Designing Natural Language Processing Tools for Teachers
The first objective gives insights of the various important terminologies of NLP and NLG, and can be useful for the readers interested to start their early career in NLP and work relevant to its applications. The second objective of this paper focuses on the history, applications, and recent developments in the field of NLP. The third objective is to discuss datasets, approaches and evaluation metrics used in NLP.
Now that the world has made strides in Machine learning and publishing newer studies in the field, automatic text summarization is on the verge of becoming a ubiquitous tool to interact with information in the digital age. Creating a summary from a given piece of content is a very abstract process that everyone participates in. Automating such a process can help parse through a lot of data and help humans better use their time to make crucial decisions. With the sheer volume of media out there, one can be very efficient by reducing the fluff around the most critical information.
#3. Hybrid Algorithms
Our findings also indicate that deep learning methods now receive more attention and perform better than traditional machine learning methods. NLP is used to understand the structure and meaning of human language by analyzing different aspects like syntax, semantics, pragmatics, and morphology. Then, computer science transforms this linguistic knowledge into rule-based, machine learning algorithms that can solve specific problems and perform desired tasks.
It is on this graph that we will use the handy PageRank algorithm to arrive at the sentence ranking. We provide conditions that define a sentence such as looking for punctuation marks such as period (.), question mark (?), and an exclamation mark (!). Once we have this definition, we simply split the text document into sentences.
The past five years have been a slow burn of what NLP can do, thanks to integration across all manner of devices, from computers and fridges to speakers and automobiles. A word (Token) is the minimal unit that a machine can understand and process. So any text string cannot be further processed without going through tokenization. Tokenization is the process of splitting the raw string into meaningful tokens. The complexity of tokenization varies according to the need of the NLP application, and the complexity of the language itself.
In one article, the cancer registry, the Surveillance, Epidemiology, and End Results (SEER) registry data, pathology reports, and radiology reports were examined. The amount of datasets in English dominates (81%), followed by datasets in Chinese (10%), Arabic (1.5%). This shows that there is a demand for NLP technology in different mental illness detection applications.
NLP Algorithms Explained
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