nlp

问题 The attention weights are computed as: I want to know what the h_s refers to. In the tensorflow code, the encoder RNN returns a tuple: encoder_outputs, encoder_state = tf.nn.dynamic_rnn(...) As I think, the h_s should be the encoder_state , but the github/nmt gives a different answer? # attention_states: [batch_size, max_time, num_units] attention_states = tf.transpose(encoder_outputs, [1, 0, 2]) # Create an attention mechanism attention_mechanism = tf.contrib.seq2seq.LuongAttention( num

问题 The attention weights are computed as: I want to know what the h_s refers to. In the tensorflow code, the encoder RNN returns a tuple: encoder_outputs, encoder_state = tf.nn.dynamic_rnn(...) As I think, the h_s should be the encoder_state , but the github/nmt gives a different answer? # attention_states: [batch_size, max_time, num_units] attention_states = tf.transpose(encoder_outputs, [1, 0, 2]) # Create an attention mechanism attention_mechanism = tf.contrib.seq2seq.LuongAttention( num

问题 I am implementing a simple multitask model in Keras. I used the code given in the documentation under the heading of shared layers . I know that in multitask learning, we share some of the initial layers in our model and the final layers are made individual to the specific tasks as per the link. I have following two cases in keras API where in the first, I am using keras.layers.concatenate while in the other, I am not using any keras.layers.concatenate . I am posting the codes as well as the

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 2 years ago . I wanted to know the spell correct api's available for commercial/non commercial usage other than google/bing. 回答1: First of all you can write your own spell corrector with this tutorial. In addition there are some Python packages that may help you with that, such as TextBlob (which I highly recommend). Another

问题 I want to find a conditional probability of a word given its previous set of words. I plan to use Google N-grams for the same. However, being such a huge resource as it is, I don't think it is computationally possible to do on my PC. ( To process all N-grams, to train a language model). So is there any way I can train a language model using Google Ngrams ? (Even python NLTK library does not support ngram language model anymore) Note - I know that a language model can be trained using ngrams,

问题 With Stanford Classifier it is possible to use cross validation by setting the options in the properties file, such as this for 10-fold cross validation: crossValidationFolds=10 printCrossValidationDecisions=true shuffleTrainingData=true shuffleSeed=1 Running this will output, per fold, the various metrics, such as precision, recall, Accuracy/micro-averaged F1 and Macro-averaged F1. Is there an option to get an averaged or otherwise aggregated score of all 10 Accuracy/micro-averaged F1 or all

问题 I am trying training a custom NER model for multiple entities. Here is the sample training data: count all <START:item_type> operating tables <END> on the <START:location_id> third <END> <START:location_type> floor <END> count all <START:item_type> items <END> on the <START:location_id> third <END> <START:location_type> floor <END> how many <START:item_type> beds <END> are in <START:location_type> room <END> <START:location_id> 2 <END> The NameFinderME.train(.) method takes a string parameter

问题 I am trying training a custom NER model for multiple entities. Here is the sample training data: count all <START:item_type> operating tables <END> on the <START:location_id> third <END> <START:location_type> floor <END> count all <START:item_type> items <END> on the <START:location_id> third <END> <START:location_type> floor <END> how many <START:item_type> beds <END> are in <START:location_type> room <END> <START:location_id> 2 <END> The NameFinderME.train(.) method takes a string parameter

问题 I have an audio file in Wav format that I want to transcribe: My code is: import speech_recognition as sr harvard = sr.AudioFile('speech_file.wav') with harvard as source: try: audio = r.listen(source) #print("Done") except sr.UnknownValueError: exec() r.recognize_google(audio) I do receive an output: Out[20]: 'thank you for calling my name is Denise who I have a pleasure speaking with hi my name is Mary Jane. Good afternoon Mary Jane I do have your account open with your email' However,

问题 The dependency-parsed output (using Stanford Parser) of the following two sentences are as follows. Sentence 1 - John is a computer scientist Dot format - digraph G{ edge [dir=forward] node [shape=plaintext] 0 [label="0 (None)"] 0 -> 5 [label="root"] 1 [label="1 (John)"] 2 [label="2 (is)"] 3 [label="3 (a)"] 4 [label="4 (computer)"] 5 [label="5 (scientist)"] 5 -> 2 [label="cop"] 5 -> 4 [label="compound"] 5 -> 3 [label="det"] 5 -> 1 [label="nsubj"] } Graph - Sentence 2 - John has an elder