MORPHE: A Practical Compiler for Reversible Morphology Rules
40882Authors
nan
Abstract
Morph~ is a Common Lisp compiler for reversible inflectional morphology rules developed at the Cen- ter for Machine Translation at Carnegie Mellon Uni- versity. This paper describes the Morph~ process- ing model, its implementation, and how it handles some common morphological processes. 1
Keywords
Full Content
MORPHIa: A Practical Compiler for Reversible Morphology Rules John R. R. Leavitt Center for Machine Translation Carnegie Mellon University Pittsburgh, PA 15213 jrrl@cs, cmu. edu Abstract Morph~ is a Common Lisp compiler for reversible inflectional morphology rules developed at the Cen- ter for Machine Translation at Carnegie Mellon Uni- versity. This paper describes the Morph~ process- ing model, its implementation, and how it handles some common morphological processes. 1 Introduction The input to the Morph~ rule compiler is a rule file containing inflection rules, the specification of a discrimination network of morphological forms, and definitions of certain classes of strings and string mappings. This rule file can be compiled into either a word generation program or a word parsing pro- gram. The word generation program produces an inflected surface form from a feature structure, 1 while the word pars- ing program takes an inflected form and produces a set of feature structures for valid parses. 2 2 The Processing Model In Morph~ the process of inflection is seen as consisting of two basic steps: 1. By making a series of feature- and orthographically- based decisions, choose an inflection procedure. 2. Apply that procedure to the uninflected root. To implement the first step, Morph~ uses a feature-based discrimination network with orthographically-based inflec- tion rules at the leaves. Each node in the discrimination network specifies a set of features common to all of its descen- dants. For example, at the top of a subtree for nouns, a node might contain the features { (cat: noun) } which wouldbe inherited by the nodes for single-noun and plural-noun, and SO on. That Morph~ explicitly divides feature-based decisions from orthographic decisions has two important consequences: 1These feature slructures are structurally the same as those used by the Generalized LR Parser/Compiler [Tomita et al., 1988] and the Generation Kit [Tomita and Nyberg, 1988], and can contain non-syntactic features. 2A detailed description of the Morph~ program and rule file for- malism and some example rule files axe given in [Leavitt, 1992]. • The type of feature that may be checked in the discrimi- nation network is not restricted. For instance, phonolog- ical and/or morphological features (e.g. paradigm) can be checked alongside syntactic features (e.g. category). • A single morpheme can be split across several leaf nodes if feature tests below the morpheme level are necessary. 2.1 The Rule Formalism As shown in Figure 1, a rule consists of a set of clauses, each of which contains orthographic pattern on the left-hand side and a set of inflection operations on the right-hand side. • Orthographic patterns. The orthographically-based de- cisions are made by matching against regular expression- based patterns. Standard regular expression operations (i.e. Kleene closure, wildcards, etc.) are included. In addition, non-standard operations for matching against a pre-defined class of strings 3, and binding and retrieval of portions of the word 4 are included. • Inflection Operations. The application of the inflection procedure is implemented as the sequential execution of the inflection operations in the right-hand side. …
Content truncated to 500 words
Labeled Text
Morph~ is a Common Lisp compiler for reversible inflectional morphology rules developed at the Cen- ter for Machine Translation at Carnegie Mellon Uni- versity. This paper describes the Morph~ process- ing model, its implementation, and how it handles some common morphological processes. 1
Publication Details
- Paper ID:
- 40882
- Year:
- 1992
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan