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BERT pioneered an approach involving the use of a dedicated [CLS] token prepended to the beginning of each sentence inputted into the model; the final hidden state vector of this token encodes information about the sentence and can be fine-tuned for use in sentence classification tasks. In practice however, BERT's sentence embedding with the ...
Topic analysis consists of two main tasks: topic identification and text segmentation. While the first is a simple classification of a specific text, the latter case implies that a document may contain multiple topics, and the task of computerized text segmentation may be to discover these topics automatically and segment the text accordingly ...
That is, after pre-training, BERT can be fine-tuned with fewer resources on smaller datasets to optimize its performance on specific tasks such as natural language inference and text classification, and sequence-to-sequence-based language generation tasks such as question answering and conversational response generation. [12]
Despite this lack of syntax or grammar, BoW representation is fast and may be sufficient for simple tasks that do not require word order. For instance, for document classification, if the words "stocks" "trade" "investors" appears multiple times, then the text is likely a financial report, even though it would be insufficient to distinguish between
The documents to be classified may be texts, images, music, etc. Each kind of document possesses its special classification problems. When not otherwise specified, text classification is implied. Documents may be classified according to their subjects or according to other attributes (such as document type, author, printing year etc.). In the ...
In the TE framework, the entailing and entailed texts are termed text (t) and hypothesis (h), respectively.Textual entailment is not the same as pure logical entailment – it has a more relaxed definition: "t entails h" (t ⇒ h) if, typically, a human reading t would infer that h is most likely true. [1]
To exploit a parallel text, some kind of text alignment identifying equivalent text segments (phrases or sentences) is a prerequisite for analysis. Machine translation algorithms for translating between two languages are often trained using parallel fragments comprising a first-language corpus and a second-language corpus, which is an element ...
A language model is a model of natural language. [1] Language models are useful for a variety of tasks, including speech recognition, [2] machine translation, [3] natural language generation (generating more human-like text), optical character recognition, route optimization, [4] handwriting recognition, [5] grammar induction, [6] and information retrieval.