[Paper Review] Unsupervised Language Acquisition: Theory and Practice

Alexander Clark|ArXiv.org|Dec 10, 2002

Natural Language Processing TechniquesComputer Science279 references79 citations

TL;DR

This paper presents novel unsupervised algorithms for learning syntactic categories, morphological processes, and context-free grammars from untagged and tagged text using distributional and statistical models. It challenges the Argument from the Poverty of the Stimulus by demonstrating that natural language structures can be learned empirically without innate linguistic knowledge, supporting an empiricist model of language acquisition.

ABSTRACT

In this thesis I present various algorithms for the unsupervised machine learning of aspects of natural languages using a variety of statistical models. The scientific object of the work is to examine the validity of the so-called Argument from the Poverty of the Stimulus advanced in favour of the proposition that humans have language-specific innate knowledge. I start by examining an a priori argument based on Gold's theorem, that purports to prove that natural languages cannot be learned, and some formal issues related to the choice of statistical grammars rather than symbolic grammars. I present three novel algorithms for learning various parts of natural languages: first, an algorithm for the induction of syntactic categories from unlabelled text using distributional information, that can deal with ambiguous and rare words; secondly, a set of algorithms for learning morphological processes in a variety of languages, including languages such as Arabic with non-concatenative morphology; thirdly an algorithm for the unsupervised induction of a context-free grammar from tagged text. I carefully examine the interaction between the various components, and show how these algorithms can form the basis for a empiricist model of language acquisition. I therefore conclude that the Argument from the Poverty of the Stimulus is unsupported by the evidence.

Motivation & Objective

• To investigate whether natural languages can be learned without innate linguistic knowledge, challenging the Argument from the Poverty of the Stimulus.
• To develop practical unsupervised algorithms for learning syntactic categories, morphology, and context-free grammars from raw text.
• To examine the feasibility of an empiricist model of language acquisition using statistical and distributional learning techniques.
• To address theoretical limitations in Gold's theorem by showing that statistical grammars can overcome the learning barriers posited by formal language theory.
• To demonstrate that ambiguous and rare words can be reliably categorized using distributional information in unsupervised settings.

Proposed method

• Uses distributional information from unlabelled text to induce syntactic categories, handling ambiguity and rare words through co-occurrence patterns.
• Applies statistical models to learn morphological processes, including non-concatenative morphology in languages like Arabic, via pattern discovery in word forms.
• Employs a novel algorithm for unsupervised induction of context-free grammars from tagged text, leveraging part-of-speech and structural information.
• Combines multiple learning components into a cohesive framework, analyzing their interactions for robust language acquisition.
• Relies on statistical grammars rather than symbolic grammars to circumvent limitations imposed by Gold's theorem in formal language learning.
• Uses distributional similarity and clustering to group words into syntactic categories without prior annotation.

Experimental results

Research questions

• RQ1 Can syntactic categories be reliably induced from unlabelled text using distributional patterns alone?
• RQ2 Can morphological processes, including non-concatenative ones, be learned unsupervised from raw word forms?
• RQ3 Is it possible to induce a context-free grammar from tagged text without explicit syntactic supervision?
• RQ4 Does the success of these algorithms undermine the Argument from the Poverty of the Stimulus?
• RQ5 Can a fully unsupervised, empiricist model of language acquisition be constructed using statistical learning methods?

Key findings

• The proposed algorithm successfully induces syntactic categories from unlabelled text, effectively handling ambiguous and rare words through distributional clustering.
• Statistical models can learn complex morphological processes, including non-concatenative morphology in Arabic, from raw word forms without prior linguistic annotation.
• An unsupervised context-free grammar induction algorithm is demonstrated on tagged text, showing that structural patterns can be recovered without explicit syntactic supervision.
• The integration of syntactic, morphological, and grammatical learning components supports a coherent, empiricist model of language acquisition.
• The results challenge the necessity of innate linguistic knowledge, suggesting that the Argument from the Poverty of the Stimulus lacks empirical and theoretical support.
• Gold's theorem does not preclude learning natural languages when statistical grammars are used instead of symbolic ones.