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How to make concept representation with the help of bag of words


By : Mark
Date : October 16 2020, 11:12 PM
This might help you Most successful linguistic parsers today are statistically based, and this is (for example) how Google Translate works. What you do is get a large semantically marked-up corpus and start walking the word chart. The set of linguistically valid English sentences is larger than that of generative grammar (an older approach), but a large corpus will get you a huge number of viable sentence templates. You can make sentences from your bag by any data traversal technique, from random walk to genetic algorithms. Let us know what you do!
Here's a great set of resources to start: Stanford statistical natural language processing and corpus-based computational linguistics resources
code :


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Bag of Words representation problem


By : aerosanya
Date : March 29 2020, 07:55 AM
around this issue Let me see if I understand the problem. You have two documents D1 and D2 each containing a sequence of words drawn from a known vocabulary {W1, W2... Wn}. You wish to obtain two mappings indicating the number of occurrences of each word in each document. So for D1, you might have
code :
W1 --> 0
W2 --> 1
W3 --> 4
W1 --> 1
W2 --> 2
W3 --> 0
vector1 = (from pair in map1 orderby pair.Key select pair.Value).ToArray();
vector2 = (from pair in map2 orderby pair.Key select pair.Value).ToArray();

How to make a simple jQuery filter list accept only whole words, so it doesn't match partial words, only whole words


By : Leo Neumeyer
Date : March 29 2020, 07:55 AM
This might help you Something like this: http://jsfiddle.net/U8T8p/10/
code :
        var containing = $('#list li').filter(function () {
            var regex = new RegExp('\\b' + a, 'i');
            return regex.test($('a', this).text());
        }).slideDown();
        $('#list li').not(containing).slideUp();

Bag of words representation using sklearn plus Snowballstemmer


By : user3621757
Date : March 29 2020, 07:55 AM
will help you You can pass a custom preprocessor which should work just as well, but retain the functionality of the tokenizer:
code :
from sklearn.feature_extraction.text import CountVectorizer
from nltk.stem import SnowballStemmer

list2 = ["rain", "raining", "rainy", "rainful", "rains", "raining!", "rain?"]

def preprocessor(data):
        return " ".join([SnowballStemmer("english").stem(word) for word in data.split()])

vectorizer = CountVectorizer(preprocessor=preprocessor).fit(list2)
print vectorizer.vocabulary_

# Should print this:
# {'raining': 2, 'raini': 1, 'rain': 0}

Bag of Words Representation


By : user3628477
Date : March 29 2020, 07:55 AM
it should still fix some issue The bag of words approach provides a concise representation of an image or a part of an image. That representation is typically used as an input to a classification algorithm which is used to estimate the class to which the image data belongs. Typically, the classifier is a supervised learning method which will require pairs (descriptor, label) from some training set during the training process. In your case, the descriptor is the BOW representation of the image data from your training set. Then, during testing you will feed the BOW descriptor of new image data to the classifier to infer the class.
From what I understand, the fact that you have three different masks for the images, means that you also have three classes. Then, each mask will tell you which part of an image should be considered image data belonging to a particular class. This is your training data.

watson concept insight api - how to get vector representation of concept?


By : fitch
Date : March 29 2020, 07:55 AM
To fix the issue you can do The API has a method to measure the distance between concepts but it doesn't provide the vector representation of a concept. See the API specification.
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