Embedding representation for a document is generated based on clustering words in the document. Representative clusters are selected and weighted sum of the embeddings of the words in the selected clusters is determined as a document embedding. Documents are labeled based on document embeddings. A machine learning algorithm is trained using the documents. The machine learning algorithm predicts a label of a given document based on the given document's document embedding.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of training a machine to learn to predict a label for data, the method performed by at least one hardware processor, comprising: receiving a document; creating clusters of words in the document based on cosine similarity of word embeddings of words in the document; responsive to determining that the document has a title, ranking the clusters based on cosine similarity of word embeddings of words in a cluster and word embeddings of words in the title; responsive to determining that the document has no title, ranking the clusters based on compactness of a cluster indicating how closely related the words in the cluster are and a semantic distance of the cluster from other clusters; selecting a top-k number of the ranked clusters; determining a document embedding as a weighted sum of word embeddings of words in the top-k number of ranked clusters, wherein the receiving, the creating, the ranking, the selecting, and the determining of the document embedding are performed for multiple documents; labeling each of the multiple documents; and training a machine learning algorithm based on the multiple documents that are labeled, wherein the training comprises separating the multiple documents as a training set and a test set, and generating a machine learning model that predicts a label for a given document based on the training set and the test set. 2. The method of claim 1, wherein the document is stored with the document embedding. 3. The method of claim 1, wherein the machine learning model comprises a support vector machine. 4. The method of claim 1, further comprising: receiving a given document to label; responsive to determining that the given document does not have a document embedding associated with the given document, generating the document embedding associated with the given document by performing the creating, the ranking, the selecting and the determining on the given document; executing the machine learning model based on the document embedding associated with the given document, the machine learning model predicting a label for the given document. 5. The method of claim 1, wherein the ranking the clusters based on cosine similarity of word embeddings of words in a cluster and word embeddings of words in the title, responsive to determining that the document has a title, comprises: determining cosine similarity between word embeddings of words in each of the clusters and a sum of word embeddings of words in the title; and the selecting comprises selecting the top-k number of clusters determined to be most similar to the title based on the cosine similarity between word embeddings of words in each of the clusters and a sum of word embeddings of words in the title. 6. The method of claim 1, wherein the compactness is measure by a variance of the words in the cluster. 7. The method of claim 1, wherein the creating clusters of words in the document based on cosine similarity of word embeddings of words in the document comprises: for a given word in the document, determining a cosine similarity between a word embedding of the given word and the word embeddings of an existing cluster; responsive to determining that the cosine similarity between the word embedding of the given word and the word embeddings of the existing cluster meets a defined threshold, placing the given word in the existing cluster; and responsive to determining that the cosine similarity between the word embedding of the given word and the word embeddings of the existing cluster does not meet the defined threshold, placing the given word in a new cluster. 8. A computer readable storage medium storing a program of instructions executable by a machine to perform a method of training a machine to learn to predict a label for data, the method performed by at least one hardware processor, the method comprising: receiving a document; creating clusters of words in the document based on cosine similarity of word embeddings of words in the document; responsive to determining that the document has a title, ranking the clusters based on cosine similarity of word embeddings of words in a cluster and word embeddings of words in the title; responsive to determining that the document has no title, ranking the clusters based on compactness of a cluster indicating how closely related the words in the cluster are and a semantic distance of the cluster from other clusters; selecting a top-k number of the ranked clusters; determining a document embedding as a weighted sum of word embeddings of words in the top-k number of ranked clusters, wherein the receiving, the creating, the ranking, the selecting, and the determining of the document embedding are performed for multiple documents; labeling each of the multiple documents; and training a machine learning algorithm based on the multiple documents that are labeled, wherein the training comprises separating the multiple documents as a training set and a test set, and generating a machine learning model that predicts a label for a given document based on the training set and the test set. 9. The computer readable storage medium of claim 8, wherein the document is stored with the document embedding. 