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I am sure there are complex methods to extract keywords, but the standard one which should serve as a strong baseline is the RAKE graph algorithm https://pypi.org/project/rake-nltk/. It should work reasonably well in most text domains.


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Yes, both of them are different tasks. POS tagging helps NER systems but it is not necessary. You can get features (say BERT/ELMo embedding) for each word in the sentence and train a CRF NER model. This looks like simple example https://www.pragnakalp.com/bert-named-entity-recognition-ner-tutorial-demo/


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This seems to be inherited from the original Google implementation, which also uses 2 outputs (https://github.com/google-research/bert/blob/master/run_pretraining.py#L293). I can see two possible reasons that the original implementation uses 2 outputs: They are using the cross entropy loss, which typically works with log probabilities. To get probabilities ...


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The simple language model will give you the probability of a sequence of tokens(sentence) for that language. So lets say if you have trained a model for English language your model can give you the probability for any random english sentence. Consider some sentence $X$ $=$ "the quick brown fox jumps over the lazy dog" $=$ $x_1 \ x_2 \ x_3 \ ... \ ...


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"BUT there are 2 different attention layers and one of which do not use the encoder’s output at all. So, what are the keys and values now?" The first attention layer in the decoder is the ‘Masked Multi-Head Attention’ layer and is self-attention layer, calculating how much each word is related to each word in the same sentence. However, our aim in ...


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My answer assumes your fine-tuning architecture simply stacks a single fully-connected layer on top of the BERT [CLS] output, as in Figure 4b of the BERT paper. Generally, when working with mixed data such as continuous and categorical features, the first step is to simply concatenate all the inputs into one long vector. In your case, you would concatenate a ...


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https://spacy.io/api/lemmatizer just uses lookup tables and the only upstream task it relies on is POS tagging, so it should be relatively fast. For large amounts of text, SpaCy recommends using nlp.pipe, which can work in batches and has built in support for multiprocessing (with the n_process keyword), rather than than simply nlp. Also, make sure you ...


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One of the renowned learning algorithms for NER tagging is the conditional random field (CRF). As you can see in the provided link, sequence labeling algorithms such as RNN with LSTM‌ can be used to named entity recognition as well. By the way, you can find an implementation of the CRF for NER tagging in this source. Notice that, the method of providing ...


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There are different algorithms, each with their advantages and disadvantages. Gazetteers: these have lists of the entities to be recognised, eg list of countries, cities, people, companies, whatever is required. They typically use a fuzzy matching algorithm to capture cases where the entity is not written in exactly the same way as in the list. For example, ...


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=> I don't have more ideas about BART and T5 Right now. but I had created a chatbot Based on GPT-2 Model-based on Microsoft DialoGPT. Which is fine-tuned on millions of parameters of Reddit. you can fine-tune on your own data using DialoGPT. I had not found a public decoding method. So, We tried to generate diverse responses using simple nucleus sampling at ...


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They're all important. NLP is an umbrella term that includes the other two; NLG is only concerned with generating language, ie transforming some internal data structure into human language. NLU is about processing information contained in language, and putting it into relation with a knowledge base etc. If you don't know anything about any of these fields, ...


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