公开(公告)号：US11836256B2

公开(公告)日：2023-12-05

申请号：US16256107

申请日：2019-01-24

Applicant: International Business Machines Corporation

Inventor： Pin-Yu Chen ,  Sijia Liu ,  Lingfei Wu ,  Chia-Yu Chen

IPC: G06F21/57 ,  G06N3/04 ,  G06N3/08 ,  G06V10/764 ,  G06V10/82

CPC classification number: G06F21/577 ,  G06N3/04 ,  G06N3/08 ,  G06V10/764 ,  G06V10/82 ,  G06F2221/034

Abstract: An adversarial robustness testing method, system, and computer program product include testing a robustness of a black-box system under different access settings via an accelerator.

公开(公告)号：US11823013B2

公开(公告)日：2023-11-21

申请号：US15689799

申请日：2017-08-29

Applicant: International Business Machines Corporation

Inventor： Michael J. Witbrock ,  Lingfei Wu

IPC: G06N20/00 ,  G06F16/33

CPC classification number: G06N20/00 ,  G06F16/3331

Abstract: Embodiments of the present invention provide a computer-implemented method for performing unsupervised feature representation learning for text data. The method generates reference text data having a set of random text sequences, in which each text sequence of set of random text sequences is of a random length and comprises a number of random words, and in which each random length is sampled from a minimum length to a maximum length. The random words of each text sequence in the set are drawn from a distribution. The method generates a feature matrix for raw text data based at least in part on a set of computed distances between the set of random text sequences and the raw text data. The method provides the feature matrix as an input to one or more machine learning models.

公开(公告)号：US20230244555A1

公开(公告)日：2023-08-03

申请号：US18296777

申请日：2023-04-06

Applicant: International Business Machines Corporation

Inventor： Rajarshi Haldar ,  Yu Deng ,  Lingfei Wu ,  Ruchi Mahindru ,  Shu Tao

IPC: G06F9/54 ,  G06F40/205 ,  G06F16/38 ,  G06N20/00 ,  G06F16/901 ,  G06N3/088 ,  G06F16/35

CPC classification number: G06F9/542 ,  G06F40/205 ,  G06F16/38 ,  G06N20/00 ,  G06F16/9024 ,  G06N3/088 ,  G06F16/35

Abstract: Computer-implemented techniques for unsupervised event extraction are provided. In one instance, a computer implemented method can include parsing, by a system operatively coupled to a processor, unstructured text comprising event information to identify candidate event components. The computer implemented method can further include employing, by the system, one or more unsupervised machine learning techniques to generate structured event information defining events represented in the unstructured text based on the candidate event components.

公开(公告)号：US11669680B2

公开(公告)日：2023-06-06

申请号：US17165440

申请日：2021-02-02

Applicant: International Business Machines Corporation

Inventor： Lingfei Wu ,  Tengfei Ma ,  Tian Gao ,  Xiaojie Guo

IPC: G06F40/205 ,  G06F16/28 ,  G06F40/279 ,  G06N3/08 ,  G06N3/044 ,  G06N3/045

CPC classification number: G06F40/205 ,  G06F16/288 ,  G06F40/279 ,  G06N3/044 ,  G06N3/045 ,  G06N3/08

Abstract: A set of sentences within a natural language text document are parsed, generating a word-level graph corresponding to a sentence in the set of sentences. Within the word-level graph using a trained entity identification model, a set of entity candidates are identified. From a set of graphs modelling relationships between portions of the set of sentences, a set of embeddings is generated. From a set of pairs of embeddings in the set of embeddings using a set of deconvolution layers, a set of links between entity candidates within the set of entity candidates is extracted. From the set of links and the set of entity candidates, an output graph modelling linkages between portions of the set of sentences within the natural language text document is generated.

公开(公告)号：US20220171923A1

公开(公告)日：2022-06-02

申请号：US17109008

申请日：2020-12-01

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION ,  THE BOARD OF TRUSTEES OF THE UNIVERSITY OF ILLINOIS

Inventor： Lingfei Wu ,  Jinjun Xiong ,  Hongyu Gong ,  Suma Bhat ,  Wen-Mei Hwu

IPC: G06F40/211 ,  G06N3/04 ,  G06N3/08 ,  G06K9/62 ,  G06N7/00

Abstract: A computer-implemented method for generating an abstract meaning representation (“AMR”) of a sentence, comprising receiving, by a computing device, an input sentence and parsing the input sentence into one or more syntactic and/or semantic graphs. An input graph including a node set and an edge set is formed from the one or more syntactic and/or semantic graphs. Node representations are generated by natural language processing. The input graph is provided to a first neural network to provide an output graph having learned node representations aligned with the node representations in the input graph. The method further includes predicting via a second neural network, node label and predicting, via a third neural network, edge labels in the output graph. The AMR is generated based on the predicted node labels and predicted edge labels. A system and a non-transitory computer readable storage medium are also disclosed.

