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Which of the following are NLP tools?
Stanford's Core NLP Suite
Natural Language Toolkit (NLTK)
SpaCy
All of the above
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1 Natural Language Processing - Introduction to Classical NLP
2 MCQ - Alan Turing published an article titled "Computing Machinery and Intelligence" in the year
3 MCQ - The NLP Layer translates users’ queries (free form) into information that can be used for appropriate responses.
4 MCQ - Which of the following are NLP tools?
5 Natural Language Processing - Creating a Quiz using Text Blob
6 Natural Language Processing - Finding Related Posts using scikit-learn
7 MCQ - Bag of Words Approach ignores order of words, and uses word counts as their basis.
8 MCQ - Vectorization is the step where for each word in the post, its occurrence is counted and noted in a vector.
9 MCQ - Tf-IDF weight is often used in Information retrieval and Text mining
10 MCQ - DialogFlow is an advanced development suite for creating conversational AI applications, including chatbots, voicebots, and IVR bots from Google.
11 Natural Language Processing - Introduction to Modern NLP - Part I
12 Natural Language Processing - Introduction to Modern NLP - Part II
13 Natural Language Processing - Sentiment Analysis
14 MCQ - Char-RNN predicts the next character in a sentence.
15 MCQ - Stateful RNN preserve final state after processing one training batch, and then use it as initial state for next training batch
16 MCQ - The pretrained model components of the TensorFlow Hub project are called
17 Natural Language Processing - Machine Translation
18 MCQ - The TensorFlow Addons project includes many sequence-to-sequence tools to let you easily build production-ready Encoder–Decoders
19 Natural Language Processing - Bi-directional RNN and Beam Search
20 Natural Language Processing - Attention Mechanisms
21 MCQ - Dzmitry Bahdanau et al. introduced a technique that allowed the decoder to focus on the appropriate words (as encoded by the encoder) at each time step in the year
22 MCQ - The Bahdanau attention mechanism is also known as
23 MCQ - Positional embedding generates embeddings using cos and tan functions which allows the model to learn the relative positions of words
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