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Quantum-like generalization of complex word embedding: a lightweight approach for textual classification.

  • Haiming Liu
    ,
  • Ingo Frommholz
    ,
  • Amit Kumar Jaiswal
    ,
  • Guilherme Holdack
Research Output:
Contribution to conference
Paper
 Peer-review

Open access

Abstract

In this paper, we present an extension, and an evaluation, to existing Quantum like approaches of word embedding for IR tasks that (1) improves complex features detection of word use (e.g., syntax and semantics), (2) enhances how this method extends these aforementioned uses across linguistic contexts (i.e., to model lexical ambiguity) - specifically Question Classification -, and (3) reduces computational resources needed for training and operating Quantum based neural networks, when confronted with existing models. This approach could also be latter applicable to significantly enhance the state-of the-art across Natural Language Processing (NLP) word-level tasks such as entity recognition, part-of-speech tagging, or sentence-level ones such as textual relatedness and entailment, to name a few.

Publication Information

Output type

Research Output:
Contribution to conference
Paper
 Peer-review

Original language

English

Publication milestones

  • Published - 30/09/2018

Publication status

Published - 30/09/2018

Publication IDs

  • handle.net: 10547/623794

Access to documents

Related Event

Title

Lernen, Wissen, Daten, Analysen 2018

Description

Lernen, Wissen, Daten, Analysen 2018 (22/08/2018-24/08/2018, Mannheim)

Event type

Conference

Date

22/08/2018 - 24/08/2018

Location

Mannheim