10. The computer readable storage medium of claim 8, wherein the machine learning model comprises a support vector machine. 11. The computer readable storage medium of claim 8, further comprising: receiving a given document to label; responsive to determining that the given document does not have a document embedding associated with the given document, generating the document embedding associated with the given document by performing the creating, the ranking, the selecting and the determining on the given document; executing the machine learning model based on the document embedding associated with the given document, the machine learning model predicting a label for the given document. 12. The computer readable storage medium of claim 8, wherein the ranking the clusters based on cosine similarity of word embeddings of words in a cluster and word embeddings of words in the title, responsive to determining that the document has a title, comprises: determining cosine similarity between word embeddings of words in each of the clusters and a sum of word embeddings of words in the title; and the selecting comprises selecting the top-k number of clusters determined to be most similar to the title based on the cosine similarity between word embeddings of words in each of the clusters and a sum of word embeddings of words in the title. 13. The computer readable storage medium of claim 8, wherein the compactness is measure by a variance of the words in the cluster. 14. The computer readable storage medium of claim 8, wherein the creating clusters of words in the document based on cosine similarity of word embeddings of words in the document comprises: for a given word in the document, determining a cosine similarity between a word embedding of the given word and the word embeddings of an existing cluster; responsive to determining that the cosine similarity between the word embedding of the given word and the word embeddings of the existing cluster meets a defined threshold, placing the given word in the existing cluster; and responsive to determining that the cosine similarity between the word embedding of the given word and the word embeddings of the existing cluster does not meet the defined threshold, placing the given word in a new cluster. 15. A system of training a machine to learn to predict a label for data, comprising: a storage device; and hardware processor coupled to the storage device, the hardware processor operable to receive a document; the hardware processor further operable to create clusters of words in the document based on cosine similarity of word embeddings of words in the document; the hardware processor further operable to, responsive to determining that the document has a title, rank the clusters based on cosine similarity of word embeddings of words in a cluster and word embeddings of words in the title; the hardware processor further operable to, responsive to determining that the document has no title, rank the clusters based on compactness of a cluster indicating how closely related the words in the cluster are and a semantic distance of the cluster from other clusters; the hardware processor further operable to select a top-k number of the ranked clusters; the hardware processor further operable to determine a document embedding as a weighted sum of word embeddings of words in the top-k number of ranked clusters, wherein the hardware processor determines document embeddings for multiple documents and store the multiple documents with document embeddings in the storage device; the hardware processor further operable to label each of the multiple documents; and the hardware processor further operable to generate a machine learning model that predicts a label for a given document by training the machine learning model based on the multiple documents separated into the training set and the test set. 16. The system of claim 15, wherein the machine learning model comprises a support vector machine. 17. The system of claim 15, wherein the hardware processor is further operable to receive a given document to label, execute the machine learning model based on a document embedding associated with the given document, the machine learning model predicting a label for the given document. 18. The system of claim 17, wherein responsive to determining that the given document does not have the associated document embedding, generating the associated document embedding. 19. The system of claim 15, wherein the hardware processor is operable to determine cosine similarity between word embeddings of words in each of the clusters and a sum of word embeddings of words in the title, and operable to select the top-k number of clusters determined to be most similar to the title based on the cosine similarity between word embeddings of words in each of the clusters and a sum of word embeddings of words in the title, for ranking the clusters responsive to determining that the document has a title. 20. The system of claim 15, wherein to create the clusters of words, the hardware processor is operable to: for a given word in the document, determine a cosine similarity between a word embedding of the given word and the word embeddings of an existing cluster; responsive to determining that the cosine similarity between the word embedding of the given word and the word embeddings of the existing cluster meets a defined threshold, place the given word in the existing cluster; and responsive to determining that the cosine similarity between the word embedding of the given word and the word embeddings of the existing cluster does not meet the defined threshold, place the given word in a new cluster.