The AI domain used in spam filter is
(a) Data Science (b) Computer Vision
(c ) Natural Language Processing
Answers
Answer:
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
Explanation:
Answer:
The AI domain used in spam filter is Option C Natural Language Processing
Explanation:
The fundamental concepts of NLP differ from those of Machine Learning or Software Engineering in general. I will start with the most low-level things (which doesn't mean "simple" though) and then I'll try to show you how do they build up a production model.
Tokenizer
This is a core tool for every NLP framework. Many ML techniques whether they aim for text classification or regression, use n-grams and features, produced by them. Before you start extracting features, you need to get the words.
POS-tagger and lemmatizer
This is the next thing you will need, although, maybe, not directly. Words can take many forms and the connections between them (as you will see below) depend on their POS. Lemmatizers are involved most often when something like TDM is needed, because they naturally reduce the dimensionality and lead to a greater overall robustness.
NER
Which stands for Named Entity Recognizers. They rely on extracted parts-of-speech and basic grammars, encoded in frameworks. There is a separated part of NLP, called information retrieval, where people do really cool things like an automated generation of reports based on several messages about the topic. NER is certainly the biggest part of it. If you want to understand it deeply, you can read about Context-Free Grammars.
Natural Language Processing (NLP) should be considered to be done in multiple languages, starting from English. Where the complexity of an NLP task can differ across languages, we consider English as the first choice, and then only tend to move to other languages. For instance, consider word segmentation, which is rather straightforward for English. The following does not cover any speech related tasks, since those topics should be better listed under the speech paradigm. Also, optical character recognition like methods are more to be listed under the computer vision paradigm.
Natural Language Generation
(1) Generation of realistic, rhymed and theme based poetry (creative writing)
(2) Generation of theme based short stories (creative writing)
(3) Generation of theme based novels (creative writing)
(4) Generation of news / short articles based on numerical / audio / video data
(5) Generation of research papers based on a topic. Here (4) helps but one also needs proper understanding and summarization of various research papers (which will involve a lot of other aspects of NLP and also some inference over figures and mathematical equations)
Reference Link
- https://brainly.in/question/17300299
- https://brainly.in/question/7456322