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Word embeddings for social goods

This document discusses different methods for creating word embeddings from text data, including bag-of-words, continuous bag-of-words, weighted continuous bag-of-words, and convolutional neural networks. Bag-of-words represents words as one-hot encodings but does not capture similarity between words. Continuous bag-of-words learns distributed word representations but treats all words equally. Weighted continuous bag-of-words aims to address this by learning word importance weights. Convolutional neural networks apply convolutions to word vectors to learn local contextual representations of words. These methods are evaluated on a text classification task, with convolutional neural networks achieving the best performance.

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Word embeddings for social goods Kyiv Deep Learning Study Group #1 Sergii Gavrylov
Overview ● Problem description ● Data preprocessing ● Bag-of-words ● Continuous bag-of-words ● Weighted continuous bag-of-words ● Convolutional neural network
www.drivendata.org
Box-Plots for Education
Dataset features
Dataset labels Function Object_Type Operating_Status Position_Type Pre_K Reporting Sharing Student_Type Use
Dataset labels
Loss function Multi-multi-class log loss
Preprocessing ● Tokenize all text columns with OpenNLP, lowercase tokens and filter out stop words ["‐", "-", "-", "‒", "–", "—", "―", "－", "+", "/", "*", ".", ",", "'", "(", ")", """, "&", ":", "to", "of", "and", "or", "for", "the", "a"]
Preprocessing ● Tokenize all text columns with OpenNLP, lowercase tokens and filter out stop words ["‐", "-", "-", "‒", "–", "—", "―", "－", "+", "/", "*", ".", ",", "'", "(", ")", """, "&", ":", "to", "of", "and", "or", "for", "the", "a"] ● Perform softmax normalization for all float columns ● Replace all NaNs in float columns with 0
Preprocessing ● Tokenize all text columns with OpenNLP, lowercase tokens and filter out stop words ["‐", "-", "-", "‒", "–", "—", "―", "－", "+", "/", "*", ".", ",", "'", "(", ")", """, "&", ":", "to", "of", "and", "or", "for", "the", "a"] ● Perform softmax normalization for all float columns ● Replace all NaNs in float columns with 0 ● Keep floats intact, replace NaNs with the specified value or
Word representation One-hot encoding social [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0] public [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]
Bag-of-words Text column features
Bag-of-words Text column features Sub_Object_Description employees wages salaries services personal
Bag-of-words Text column features Sub_Object_Description employees wages salaries services personal [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0] [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0] [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0] [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]
Bag-of-words Text column features Sub_Object_Description employees wages salaries services personal [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0] [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0] [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0] [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0] Sub_Object_Description_bow [0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0] ∑
Bag-of-words ● Concatenated BOW features and floats comprise final feature vector.
Bag-of-words ● Concatenated BOW features and floats comprise final feature vector. ● Replace FTE field NaNs with -1, Total filed NaNs with -20000
Bag-of-words ● Concatenated BOW features and floats comprise final feature vector. ● Replace FTE field NaNs with -1, Total filed NaNs with -20000 ● Train sklearn.ensemble.RandomForestClassifier
Bag-of-words ● Concatenated BOW features and floats comprise final feature vector. ● Replace FTE field NaNs with -1, Total filed NaNs with -20000 ● Train sklearn.ensemble.RandomForestClassifier ● Score: 0.8671
Bag-of-words (+) Pros ● Simplicity
Bag-of-words (+) Pros (-) Cons ● Simplicity ● Notion of word similarity is undefined with one-hot encoding
Bag-of-words (+) Pros (-) Cons ● Simplicity ● Notion of word similarity is undefined with one-hot encoding social [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0] public [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]
Bag-of-words (+) Pros (-) Cons ● Simplicity ● Notion of word similarity is undefined with one-hot encoding social [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0] public [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0] ● Impossible to generalize to unseen words
