International Business Machines Corporation

Elaine M Branagh

Pitipong Jun Sen Lin

Zhong Su

Shiwan Zhao

Honglei Guo

Zhili Guo

Song Xu

Zhong Fang Yuan

Akihiro Kishimoto

Tong Liu

Aliza Rivka Heching

Randall Lee Cogill

Joe Naoum-Sawaya

Chen Yu Chang

Pitipong J Lin

Nicole L Lechelt

Yin Long

Surya S K Sajja

An example operation may include one or more of generating a plurality of bounding boxes at a plurality of content areas in an image corresponding to a plurality of pieces of text within the image, converting the plurality of bounding boxes into a plurality of bounding box vectors based on attributes of the plurality of bounding boxes, training a machine learning model to transform a bounding box into a location in vector space based on the plurality of bounding box vectors, and storing the trained machine learning model in memory.

Visualization of the impact of training data using bounding box vectors to train a machine learning model

A method is provided for role-oriented risk analysis in a contract. The method generates, using deep semantic association analysis, a report specifying a set of potential risks relating to explicit and hidden roles of contract parties. The generating step categorizes input statements of the contract into respective obligation/right pairs according to a deep semantic association distribution thereof. Each pair includes a respective obligation and a respective right. The generating step detects deep semantic differences between the respective pairs and a set of reference obligation/right pairs. The generating step identifies the explicit and hidden roles of the involved parties in the respective obligations/rights pairs according to domain-specific use scenarios and multidimensional local and global context clues in the contract. The generating step identifies the set of potential risks by applying a deep semantic role-oriented risk entailment model to the deep semantic differences.

Role-oriented risk checking in contract review based on deep semantic association analysis

An aspect of providing job assignment optimization includes creating a feature vector from features extracted from a new job request. Each of the features is associated with a constraint on minimum requirements with respect to a level of experience of a potential candidate. An aspect further includes creating a feature vector for each employee that includes skills attributed to the employee and instances that each of the skills have been completed, searching feature vectors generated from the employee database for the minimum requirements indicated in the new job request feature vector, searching the feature vectors of the database for the minimum requirements indicated by constraints associated with the new job request, modeling aggregate utility of past experience of employees as a weighted sum of applied utility functions, and generating a score indicating a difference between a potential post assignment and a pre-assignment utility. An aspect also includes outputting qualified candidates.

Job assignment optimization

Methods and systems for natural language processing include generating respective feature vectors, for each word in an input, based on sequences of input words of different respective lengths. The respective feature vectors for each word in the input are combined to form a combined vector for each word. A hidden state is determined for each word in the input based on the combined vector. The hidden states for all words in the input are combined to form a multi-resolution context vector. A natural language processing action is performed using the multi-resolution context vector.

Multi-resolution convolutional neural networks for sequence modeling

A method is provided for clause analysis in a legal domain. The method builds a coherence graph from a set of labeled training documents by (a) creating entity nodes from and of a same type as entities extracted from the set of labeled training documents, (b) creating clause nodes from labeled clauses in the set of labeled training documents, (c) forming bi-directional edges (i) between each of the clause nodes and the entity nodes belonging thereto, (ii) among parent-child clause nodes from among the clause nodes, and (iii) among same-level sibling clause nodes from among the clause nodes. The method merges nodes, from among the entity and clause nodes, that have a same semantic meaning. The method weights the bi-directional edges using a coherence metric. The method identifies a clause structure of a new document by matching the new document against the coherence graph using a node-covering algorithm.

Clause analysis based on collection coherence in legal domain

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Austin, TX, United States of America

Elaine M Branagh