Core NLP Responsibilities – Fundamentals of Natural Language Processing

Core NLP Responsibilities

Text analysis and entity recognition, sentiment analysis, audio recognition and synthesis, machine translation, and semantic language modeling are some of the most crucial things that NLP performs. You will comprehend each of these fundamental NLP duties in the following area.

Text Analysis and Entity Recognition

Text analysis is the process of looking at different parts of a document or phrase to figure out what it’s about. Entity recognition is often needed to look at a written document to figure out what its main points are or to find dates, places, and people who are mentioned in it.

Text Analysis

Humans are generally capable of reading material and comprehending its meaning. Even if you don’t know the grammar rules of the language the text is written in, you can still get some ideas from it. For example, you could read a text and pick out the key phrases that show what the text is about. You might also recognize people’s names or well-known places like the Eiffel Tower. Although it may be difficult at times, you may be able to gain a sense of how the individual felt when they wrote the piece, also known as “sentiment.”

Here are a few text analysis techniques:

Text analytics is the process by which an artificial intelligence (AI) program running on a computer looks at some properties in text to get specific insights. To obtain insights, a person will often rely on their own experiences and knowledge. To execute the work, a computer must be equipped with similar knowledge. The following are some regularly used strategies for developing text analysis software:

• Statistical analysis of textual terms. For example, deleting common “stop words” (words such as “the” or “a,” which offer little semantic information about the text) and running frequency analysis on the remaining words (counting how frequently each word appears) can yield indications about the text’s major subject.
• Extending frequency analysis to multiterm phrases (a two-word phrase is a bigram; a three-word phrase is a trigram, and so on).
• Using stemming or lemmatization techniques to standardize words before counting them, such that terms like “power,” “powered,” and “powerful” are all considered as the same word. Stemming is the process of removing the last few characters from a word, which often results in incorrect meanings and spelling. Stemming is used when there is a large dataset and performance is an issue. Lemmatization takes the context into account and converts the word to its meaningful base form, which is known as a lemma. Because it involves lookup tables and other such things, lemmatization is computationally expensive.
• Applying linguistic structure norms to sentence analysis, such as breaking sentences down into tree-like structures such as a noun phrase, which contains nouns, verbs, adjectives, and so on.
• Words or keywords are encoded as numeric features that can be used to train a machine learning model, for example, to categorize a text document based on the terms contained inside it. This technique is frequently used to perform sentiment analysis, which classifies a document as positive or negative.
• Developing vectorized models for capturing semantic links between words by assigning them to locations in n-dimensional space. For example, this modeling technique may assign values to the terms “flower” and “plant” that place them close to one another, whereas “skateboard” may be assigned a value that places them much further away.

While these strategies can be quite effective, programming them can be difficult. Microsoft Azure’s Language Cognitive Service can help with app development by using pretrained models that can

• Find out what language a text or document was written in (e.g., French or English).
• Perform sentiment analysis on text to determine whether it is favorable or negative.
• Extract important phrases from the text that may reveal the major themes.

Identify and classify things in the text. Entities can be persons, locations, organizations, or even everyday objects like dates, times, and quantities.