Natural Language Processing And Its Applications


5 Applications Of Deep Learning Of Natural Language Processing

The field of natural language processing was before statistical and now it is becoming a neural network. Though trying, researchers still face some challenging problems in solving natural language. However, deep learning methods can give some possible outcomes and satisfying results. This is not only about learning and solving benchmark problems, but it is also about learning the hard word meaning of natural tasks. In this post, you will discover 5 amazing learning methods of natural language.

5 Applications Of Deep Learning Of Natural Language Processing
5 Applications Of Deep Learning Of Natural Language Processing

Text Clarification

The popular classification is based on sentiment analysis and the class labels represent the emotional tone of the source text. The labels can be of two types, “positive” or “negative“. The examples are spam filtering, language identification, and genre classification. Furthermore, the problem may need several other multiple classes called multi-label classification.

Language Modeling

Language modeling is a subtask of rather exciting natural problems and for those who condition the language model on other inputs. Here you have to predict the next word given the previous words and the goal is fundamental to speech. Moreover, you should recognize the optical character and that is necessary for spelling correction, handwriting recognition, and statistical machine translation as well. I just clarified this in academic language and in general language, the task is to learn language processing architectures deeply. It also means learning the possible relationship between words and new sentences or words. And that also needs to be statistically consistent with the source text. And the language models are useful for

  • Generating new article headlines.
  • Generating new sentences, paragraphs, or documents.
  • Creating suggested continuation of a sentence

Speech Recognization

It is a problem of understanding the language or what the other person just said. Additionally, when someone is giving you listenable audio, the person should also include readable text data. As this is an automatic nature of the process, it is also known as Automatic Speech Recognition (ASR). And the language model will create a text out depending on the data in the audio. The examples are-

  • Transcribing a speech.
  • Creating text captions for movies or TV shows.
  • Assigning commands to the radio while driving.

Caption Generation

Caption generation means describing what is going on in an image. It should be one or two liners or something more than that. Digital images such as photos need clarification about the content within it. And captions are textual descriptions of the photos. The language model will create a caption that is related to the image. And the examples are drawing the contents of a scene, producing a caption for a photograph, representing a video.

Machine Translation

It refers to converting a source text into a different language from another language. Machine translation does not require human assistance and it is an automatic process. The process can depend on the language given such as French to English text translation, German to English audio translation, text to audio, etc.

5 Applications Of Deep Learning Of Natural Language Processing
5 Applications Of Deep Learning Of Natural Language Processing

Natural Language Processing

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