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Tag: nlp

Word2Vec + LSTM Good Training and Validation but Poor on Test

currently I’am training my Word2Vec + LSTM for Twitter sentiment analysis. I use the pre-trained GoogleNewsVectorNegative300 word embedding. The reason I used the pre-trained GoogleNewsVectorNegative300 because the performance much worse when I trained my own Word2Vec using own dataset. The problem is why my training process had validation acc and loss stuck at 0.88 and 0.34 respectively. Then, my confussion

A* search algorithm implementation in python

I am trying to build a very simple A* Search Algorithm in Python 3. Given the following distances for each node (considering S is the starting node and G the end one) I want to write a function that finds the best path based on total cost (i.e., f(n) for those familiar with the terminology) for the following search space:

Train/Validation/Testing sets for imbalanced dataset

I am working in an NLP task for a classification problem. My dataset is imbalanced and some authors have 1 only text, thus I want to have this text only in the training test. As for the other authors I have to have a spliting of 70%, 15% and 15% respectivelly. I tried to use train_test_split function from sklearn, but

spacy Entity Ruler pattern isn’t working for ent_type

I am trying to get the entity ruler patterns to use a combination of lemma & ent_type to generate a tag for the phrase “landed (or land) in Baltimore(location)”. It seems to be working with the Matcher, but not the entity ruler I created. I set the override ents to True, so not really sure why this isn’t working. It