International Business Machines Corporation

Tencent America LLC

Kun Xu

Lingfei Wu

Dong Yu

Wei Zhang

Vadim Sheinin

Chao Weng

Chengzhu Yu

Zhiguo Wang

Chia-Yu Chen

Pin-Yu Chen

A method (and structure and computer product) of machine translation for processing input questions includes receiving, in a processor on a computer, an input question presented in a natural language. The input question is preprocessed to find one or more condition values for possible Structured Query Language (SQL) queries. One or more possible SQL queries are enumerated based on the one or more found condition values and a paraphrasing model is used to rank the enumerated SQL queries. The highest ranked SQL query is executed against a relational database to search for a response to the input question.

Natural language to structured query generation via paraphrasing

Task-dependent analysis of various types of data graphs is provided, based at least on generation of a random graph based on node embeddings corresponding to a data graph, and a graph feature matrix corresponding to the data graph based on a distance between the random graph and the data graph.

Graph analytics using random graph embedding

Method and apparatus for automatically predicting lexical sememes using a lexical dictionary, comprising inputting a word, retrieving the word's semantic definition and sememes corresponding to the word from an online dictionary, setting each of the retrieved sememes as a candidate sememe, inputting the word's semantic definition and candidate sememe, and estimating the probability that the candidate sememe can be inferred from the word's semantic definition.

Automatic lexical sememe prediction system using lexical dictionaries

A method and system of analyzing a symbolic sequence is provided. Metadata of a symbolic sequence is received from a computing device of an owner. A set of R random sequences are generated based on the received metadata and sent to the computing device of the owner of the symbolic sequence for computation of a feature matrix based on the set of R random sequences and the symbolic sequence. The feature matrix is received from the computing device of the owner. Upon determining that an inner product of the feature matrix is below a threshold accuracy, the iterative process returns to generating R random sequences. Upon determining that the inner product of the feature matrix is at or above the threshold accuracy, the feature matrix is categorized based on machine learning. The categorized global feature matrix is sent to be displayed on a user interface of the computing device of the owner.

Computational efficiency in symbolic sequence analytics using random sequence embeddings

Systems, computer-implemented methods, and computer program products that facilitate task-dependent analysis of various types of data graphs, based at least on generation of a random graph based on node embeddings corresponding to a data graph, and compution of a graph feature matrix corresponding to the data graph based on a distance between the random graph and the data graph.

A semantic parsing method using a graph-to-sequence model, system, and computer program product include generating a syntactic graph for a sentence, generating node embeddings for each node based on other nodes the each node is connected to in the syntactic graph, generating a graph embedding over the node embeddings, performing attention-based recurrent neural network (RNN) decoding of the graph embedding and the node embeddings, and providing a logical translation of the sentence based on the decoding.

Growing community of inventors

Yorktown Heights, NY, United States of America

Kun Xu