Combining unsupervised and semi-supervised deep clustering approaches for mining intentions from texts

1. A system for mining latent intentions from natural language inputs, the system comprising:

a computing device that maintains a plurality of natural language inputs; and

an analysis platform that uses a plurality of unsupervised and semi-supervised approaches to surface and organize a plurality of relevant user intentions from the plurality of natural language inputs, wherein the analysis platform comprises:

a language models fine-tuning module;

a K-nearest neighbor (KNN)-graph building module; and

a clustering module.

2. The system of claim 1 , wherein the language models fine-tuning module is configured to fine-tune a plurality of language models based on the plurality of natural language inputs.

3. The system of claim 1 , wherein the language models fine-tuning module is configured to tokenize a plurality of labeled texts and unlabeled texts into a plurality of language models.

4. The system of claim 1 , wherein the KNN-graph building module is configured to build a distributed KNN-graph.

5. The system of claim 1 , wherein the clustering module comprises a clustering technique that requires a number of clusters to be known ahead of time, and a clustering technique that is graph-based that does not require the number of clusters to be known ahead of time.

6. The system of claim 1 , wherein the clustering module is configured to perform clustering based on whether a number of clusters is known or unknown, wherein when the number of clusters is unknown, then a Louvain clustering technique is used, and when the number of clusters is known, then a K-means clustering technique is used.

7. The system of claim 1 , wherein the clustering module is configured to perform clustering based on whether a number of clusters is predetermined or detected automatically, wherein when the number of clusters is detected automatically, then a Louvain clustering technique is used, and when the number of clusters is predetermined, then a K-means clustering technique is used.

8. The system of claim 1 , further comprising an intention mining module.

9. The system of claim 8 , wherein the intention mining module is configured to design and refine a plurality of Intelligent Virtual Assistants (IVAs) for customer service and sales support.

10. The system of claim 1 , further comprising an output device that receives an output from the analysis platform and determines a plurality of latent intentions using the output.

11. An analysis platform stored on one or more computer-readable tangible storage media, the platform comprising:

a language models fine-tuning module that fine-tunes a plurality of language models;

a K-nearest neighbor (KNN)-graph building module that builds a distributed KNN-graph;

a clustering module that comprises a K-means clustering technique and a Louvain clustering technique, wherein the clustering module is configured to perform clustering based on whether a number of clusters is known or unknown; and

an intention mining module that mines a plurality of latent intentions from a plurality of natural language inputs and an output from the clustering module.

12. The analysis platform of claim 11 , wherein the language models fine-tuning module fine-tunes a plurality of language models based on the plurality of natural language inputs.

13. The analysis platform of claim 11 , wherein the intention mining module is configured to design and refine a plurality of Intelligent Virtual Assistants (IVAs) for customer service and sales support.

14. The analysis platform of claim 11 , wherein when the number of clusters is unknown, then the Louvain clustering technique is used, and when the number of clusters is known, then the K-means clustering technique is used.

15. The analysis platform of claim 11 , wherein when the number of clusters is detected automatically, then the Louvain clustering technique is used, and when the number of clusters is predetermined, then the K-means clustering technique is used.

16. A method for mining latent intentions from natural language inputs, the method comprising:

receiving a plurality of language models based on a plurality of natural language inputs;

fine-tuning the plurality of language models;

performing clustering using the plurality of fine-tuned language models; and

determining a plurality of latent intentions based on results of the clustering;

wherein performing clustering comprises performing clustering based on whether a number of clusters is known or unknown, wherein when the number of clusters is unknown, then a Louvain clustering technique is used, and when the number of clusters is known, then a K-means clustering technique is used.

17. The method of claim 16 , wherein fine-tuning the plurality of language models comprises encoding the plurality of language models and using a softmax classifier to fine-tune the plurality of language models.

18. The method of claim 16 , further comprising building a K-nearest neighbor (KNN)-graph using the plurality of language models, when a number of clusters for performing the clustering is unknown or detected automatically.

19. A method for mining latent intentions from natural language inputs, the method comprising:

receiving a plurality of language models based on a plurality of natural language inputs;

fine-tuning the plurality of language models;

performing clustering using the plurality of fine-tuned language models; and

determining a plurality of latent intentions based on results of the clustering;

wherein performing clustering comprises performing clustering based on whether a number of clusters is predetermined or detected automatically, wherein when the number of clusters is detected automatically, then a Louvain clustering technique is used, and when the number of clusters is predetermined, then a K-means clustering technique is used.