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. But deep learning is a more flexible, intuitive approach in which algorithms learn to identify speakers’ intent from many examples — almost like how a child would learn human language.

What is NLP and its types?

Natural language processing (NLP) refers to the branch of computer science—and more specifically, the branch of artificial intelligence or AI—concerned with giving computers the ability to understand text and spoken words in much the same way human beings can.

A better way to parallelize the vectorization algorithm is to form the vocabulary in a first pass, then put the vocabulary in common memory and finally, hash in parallel. This approach, however, doesn’t take full advantage of the benefits of parallelization. Additionally, as mentioned earlier, the vocabulary can become large very quickly, especially for large corpuses containing large documents.

Various Stemming Algorithms:

Nevertheless it seems that the general trend over the past time has been to go from the use of large standard stop word lists to the use of no lists at all. The tokenization process can be particularly problematic when dealing with biomedical text domains which contain lots of hyphens, parentheses, and other punctuation marks. And what would happen if you were tested as a false positive? (meaning that you can be diagnosed with the disease even though you don’t have it).

Lemmatization tries to achieve a similar base “stem” for a word. However, what makes it different is that it finds the dictionary word instead of truncating the original word. That is why it generates results faster, but it is less accurate than lemmatization. Pragmatic analysis deals natural language processing algorithms with overall communication and interpretation of language. It deals with deriving meaningful use of language in various situations. Syntactic analysis involves the analysis of words in a sentence for grammar and arranging words in a manner that shows the relationship among the words.

Is artificial data useful for biomedical Natural Language Processing algorithms?

For example, Hale et al.36 showed that the amount and the type of corpus impact the ability of deep language parsers to linearly correlate with EEG responses. The present work complements this finding by evaluating the full set of activations of deep language models. It further demonstrates that the key ingredient to make a model more brain-like is, for now, to improve its language performance. Text classification is the process of understanding the meaning of the unstructured text and organizing it into predefined classes .

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" 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After preprocessing, the output is a set of prepared words. Therefore, vectors are created from the incoming information — they represent it as a set of numerical values. One downside to vocabulary-based hashing is that the algorithm must store the vocabulary.

Sentence splitter

It can be used in real cases but it is mainly used for didactic or research purposes. Similar filtering can be done for other forms of text content – filtering news articles based on their bias, screening internal memos based on the sensitivity of the information being conveyed. This automatic routing can also be used to sort through manually created support tickets to ensure that the right queries get to the right team. Again, NLP is used to understand what the customer needs based on the language they’ve used in their ticket.

natural language processing algorithms

The non-induced data, including data regarding the sizes of the datasets used in the studies, can be found as supplementary material attached to this paper. The literature search generated a total of 2355 unique publications. After reviewing the titles and abstracts, we selected 256 publications for additional screening. Out of the 256 publications, we excluded 65 publications, as the described Natural Language Processing algorithms in those publications were not evaluated. Reference checking did not provide any additional publications.

Techniques and methods of natural language processing

In complex extractions, it is possible that chunking can output unuseful data. In such case scenarios, we can use chinking to exclude some parts from that chunked text. If accuracy is not the project’s final goal, then stemming is an appropriate approach.

  • This is when common words are removed from text so unique words that offer the most information about the text remain.
    • Table4 lists the included publications with their evaluation methodologies.
    • There are many challenges in Natural language processing but one of the main reasons NLP is difficult is simply because human language is ambiguous.
    • This technique allows you to estimate the importance of the term for the term relative to all other terms in a text.

    Penulis: Hannani Juhari

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