## 5 Challenges
This section lists some of the challenges faced when analysing the context of files on storage devices. * Abbreviations: One of the most important challenges faced when analysing a document, regardless of language, is recognising words that may have multiple meanings or words that may be part of different sentences, and classifying similar words. Several words or sentences can be written in different ways, these can be abbreviated to facilitate writing, reading, and understanding. The same words can be written in long forms, e.g., BTW means By the way, and in many cases abbreviations may coincide with an organisation, a place, a position, or a position title. * Errors related to speed and text: Models that rely on semantics cannot be trained if the speech and text data are wrong. This problem is analogous to the implication of misused or even misspelled words, which allow the model to learn over time. Although evolved grammar correction tools are good enough to remove sentence-specific errors, the training data must be error-free to facilitate accurate development in the first place. * Spelling Variation: The vowels in the English language are very important. These letters do not make a big difference when heard, but they do make a big difference in spelling. Everybody makes spelling mistakes, but for the majority of us, we can gauge what the word was actually meant to be. However, this is a major challenge for computers, as they do not have the same ability to infer what the word was actually meant to be. * Foreign Words: Words that are currently not used or heard very often are an area of interest in this field. Such words include names of people, place names, or ancient organisations. * Different types of text: Sometimes, when analysing a document, it is difficult to relate two texts of different subject matter, for example, it is difficult to relate a judicial text to a common text, given that the words used to refer to the same thing may vary. * Synonyms: Some phrases or words can have exactly the same meanings at different grammatical levels and with the same grammatical category. In any language, people often use synonyms to denote slightly different meanings within their vocabulary without changing the meaning of the sentence. Small and little can be synonyms when automatically parsing a sentence to denote the same meaning, but they are not interchangeable in all contexts, as one could denote only size and the other could denote both size and emotion. For example, buy a small cup of coffee for the office and even the small change can make a difference, in this example the words are not interchangeable. * Colloquialisms: In every culture, phrases, expressions, and idiomatic jargon are used that have specific meanings, posing a problem for NLP developments. These expressions may exist in more than one culture, yet have different meanings in different geographical regions or simply have no coherent meaning. Even if NLP services try to scale beyond ambiguities, errors, and homonyms, it is not easy to include specific words that have different connotations from one culture to another. There are words that lack a definition in the language, but may still be relevant to a specific audience. It is important to include relevant references so that the resource is sufficiently perceptive. * Sarcasm: Generally used words and phrases that can be positive or negative, but in reality connote the opposite. When expressing a sentence, the intention with which it is intended to be transmitted, the emotions that were present when creating it, and the personality of the author or speaker can influence it. Some of them, such as irony and sarcasm, can make a sentence be taken as positive, however, the emotions of the author can go in an opposite sense to the literal one. Although sentiment analysis has now made advances, the correct extraction of context when confronted with sarcastic sentences remains a research challenge.