Using text classification for information retrieval

Abstract. Bidirectional Encoder Representations from Transformers (BERT) and BERT-based approaches are the current state-of-the-art in many natural language ... Sentiment analysis. Text classification allows organizations to manage their brand effectively on multiple channels by extracting specific words that indicate ... May 20, 2016 ... The basic idea is that semantic vectors (such as the ones provided by Word2Vec) should preserve most of the relevant information about a text ... Evaluate with Argilla¶. Now, we can start the annotation process. Just open the dataset in the Argilla UI and start annotating the records. If the suggestions ... Aug 15, 2018 ... we are going to learn about text classification — the secret weapon that NLP developers use to build cutting edge systems with relatively dumb ... Jul 17, 2019 ... In this work, we propose to use graph convolutional networks for text classification. We build a single text graph for a corpus based on word co ... ... text classification to improve the detection of the target classes from Twitter. We reviewed the literature related to short text classification of ... In this paper, a brief overview of text classification algorithms is discussed. This overview covers different text feature extractions, dimensionality ... Existing statistical text learning algorithms can be trained to approximately classify documents, given a sufficient set of labeled training examples. These ... Feb 11, 2018 ... I'm able to do text classification and NER using Prodigy, but can I combine the two into a single model which I can load into spacy? Does that make sense?