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Electronic workshops in computing

Eraser Lattices for Documents and Sets of Documents

Álvaro Francisco Huertas-Rosero

Published Sep 1, 2009• 0 citations
Publisher

Abstract

Automatic schemes for the analysis of Natural Language based on word cooccurrence counting have been very successful in capturing meaning, like automatically grouping words referring to similar concepts, or documents about similar topics. In this work, a more general framework is proposed to represent documents and measurements geometrically, in a way directly related with the representation of measurement in Quantum Theory.

DOI
10.14236/ewic/fdia2009.25

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Automatic schemes for the analysis of Natural Language based on word cooccurrence counting have been very successful in capturing meaning, like automatically grouping words referring to similar concepts, or documents about similar topics. In this work, a more general framework is proposed to represent documents and measurements geometrically, in a way directly related with the representation of measurement in Quantum Theory.
The problem of representing text documents within an Information Retrieval system is formulated as an analogy to the problem of representing the quantum states of a physical system. Lexical measurements on text are proposed as a way of representing documents which is akin to physical measurements on quantum states. The representation of the text is only known after measurements have been made, and because the process of measuring may destroy parts of the text, the document is characterised through erasure. These so called “Selective Erasers” provide the basis for representation [1]. During my presentation, the mathematical foundations of such a quantum representation of text will be presented and discussed in the context of indexing and retrieval within a “quantum like” Information Retrieval system. The presentation will then outline the various directions for future work using Selective Erasers.
Conceptual entanglement is a crucial phenomenon in quantum cognition because it implies that classical probabilities cannot model non--compositional conceptual phenomena. While several psychological experiments have been developed to test conceptual entanglement, this has not been explored in the context of Natural Language Processing. In this paper, we apply the hypothesis that words of a document are traces of the concepts that a person has in mind when writing the document. Therefore, if these concepts are entangled, we should be able to observe traces of their entanglement in the documents. In particular, we test conceptual entanglement by contrasting language simulations with results obtained from a text corpus. Our analysis indicates that conceptual entanglement is strongly linked to the way in which language is structured. We discuss the implications of this finding in the context of conceptual modeling and of Natural Language Processing.
Abstract Automatic text processing is now a mature discipline in computer science, and so attempts at advancements using quantum computation have emerged as the new frontier, often under the term of quantum natural language processing. The main challenges consist in finding the most adequate ways of encoding words and their interactions on a quantum computer, considering hardware constraints, as well as building algorithms that take advantage of quantum architectures, so as to show improvement on the performance of natural language tasks. In this paper, we introduce a new framework that starts from a grammar that can be interpreted by means of tensor contraction, to build word representations as quantum states that serve as input to a quantum algorithm. We start by introducing an operator measurement to contract the representations of words, resulting in the representation of larger fragments of text. We then go on to develop pipelines for the tasks of sentence meaning disambiguation and question answering that take advantage of quantum features. For the first task, we show that our contraction scheme deals with syntactically ambiguous phrases storing the various different meanings in quantum superposition, a solution not available on a classical setting. For the second task, we obtain a question representation that contains all possible answers in equal quantum superposition, and we implement Grover’s quantum search algorithm to find the correct answer, agnostic to the specific question, an implementation with the potential of delivering a result with quadratic speedup.