Key to text introduces the idea of building a model that would translate keywords into sentences.

Lemmatisation (or lemmatization) in linguistics is the process of grouping together the inflected forms of a word so they can be analysed as a single item, identified by the word's lemma, or dictionary form.

In linguistics, morphology is the study of words, how they are formed, and their relationship to other words in the same language.

Named-entity recognition (NER) (also known as (named) entity identification, entity chunking, and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.

In corpus linguistics, part-of-speech tagging (POS tagging or PoS tagging or POST), also called grammatical tagging is the process of marking up a word in a text (corpus) as corresponding to a particular part of speech,[1] based on both its definition and its context. A simplified form of this is commonly taught to school-age children, in the identification of words as nouns, verbs, adjectives, adverbs, etc.

Sentiment analysis (also known as opinion mining or emotion AI) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.

As financial markets getting faster and more complex, it is difficult for market participants to manage the information overload. Sentiment analysis is a useful text mining method to process textual content and filter the results with analysis methods to relevant and meaningful information.

Text segmentation is the process of dividing written text into meaningful units, such as words, sentences, or topics.