Bag-of-words (+) Pros (-) Cons ● Simplicity ● Notion of word similarity is undefined with one-hot encoding social [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0] public [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0] ● Impossible to generalize to unseen words ● One-hot encoding can be memory inefficient
Word representation Distributed representation social [-0.56, 8.65, 5.32, -3.14] public [-0.42, 9.84, 4.51, -2.71]
Word representation Cosine similarity
Stanford GloVe Trained on the Common Crawl (840B tokens) Vector dimensionality is 300 nlp.stanford.edu/projects/glove
Sub_Object_Description Function_Description...Text columns Continuous bag-of-words
personal employees wages salaries services instructional staff training services words words Sub_Object_Description Function_Description...Text columns ... Continuous bag-of-words
personal employees wages salaries services instructional staff training services words vectors words vectors Sub_Object_Description Function_Description...Text columns ... Continuous bag-of-words
personal employees wages salaries services instructional staff training services words vectors words vectors Sub_Object_Description Function_Description ∑ ∑ ...Text columns ... Continuous bag-of-words
personal employees wages salaries services instructional staff training services words vectors words vectors Sub_Object_Description Function_Description ∑ ∑ CBOW features ...Text columns ... ... Continuous bag-of-words
personal employees wages salaries services instructional staff training services words vectors words vectors Sub_Object_Description Function_Description ∑ ∑ CBOW features ...Text columns ... ... Continuous bag-of-words
Continuous bag-of-words ● Concatenated CBOW features and floats comprise the final feature vector.
Continuous bag-of-words ● Concatenated CBOW features and floats comprise the final feature vector. ● Replace FTE field NaNs with -1, Total filed NaNs with -20000
Continuous bag-of-words ● Concatenated CBOW features and floats comprise the final feature vector. ● Replace FTE field NaNs with -1, Total filed NaNs with -20000 ● Train sklearn.ensemble.RandomForestClassifier
Continuous bag-of-words ● Concatenated CBOW features and floats comprise the final feature vector. ● Replace FTE field NaNs with -1, Total filed NaNs with -20000 ● Train sklearn.ensemble.RandomForestClassifier ● Score: 0.6616
Continuous bag-of-words (+) Pros ● Simplicity
Continuous bag-of-words (+) Pros ● Simplicity ● Possible to generalize to unseen words
Continuous bag-of-words (+) Pros ● Simplicity ● Possible to generalize to unseen words (-) Cons ● All words are equal
Continuous bag-of-words (+) Pros ● Simplicity ● Possible to generalize to unseen words (-) Cons ● All words are equal, but some words are more equal than others
Weighted CBOW Text column features
Weighted CBOW Text column features Sub_Object_Description employees wages salaries services personal
Weighted CBOW Text column features Sub_Object_Description employees wages salaries services personal
Weighted CBOW Text column features Sub_Object_Description employees wages salaries services personal
Weighted CBOW Text column features Sub_Object_Description employees wages salaries services personal
Weighted CBOW Text column features Sub_Object_Description employees wages salaries services personal
Weighted CBOW Text column features Sub_Object_Description employees wages salaries services personal
Weighted CBOW Text column features Sub_Object_Description employees wages salaries services personal
Weighted CBOW Text column features Sub_Object_Description employees wages salaries services personal
Weighted CBOW Text column features Sub_Object_Description employees wages salaries services personal
Weighted CBOW Text column features Sub_Object_Description employees wages salaries services personal
Weighted CBOW Text column features Sub_Object_Description employees wages salaries services personal
Weighted CBOW Text column features Sub_Object_Description employees wages salaries services personal
Weighted CBOW Text column features Sub_Object_Description employees wages salaries services personal
Weighted CBOW Text column features Sub_Object_Description employees wages salaries services personal × × × × ×
Weighted CBOW Text column features Sub_Object_Description employees wages salaries services personal × × × × × ∑ Sub_Object_Description_wcbow
Weighted CBOW Text column features Sub_Object_Description employees wages salaries services personal × × × × × ∑ Sub_Object_Description_wcbow
