Visual Data in NLP is a subfield of Multimodality that deals with the processing of visual data such as images or videos in conjunction with textual data.

Multimodality is a subfield of Natural Language Processing (NLP) that refers to the capability of a system or method to process input of different types or “modalities”, such as natural language text, speech, audio, images, video, and programming languages in NLP applications. It involves developing algorithms and models that can process and analyze information from multiple modalities, and integrate them to form a unified representation of the input.

Sentiment Analysis is a subfield of Natural Language Processing (NLP) that deals with the identification, extraction and quantification of affective states and subjective information from textual data.

Attention is a mechanism that allows models to focus on specific parts of the input when generating the output. It helps the model to pay 'attention' to relevant words or phrases, and ignore the less relevant ones.

Semantic Text Processing is a subfield of Natural Language Processing (NLP) that attempts to derive meaning from natural language and helps machines interpret textual data semantically. It involves developing algorithms and models that can understand and represent the semantics of words and sentences, and the relationships between them.

Natural Language Processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of 'understanding' the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.

Language Models are a subfield of Semantic Text Processing that deal with the probabilistic modeling and generation of natural language, based on a learned probability function of sequences of words. Language models generate probabilities by training on corpora of natural language texts and can be fine-tuned for a variety of different downstream tasks.

Language Model Mechanisms refer to the design decisions and concepts used to build language models.

Publication:

Emoji-based Co-attention Network for Microblog Sentiment Analysis

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