NLP Algorithms & Terminologies

There are several algorithms commonly used in Natural Language Processing (NLP) for different tasks, some examples include:

Tokenization Algorithms:

Tokenization is the process of breaking down a text into smaller units called tokens, which can include words, phrases, or sentences. Algorithms for tokenization include regular expression-based tokenization, rule-based tokenization, and statistical tokenization.

Part-of-Speech Tagging Algorithms:

Part-of-speech tagging is the process of identifying the parts of speech (e.g. nouns, verbs, adjectives) for each token in the text. Algorithms for part-of-speech tagging include rule-based methods, probabilistic methods such as Hidden Markov Models (HMM) and Conditional Random Fields (CRF), and neural network-based methods.

Named Entity Recognition Algorithms:

Named entity recognition is the process of identifying and extracting named entities (e.g. people, places, organizations) from the text. Algorithms for named entity recognition include rule-based methods, probabilistic methods such as HMM and CRF, and neural network-based methods.

Parsing Algorithms:

Parsing is the process of analyzing the grammatical structure of the text and identifying relationships between different tokens and phrases. Algorithms for parsing include dependency parsing and constituency parsing.

Sentiment Analysis Algorithms:

Sentiment analysis is the process of determining the overall sentiment or emotional tone of the text. Algorithms for sentiment analysis include lexicon-based methods, rule-based methods, and machine learning-based methods such as support vector machines (SVMs) and deep neural networks (DNNs).

Text Generation Algorithms:

Text generation is the process of generating new text based on the input text. Algorithms for text generation include Markov models, language models like LSTM and Transformer based models like GPT, T5 and RoBERTa.

These are just a few examples of the many NLP algorithms available. The specific algorithm used will depend on the specific task and application, and the size of the dataset available for training.

Natural Language Processing Terminologies :

Phonology :

Phonology in NLP (Natural Language Processing) isstudy of organizing sound systematically & the sound systems of languages, and how they are used to convey meaning.

It is an important part of understanding how speech and language are represented in computer systems. Techniques from phonology are used in tasks such as speech recognition, text-to-speech synthesis, and speech synthesis.

For example,

phonological information can be used to improve the accuracy of speech recognition systems by taking into account the variation in pronunciation across different dialects and languages.

Morphology :

Morphology in NLP (Natural Language Processing) is study of construction of words from primitive meaningful units & the study of the internal structure of words and how they are formed . It is concerned with the rules and processes that govern the creation of words, including the use of prefixes, suffixes, and inflections.

Morphological analysis is the process of identifying the root or stem of a word and its inflections, such as the plural or past tense forms of a noun or verb. This information is used in various NLP tasks, such as text classification, information retrieval, and machine translation.

Morphological generation is the process of creating inflected or derived forms of a word given its root or stem and morphological information. This is used in text-to-speech synthesis, text summarization and text generation.

Morphological analysis and generation are closely related to other NLP subfields like phonology, syntax and semantics. However, morphology focuses specifically on the internal structure of words and their relationships to one another.

Morpheme :

A morpheme is the smallest unit of meaning in a language. In NLP (Natural Language Processing), morphological analysis is the process of identifying and analyzing the morphemes in a word or sentence. Morphemes are classified as either root or affixes. A root morpheme is the core of a word that carries the main meaning, and an affix is a bound morpheme which added to a root morpheme to give new meaning or change the grammatical function of a word.

For example,

the word "unhappy" is made up of two morphemes: "un-" which is a negative prefix, and "-happy" which is the root morpheme. The prefix "un-" changes the meaning of the root morpheme "happy" from positive to negative.

Morphemes are a key concept in NLP because they provide a way to understand the internal structure of words and how they are formed. This information is used in various NLP tasks such as text classification, information retrieval, and machine translation.

In morphological generation, Morphemes are used to generate inflected or derived forms of a word given its root or stem and morphological information.

This is used in text-to-speech synthesis, text summarization and text generation.

Syntax : It refers to arranging words to make a sentence. It also involves determining the structural role of words in the sentence and in phrases.

Semantics : It is concerned with the meaning of words and how to combine words into meaningful phrases and sentences.

Pragmatics : It deals with using and understanding sentences in different situations and how the interpretation of the sentence is affected.

Discourse : It deals with how the immediately preceding sentence can affect the interpretation of the next sentence.

World Knowledge : It includes the general knowledge about the world.