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‎KeywordExtractionNotebook.ipynb

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"cell_type": "code",
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"text": [
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"[nltk_data] Downloading package stopwords to /home/jovyan/nltk_data...\n",
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"[nltk_data] Package stopwords is already up-to-date!\n",
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"[nltk_data] Downloading package wordnet to /home/jovyan/nltk_data...\n",
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"[nltk_data] Unzipping corpora/wordnet.zip.\n"
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]
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}
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"source": [
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"#If this is the first time importing the stopwords package /\n",
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"#you must first download the stopwords using nltk.download('stopwords')\n",
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"#nltk.download('stopwords')\n",
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"import re\n",
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"import pandas\n",
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"import nltk\n",
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"from nltk.corpus import stopwords\n",
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"nltk.download('stopwords')\n",
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"nltk.download('wordnet')\n",
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"from nltk.stem.wordnet import WordNetLemmatizer\n",
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"from sklearn.feature_extraction.text import CountVectorizer"
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]
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"4 Abstract Missing U sing a neural net to instantiate a\\ndeformab... "
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"2387 Regularized Boost for Semi-Supervised Learning... "
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"4 2387 Semi-supervised inductive learning concerns ho... 119"
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"' Non negative matrix factorization NMF has previously been shown to be a useful decomposition for multivariate data Two different multi plicative algorithms for NMF are analyzed They differ only slightly in the multiplicative factor used in the update rules One algorithm can be shown to minimize the conventional least squares error while the other minimizes the generalized Kullback Leibler divergence The monotonic convergence of both algorithms can be proven using an auxiliary func tion analogous to that used for proving convergence of the Expectation Maximization algorithm The algorithms can also be interpreted as diag onally rescaled gradient descent where the rescaling factor is optimally chosen to ensure convergence Spike triggered averaging techniques are effective for linear characterization of neural responses But neurons exhibit important nonlinear behaviors such as gain control that are not captured by such analyses We describe a spike triggered covariance method for retrievi'"
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" 'Spike triggered averaging technique effective linear characterization neural response But neuron exhibit important nonlinear behavior gain control captured analysis We describe spike triggered covariance method retrieving suppressive component gain control signal neuron We demonstrate method simulation retinal ganglion cell data Analysis physiological data reveals significant suppressive ax explains neural nonlinearities This method applicable sensory area modality']"
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"<3924x10000 sparse matrix of type '<class 'numpy.int64'>'\n",
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"\twith 265145 stored elements in Compressed Sparse Row format>"
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"\twith 26681 stored elements in Compressed Sparse Row format>"
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