Automatic clustering of tokens from a corpus for grammar acquisition
View Patent ↗A method of grammar learning from a corpus comprises, for the other non-context words, generating frequency vectors for each non-context token in a corpus based upon counted occurrences of a predetermined relationship of the non-context tokens to identified context tokens. Clusters are grown from the frequency vectors according to a lexical correlation among the non-context tokens.
1. A machine-readable medium having stored thereon executable instructions that when executed by a processor, cause the processor to:
generate frequency vectors for each non-context token in a corpus based upon counted occurrences of a predetermined relationship of the non-context tokens to context tokens; and
cluster the non-context tokens into a cluster tree based upon the frequency vectors according to a lexical correlation among the non-context tokens, wherein the cluster tree is used in a pattern recognition system.
2. A method of grammar learning from a corpus, comprising:
generating frequency vectors for each non-context token in a corpus based upon counted occurrences of a predetermined relationship of the non-context tokens to context tokens; and
clustering the non-context tokens based upon the frequency vectors according to a lexical correlation among the non-context tokens, wherein the cluster tree is used in a pattern recognition system.
3. The method of claim 2 , wherein the step of clustering further comprises clustering the non-context tokens into a cluster tree.
4. The method of claim 3 , wherein the cluster tree represents a grammatical relationship among the non-context tokens.
5. The method of claim 3 , further comprising cutting the cluster tree along a cutting line to separate large clusters from small clusters.
6. The method of claim 2 , wherein small clusters are ranked according to a compactness value.
7. The method of claim 2 , wherein the predetermined relationship is a measure of adjacency.
8. The method of claim 2 , wherein the clustering is performed based on Euclidean distances between the frequency vectors.
9. The method of claim 2 , wherein the clustering is performed based on Manhattan distances between the frequency vectors.
10. The method of claim 2 , wherein the clustering is performed based on maximum distance metrics between the frequency vectors.
11. The method of claim 2 , further comprising normalizing the frequency vectors based upon a number of occurrences of the non-context token in the corpus.
12. The method of claim 2 , wherein the frequency vectors are multi-dimensional vectors, the number of dimensions being determined by the number of context tokens and a number of predetermined relationships of non-context tokens to the context token being counted.
13. A file storing a grammar model of a corpus of speech, created according to a method comprising:
generating frequency vectors for each non-context token in a corpus based upon counted occurrences of a predetermined relationship of the non-context tokens to context tokens;
clustering the non-context tokens into a cluster based upon the frequency vectors according to a lexical correlation among the non-context tokens; and
storing the non-context tokens and a representation of the clusters in a file for use in a pattern recognition system.
14. The file of claim 13 , wherein the clusters may be represented by centroid vectors.
15. The file of claim 13 , wherein the predetermined relationship is adjacency.
16. The file of claim 13 , wherein the correlation is based on Euclidean distance.
17. The file of claim 13 , wherein the correlation is based on Manhattan distance.
18. The file of claim 13 , wherein the correlation is based on a maximum distance metric.
19. The file of claim 13 , wherein the frequency vectors are normalized based upon the number of occurrences of the non-context token in the corpus.
20. The file of claim 13 , wherein the frequency vectors are multi-dimensional vectors, the number of dimensions of which is determined by the number of context tokens and the number of predetermined relationships of non-context tokens to context tokens.