannot always be taught in a linear process (like humans learning a new language).
 

Misspelt or misused words can create problems for text analysis. While autocorrect and grammar correction can handle common mistakes, they do not always understand the writer’s intention. The same raises havoc in the spoken word with mispronunciations, different accents and stutters. However, as databases grow and smart assistants are trained by individual users, these issues can be substantially minimized.
 

Idioms, informal phrases, cultural-specific lingo and expressions can cause several problems for Natural Language Processing - especially those built for wide use. Colloquialisms may not have a dictionary definition, and sometimes expression meanings can differ depending on the geographic location. Cultural language, including slang, morphs constantly with new words evolving daily - so it is difficult for machines to stay up-to-date. 

Development of large pre-trained models, such as GPT-4 and BERT, will continue.
 

The integration of text with other formats, such as images, videos and audio, to develop more complex and thorough Natural Language Processing models. 
 

Further work towards enhancing interpretability of Natural Language Processing models and addressing the 'black box' decision-making process. This will be of note in information-sensitive areas, such as health care, finance, and law.

Refining the ability of Natural Language Processing models to perform tasks with no task-specific training.

Improvements to AI’s conversational ability. This includes helping chatbots to be more natural, context-aware and allowing them to approach and handle complex interactions.