International Business Machines Corporation

Other

Julie T Yu

Charles Evan Beller

Kristen Maria Summers

Michael Drzewucki

Christopher Phipps

William G Dubyak

Alexander Charles Tonetti

Sean Thomas Thatcher

Sean L Bethard

Robert M Riggs

Andrew James Pierce

Greg K Justice

Dan Beltzer

Jacob Drewniak

Mike Mosman

This disclosure describes, in part, systems for tracking the removal of shopping bags or other items from a dispenser. Folded shopping bags are stacked in a holder so that a front shopping bag can be removed by pulling it along an axis that is parallel to the length and/or width dimension of the shopping bag. A motion sensor is positioned to sense movement of the front shopping bag as it is removed. Control logic monitors and/or analyzes data or other signals from the motion sensor and determines whether movement of at least a threshold distance has occurred. If movement of at least the threshold distance has occurred, the control logic concludes that a shopping bag has been removed and generates a corresponding event notice. Other system components may respond to the event notice by applying a charge for the removed shopping bag or by performing other actions.

Item dispenser with motion-based removal detection

Embodiments can provide a computer implemented method, in a data processing system comprising a processor and a memory comprising instructions which are executed by the processor to cause the processor to implement an improved search query generation system, the method comprising inputting a natural language question; parsing the natural language question into a parse tree; identifying argument positions comprising one or more argument position terms; for each argument position: comparing a head term's discriminator score against a threshold discriminator score; and if the head term surpasses the threshold discriminator score, adding the head term as a required term to an improved search query; and outputting the improved search query.

System and method for generating improved search queries from natural language questions

A mechanism is provided in a data processing system for identifying nonsense passages. An annotator in a natural language processing pipeline configured to execute in the data processing system annotates an input passage in a corpus with linguistic features to form an annotated passage. A domain-specific policy is associated with a domain of the corpus. A metric counters component in the natural language processing pipeline counts a number of instances of each type of linguistic feature in the annotated passage to form a set of feature counts. The metric counters component of the natural language processing pipeline determines a value for a metric based on the set of feature counts. The metric is specified in the domain-specific policy. A comparator component of the natural language processing pipeline compares the value for the metric to a predetermined model threshold. The threshold is specified in the domain-specific policy. A filter component of the natural language processing pipeline identifies whether the input passage is a nonsense passage based on a result of the comparison.

Identifying nonsense passages in a question answering system based on domain specific policy

A mechanism is provided in a data processing system for identifying nonsense passages. The mechanism annotates an input passage with linguistic features to form an annotated passage. The mechanism counts a number of instances of each type of linguistic feature in the annotated passage to form a set of feature counts. The mechanism determines a value for a metric based on the set of feature counts and compares the value for the metric to a predetermined model threshold. The mechanism identifies whether the input passage is a nonsense passage based on a result of the comparison.

Post-processing for identifying nonsense passages in a question answering system

A mechanism is provided in a data processing system for identifying nonsense passages in documents being ingested into a corpus. A natural language processing pipeline configured to execute in the data processing system receives an input document to be ingested into a corpus. The natural language processing pipeline divides the input document into a plurality of input passages. A filter component of the natural language processing pipeline identifies whether each input passage is a nonsense passage based on a value of a metric determined according to a set of feature counts. The natural language processing pipeline filters each input passage in the plurality of input passages based on whether the input passage is identified as a nonsense passage or not identified as a nonsense passage to form a filtered plurality of input passages. The natural language processing pipeline adds the filtered plurality of input passages into the corpus.

Pre-processing for identifying nonsense passages in documents being ingested into a corpus of a natural language processing system

Growing community of inventors

Chantilly, VA, United States of America

Julie T Yu