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IEICE TRANSACTIONS on Fundamentals

Unified Likelihood Ratio Estimation for High- to Zero-Frequency N-Grams

Masato KIKUCHI, Kento KAWAKAMI, Kazuho WATANABE, Mitsuo YOSHIDA, Kyoji UMEMURA

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Summary :

Likelihood ratios (LRs), which are commonly used for probabilistic data processing, are often estimated based on the frequency counts of individual elements obtained from samples. In natural language processing, an element can be a continuous sequence of N items, called an N-gram, in which each item is a word, letter, etc. In this paper, we attempt to estimate LRs based on N-gram frequency information. A naive estimation approach that uses only N-gram frequencies is sensitive to low-frequency (rare) N-grams and not applicable to zero-frequency (unobserved) N-grams; these are known as the low- and zero-frequency problems, respectively. To address these problems, we propose a method for decomposing N-grams into item units and then applying their frequencies along with the original N-gram frequencies. Our method can obtain the estimates of unobserved N-grams by using the unit frequencies. Although using only unit frequencies ignores dependencies between items, our method takes advantage of the fact that certain items often co-occur in practice and therefore maintains their dependencies by using the relevant N-gram frequencies. We also introduce a regularization to achieve robust estimation for rare N-grams. Our experimental results demonstrate that our method is effective at solving both problems and can effectively control dependencies.

Publication
IEICE TRANSACTIONS on Fundamentals Vol.E104-A No.8 pp.1059-1074
Publication Date
2021/08/01
Publicized
2021/02/08
Online ISSN
1745-1337
DOI
10.1587/transfun.2020EAP1088
Type of Manuscript
PAPER
Category
Mathematical Systems Science

Authors

Masato KIKUCHI
  Nagoya Institute of Technology
Kento KAWAKAMI
  LINE Corporation
Kazuho WATANABE
  Toyohashi University of Technology
Mitsuo YOSHIDA
  Toyohashi University of Technology
Kyoji UMEMURA
  Toyohashi University of Technology

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