Text Data Encoding Task: Difference between revisions
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** It can handle various languages and scripts, enabling applications in [[multilingual NLP]]. | ** It can handle various languages and scripts, enabling applications in [[multilingual NLP]]. | ||
** It can leverage [[pretrained language models]] like [[BERT]], [[GPT]], or [[Word2Vec]] for efficient encoding. | ** It can leverage [[pretrained language models]] like [[BERT]], [[GPT]], or [[Word2Vec]] for efficient encoding. | ||
** It can improve with advancements in [[neural network architectures]] and [[language modeling | ** It can improve with advancements in [[neural network architectures]] and [[language modeling technique]]s. | ||
** It can utilize [[contextual embeddings]] to capture the meaning of words based on their usage in sentences. | ** It can utilize [[contextual embeddings]] to capture the meaning of words based on their usage in sentences. | ||
** ... | ** ... |
Latest revision as of 13:40, 21 July 2024
An Text Data Encoding Task is a data encoding task for text data.
- Context:
- It can (typically) involve converting raw text data into high-dimensional text embeddings.
- It can (typically) be performed by a Text Encoding System (that implements a text encoding algorithm).
- It can range from encoding short text snippets to entire documents or corpora.
- It can produce outputs that serve as inputs for downstream tasks like text classification, sentiment analysis, or machine translation.
- It can handle various languages and scripts, enabling applications in multilingual NLP.
- It can leverage pretrained language models like BERT, GPT, or Word2Vec for efficient encoding.
- It can improve with advancements in neural network architectures and language modeling techniques.
- It can utilize contextual embeddings to capture the meaning of words based on their usage in sentences.
- ...
- Example(s):
- Word2Vec Encoding that encodes words into vectors for semantic similarity analysis.
- BERT-based text encoding that encodes ...
- JSON Encoding.
- ...
- Counter-Example(s):
- Image Data Encoding, which involve converting images into image embeddings.
- Audio Data Encoding, which focus on encoding audio signals into audio feature vectors.
- Text Embedding Decoding, ...
- See: Image Data Encoding, Audio Data Encoding, Feature Extraction, BERT, GPT, Word2Vec
References
2018
- (Devlin et al., 2018) ⇒ Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. (2018). “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.” In: arXiv. doi:10.48550/arXiv.1810.04805
2013
- (Mikolov et al., 2013) ⇒ Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. (2013). “Efficient Estimation of Word Representations in Vector Space.” In: arXiv. doi:10.48550/arXiv.1301.3781