公开(公告)号：US11321541B2

公开(公告)日：2022-05-03

申请号：US16919208

申请日：2020-07-02

Applicant: International Business Machines Corporation

Inventor： Lingfei Wu ,  Chen Wang

IPC: G06F40/58 ,  G06F40/47 ,  G06F16/901 ,  G06N3/08 ,  G06F16/332 ,  G06F40/20 ,  G06N3/04 ,  G06N3/02

Abstract: Technology for using a bi-directed graph convolutional neural network (“BGCNN”) to convert RDF data into natural language text. Some embodiments perform RDF-to-Text generation by learning graph-augmented structural neural encoders, consisting of: (a) bidirected graph-based meta-paths encoder; (b) bidirected graph convolutional networks encoder, and (c) separated attention mechanism for combining encoders and decoder to translate RDF triplets to natural language description.

公开(公告)号：US11314950B2

公开(公告)日：2022-04-26

申请号：US16830106

申请日：2020-03-25

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION ,  The Board of Trustees of the University of Illinois

Inventor： Lingfei Wu ,  Jinjun Xiong ,  Hongyu Gong ,  Suma Bhat ,  Wen-Mei Hwu

IPC: G06F40/56 ,  G06F40/253 ,  G06F40/35 ,  G06N3/04

Abstract: A computer-implemented method is provided for transferring a target text style using Reinforcement Learning (RL). The method includes pre-determining, by a Long Short-Term Memory (LSTM) Neural Network (NN), the target text style of a target-style natural language sentence. The method further includes transforming, by a hardware processor using the LSTM NN, a source-style natural language sentence into the target-style natural language sentence that maintains the target text style of the target-style natural language sentence. The method also includes calculating an accuracy rating of a transformation of the source-style natural language sentence into the target-style natural language sentence based upon rewards relating to at least the target text style of the source-style natural language sentence.

公开(公告)号：US20220113964A1

公开(公告)日：2022-04-14

申请号：US17069402

申请日：2020-10-13

Applicant: International Business Machines Corporation

Inventor： Dakuo Wang ,  Lingfei Wu ,  Yi Wang ,  Xuye Liu ,  Chuang Gan ,  Si Er Han ,  Bei Chen ,  Ji Hui Yang

IPC: G06F8/73 ,  G06N20/00 ,  G06F40/169

Abstract: One embodiment of the invention provides a method for automated code annotation in machine learning (ML) and data science. The method comprises receiving, as input, a section of executable code. The method further comprises classifying, via a ML model, the section of executable code with a stage classification label indicative of a stage within a workflow for automated ML that the executable code applies to. The method further comprises categorizing, based on the stage classification label, the section of executable code with a category of annotation that is most appropriate for the section of executable code. The method further comprises generating a suggested annotation for the section of executable code based on the category of annotation. The method further comprises providing, as output, the suggested annotation to a display of an electronic device for user review. The suggested annotation is user interactable via the electronic device.

公开(公告)号：US11157705B2

公开(公告)日：2021-10-26

申请号：US16518120

申请日：2019-07-22

Applicant: International Business Machines Corporation

Inventor： Lingfei Wu ,  Wei Zhang

IPC: G06F40/30 ,  G06F40/211

Abstract: Aspects described herein include a method of semantic parsing, and related system and computer program product. The method comprises receiving an input comprising a plurality of words, generating a structured representation of the plurality of words, encoding the structured representation into a latent embedding space, and decoding the encoded structured representation from the latent embedding space into a logical representation of the plurality of words.

公开(公告)号：US20210141863A1

公开(公告)日：2021-05-13

申请号：US16678341

申请日：2019-11-08

Applicant: International Business Machines Corporation ,  The Board of Trustees of the University of Illinois

Inventor： Lingfei Wu ,  Jinjun Xiong ,  Julia Constanze Hockenmaier ,  Rajarshi Haldar

IPC: G06F17/27 ,  G06N3/08 ,  G06N3/04 ,  G06F16/2452

Abstract: Embodiments of the invention describe a computer-implemented method that includes receiving a query that includes a query sequence having query characters grouped into query words. A segment of program code is retrieved from a database for evaluation. The program code includes a program code sequence including program code characters grouped into program code words. The query sequence, the query word, the program code sequence, and the program code word are each converted to sequence and word representations. Query sequence-level features, query word-level features, program code sequence-level features, and program code word-level features are extracted from the sequence and word representation. Similarity between the query and the segment of program code is determined by applying a similarity metric technique to the query sequence-level features, the query word-level features, the program code sequence-level features, and the program code word-level features.