Weighted CBOW ● Concatenated WCBOW features and floats compose the final feature vector.
Weighted CBOW ● Concatenated WCBOW features and floats compose the final feature vector. ● Use softmax normalization for float columns
Weighted CBOW ● Concatenated WCBOW features and floats compose the final feature vector. ● Use softmax normalization for float columns ● Replace all float NaNs with 0
Weighted CBOW ● Concatenated WCBOW features and floats compose the final feature vector. ● Use softmax normalization for float columns ● Replace all float NaNs with 0 ● Train softmax jointly with θc parameters
Weighted CBOW ● Concatenated WCBOW features and floats compose the final feature vector. ● Use softmax normalization for float columns ● Replace all float NaNs with 0 ● Train softmax jointly with θc parameters ● Score: 0.9159
Weighted CBOW Why so poorly?
Weighted CBOW Why so poorly? H1: Softmax is not so powerful as Random forest
Weighted CBOW Why so poorly? H1: Softmax is not as powerful as Random Forest H2: Model assumes that for describing relevant words it is enough to use one direction per column in the word space
How many directions should a good model have?
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees mean max
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees mean max
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees
Convolutional NN personal services wagessalariesemployees max mean
Convolutional NN personal services wagessalariesemployees
Convolutional NN employees wages salaries services personal
Convolutional NN employees wages salaries services personal
Convolutional NN employees wages salaries services personal ×
Convolutional NN employees wages salaries services personal × =
Convolutional NN employees wages salaries services personal × = Stride size = word dimensionality
Convolutional NN ● Concatenated mean and max values for feature maps and floats form the final feature vector
Convolutional NN ● Concatenated mean and max values for feature maps and floats form the final feature vector ● Use softmax normalization for float columns
Convolutional NN ● Concatenated mean and max values for feature maps and floats form the final feature vector ● Use softmax normalization for float columns ● Replace all float NaNs with 0
Convolutional NN ● Concatenated mean and max values for feature maps and floats form the final feature vector ● Use softmax normalization for float columns ● Replace all float NaNs with 0 ● Train softmax with Wf parameters jointly
● Concatenated mean and max values for feature maps and floats form the final feature vector ● Use softmax normalization for float columns ● Replace all float NaNs with 0 ● Train softmax with Wf parameters jointly ● Score: 0.6932 Convolutional NN
Convolutional NN Why is it not as good as CBOW + RF?
Convolutional NN Why is it not as good as CBOW + RF? ● There is fewer parameters
Convolutional NN Why is it not as good as CBOW + RF? ● There is fewer parameters ● The performance is still comparable to CBOW+RF. Therefore, using cnn is a sensible idea.
Convolutional NN Why is it not as good as CBOW + RF? ● There is fewer parameters ● The performance is still comparable to CBOW+RF. Therefore, using cnn is a sensible idea. ● Probably, we could gain more from this type of feature learner if we go deeper
Final model ● Train RF on the concatenated CBOW features and NN logits
Final model ● Train RF on the concatenated CBOW features and NN logits ● Train 2 CBOW classifiers, 2 NN classifiers, 2 meta-classifiers and blend them
Final model ● Train RF on the concatenated CBOW features and NN logits ● Train 2 CBOW classifiers, 2 NN classifiers, 2 meta-classifiers and blend them ● Score: 0.5228
Results
Conclusion ● Explore your data before doing any analysis ● Keep trying ● Ensembles are powerful ● Participating in competitions provides a great learning opportunity

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Word embeddings for social goods

Editor's Notes

  • #4 DrivenData is a for-profit social enterprise that hosts online competitions, with the goal of engaging a global community of data scientists in solving social problems.
  • #5 ERS helps school districts use their resources more strategically by providing them with a way to compare their spending to other school districts. Before partnering with DrivenData, that process involved assigning every line item to certain categories in a comprehensive financial spending framework — a task that required an average 400 man-hours per project, and limited the nonprofit's ability to give school districts the analysis they need to improve.