公开(公告)号：US11797594B2

公开(公告)日：2023-10-24

申请号：US17093722

申请日：2020-11-10

Applicant: Verint Americas Inc.

Inventor： Xinyu Chen ,  Ian Beaver

IPC: G06F16/35 ,  G06F16/36 ,  G06F40/289

CPC classification number: G06F16/355 ,  G06F16/367 ,  G06F40/289

Abstract: A set of documents related to a particular topic, industry, or entity are received. Sentences are extract from each document. The sentences are grouped into tuples of one, two, or three consecutive sentences (i.e., short text sequences). The sentence tuples are clustered based on vector representations of the sentences. For each cluster, a set of tuples that best represents or best fits the cluster is selected. These sentence tuples are fed to an ontology to determine ontological entities associated with each tuple. These determined ontological entities are associated with the clusters corresponding to each tuple. The sentence tuples associated with each cluster are labeled based on the ontological entities associated with the cluster. The labeled sentence tuples may then be used for a variety of purposes such as training a model to determine the topic of short text sequences.

公开(公告)号：US12153897B2

公开(公告)日：2024-11-26

申请号：US17605326

申请日：2021-09-17

Applicant: VERINT AMERICAS INC.

Inventor： Ian Beaver ,  Xinyu Chen

IPC: G06F40/40 ,  G06F18/2323 ,  G06F40/289

Abstract: An analysis platform combines unsupervised and semi-supervised approaches to quickly surface and organize relevant user intentions from conversational text (e.g., from natural language inputs). An unsupervised and semi-supervised pipeline is provided that integrates the fine-tuning of high performing language models via a language models fine-tuning module, a distributed KNN-graph building method via a KNN-graph building module, and community detection techniques for mining the intentions and topics from texts via an intention mining module.

公开(公告)号：US20230114897A1

公开(公告)日：2023-04-13

申请号：US17605326

申请日：2021-09-17

Applicant: VERINT AMERICAS INC.

Inventor： Ian Beaver ,  Xinyu Chen

IPC: G06F40/40 ,  G06F40/289 ,  G06F18/2323

Abstract: An analysis platform combines unsupervised and semi-supervised approaches to quickly surface and organize relevant user intentions from conversational text (e.g., from natural language inputs). An unsupervised and semi-supervised pipeline is provided that integrates the fine-tuning of high performing language models via a language models fine-tuning module, a distributed KNN-graph building method via a KNN-graph building module, and community detection techniques for mining the intentions and topics from texts via an intention mining module.

公开(公告)号：US20210173862A1

公开(公告)日：2021-06-10

申请号：US17093722

申请日：2020-11-10

Applicant: Verint Americas Inc.

Inventor： Xinyu Chen ,  Ian Beaver

IPC: G06F16/35 ,  G06F40/289 ,  G06F16/36

Abstract: A set of documents related to a particular topic, industry, or entity are received. Sentences are extract from each document. The sentences are grouped into tuples of one, two, or three consecutive sentences (i.e., short text sequences). The sentence tuples are clustered based on vector representations of the sentences. For each cluster, a set of tuples that best represents or best fits the cluster is selected. These sentence tuples are fed to an ontology to determine ontological entities associated with each tuple. These determined ontological entities are associated with the clusters corresponding to each tuple. The sentence tuples associated with each cluster are labeled based on the ontological entities associated with the cluster. The labeled sentence tuples may then be used for a variety of purposes such as training a model to determine the topic of short text sequences.