Shape Analysis as an Aid for Grammar Induction

Proceedings of Formal Grammar, 2004

Different Alignment Based Learning (ABL) algorithms have been proposed for unsupervised grammar induction, eg Zaanen (2001) and Déjean (1998), in particular for the induction of syntactic rules. However, ABL seems to be better suited for the induction of morphological rules. In this paper we show how unsupervised hypothesis generation with ABL algorithms can be used to induce a lexicon and morphological rules for various types of languages, eg agglutinative or polysynthetic languages. The resulting morphological rules and structures are optimized with the use of conflicting constraints on the size and statistical properties of the grammars, ie Minimium Description Length and Minimum Relative Entropy together with Maximum Average Mutual Information. Further, we discuss how the resulting (optimal and regular) grammar can be used for lexical clustering/classification for the induction of syntactic (context free) rules.

Natural Language Engineering, 1998

The statistical induction of stochastic context free grammars from bracketed corpora with the Inside Outside Algorithm is an appealing method for grammar learning, but the computational complexity of this algorithm has made it impossible to generate a large scale grammar. Researchers from natural language processing and speech recognition have suggested various methods to reduce the computational complexity and, at the same time, guide the learning algorithm towards a solution by, for example, placing constraints on the grammar. We suggest a method that strongly reduces that computational cost of the algorithm without placing constraints on the grammar. This method can in principle be combined with any of the constraints on grammars that have been suggested in earlier studies. We show that it is feasible to achieve results equivalent to earlier research, but with much lower computational effort. After creating a small grammar, the grammar is incrementally increased while rules that ...

2004

2000

This paper presents a framework for unsuper- vised natural language morphology induction wherein candidate suffixes are grouped into candidate inflection classes, which are then ar- ranged in a lattice structure. With similar can- didate inflection classes placed near one an- other in the lattice, I propose this structure is an ideal search space in which to isolate the true inflection

2000

Most of the world's natural languages have complex morphology. But the expense of building morphological analyzers by hand has prevented the development of morphological analysis systems for the large majority of languages. Unsupervised induction techniques, that learn from unannotated text data, can facilitate the development of computational morphology systems for new languages. Such unsupervised morphological analysis systems have been shown to help natural language processing tasks including speech recognition and information retrieval . This thesis describes ParaMor, an unsupervised induction algorithm for learning morphological paradigms from large collections of words in any natural language. Paradigms are sets of mutually substitutable morphological operations that organize the inflectional morphology of natural languages. ParaMor focuses on the most common morphological process, suffixation.

This paper introduces the special issue " Advances in Implemented Shape Grammars: Solutions and Applications " and frames the topic of computer implementations of shape grammars, both with a theoretical and an applied focus. This special issue focuses on the current state of the art regarding computer implementations of shape grammars and brings a discussion about how those systems can evolve in the coming years so that they can be used in real life design scenarios. This paper presents a brief state of the art of shape grammars implementation and an overview of the papers included in the current special issue categorized under technical design, interpreters and interface design, and uses cases. The paper ends with a comprehensive outlook into the future of shape grammars implementations.

Rule-based approaches to grammar (Albright & Hayes, 2003; Reiss, 2004, inter alia) suggest that knowledge of grammar consists largely of knowledge of rules. A rule describes a change and the context in which that change has been observed to occur and is to be carried out (e.g., Reiss, 2004). In its strongest version (Reiss, 2004), Rule-based Phonology proposes that phonology is based only on rules. Constructionist and Cognitive theories of grammar instead suggest that grammar largely consists of first-order schemas or constructions, which are generalizations about form-meaning pairings.

Proceedings of the 29th Penn …, 2005

Environment and Planning B: Planning and Design, 2013

This paper explores the theoretical basis for a concept of "computation-friendly" shape grammars, through a formal examination of tractability of the grammar formalism.

Language & Cognition, 2017

This paper presents an algorithm for learning the construction grammar of a language from a large corpus. This grammar induction algorithm has two goals: First, to show that construction grammars are learnable without highly specified innate structure; Second, to develop a model of which units do or do not constitute constructions in a given dataset. The basic task of construction grammar induction is to identify the minimum set of constructions that represents the language in question with maximum descriptive adequacy. These constructions must (1) generalize across an unspecified number of units while (2) containing mixed levels of representation internally (e.g., both item-specific and schematized representations) and (3) allowing for unfilled and partially filled slots. Additionally, these constructions may (4) contain recursive structure within a given slot that needs to be reduced in order to produce a sufficiently schematic representation. In other words, these constructions are multi-length, multi-level, possibly discontinuous co-occurrences which generalize across internal recursive structures. These co-occurrences are modeled using frequency and the ΔP measure of association, expanded in novel ways to cover multi-unit sequences. This work provides important new evidence for the learnability of construction grammars as well as a tool for the automated corpus analysis of constructions.