Combining Data and Mathematical Models of Language Change
Sonderegger, Morgan and Niyogi, Partha

Article Structure

Abstract

English nourflverb (NN) pairs (contract, cement) have undergone complex patterns of change between 3 stress patterns for several centuries.

Introduction

The fascinating phenomena of language evolution and language change have inspired much work from computational perspectives in recent years.

Data: English N/V pairs

The data considered here are the stress patterns of English homographic, disyllabic nourflverb pairs (Table 1); we refer to these throughout as “NN pairs”.

Modeling preliminaries

We first describe assumptions and notation for models developed below (§4).

Models

We now describe 5 DS models, each corresponding to a learning algorithm A used by individual language learners.

Discussion

We have developed 5 dynamical systems models for a relatively complex diachronic change, found one successful model, and were able to reason about the source of model behavior.

Topics

learning algorithm

Appears in 5 sentences as: learning algorithm (4) learning algorithms (1)
In Combining Data and Mathematical Models of Language Change
  1. This setting allows us to determine the diachronic, population-level consequences of assumptions about the learning algorithm used by individuals, as well as assumptions about population structure or the input they receive.
    Page 4, “Modeling preliminaries”
  2. We now describe 5 DS models, each corresponding to a learning algorithm A used by individual language learners.
    Page 5, “Models”
  3. The models differ along two dimensions, corresponding to assumptions about the learning algorithm (A): whether or not it is assumed that the stress of examples is possibly mistransmitted (Models 1, 3, 5), and how the N and V probabil-
    Page 5, “Models”
  4. Each model describes the diachronic, population-level consequences of assuming a particular learning algorithm for individuals.
    Page 8, “Discussion”
  5. By using simple models, we were able to consider a range of learning algorithms corresponding to different explanations for the observed diachronic dynamics.
    Page 9, “Discussion”

See all papers in Proc. ACL 2010 that mention learning algorithm.

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