Malin Ahlberg

Semi-supervised learning of morphological paradigms and lexicons

We present a semi-supervised approach to the problem of paradigm induction from inflection tables. Our system extracts generalizations from inflection tables, representing the resulting paradigms in an abstract form. The process is intended to be language-independent, and to provide human-readable generalizations of paradigms. The tools we provide can be used by linguists for the rapid creation of lexical resources. We evaluate the system through an inflection table reconstruction task using Wiktionary data for German, Spanish, and Finnish. With no additional corpus information available, the evaluation yields per word form accuracy scores on inflecting unseen base forms in different languages ranging from 87.81% (German nouns) to 99.52% (Spanish verbs); with additional unlabeled text corpora available for training the scores range from 91.81% (German nouns) to 99.58% (Spanish verbs). We separately evaluate the system in a simulated task of Swedish lexicon creation, and show that on the basis of a small number of inflection tables, the system can accurately collect from a list of noun forms a lexicon with inflection information ranging from 100.0% correct (collect 100 words), to 96.4% correct (collect 1000 words).

Paradigm classification in supervised learning of morphology

Supervised morphological paradigm learning by identifying and aligning the longest common subsequence found in inflection tables has recently been proposed as a simple yet competitive way to induce morphological patterns. We combine this non-probabilistic strategy of inflection table generalization with a discriminative classifier to permit the reconstruction of complete inflection tables of unseen words. Our system learns morphological paradigms from labeled examples of inflection patterns (inflection tables) and then produces inflection tables from unseen lemmas or base forms. We evaluate the approach on datasets covering 11 different languages and show that this approach results in consistently higher accuracies vis-` other methods on the same task, thus indicating that the general method is a viable approach to quickly creating highaccuracy morphological resources.

A Type-Theoretical Wide-Coverage Computational Grammar for Swedish

The work describes a wide-coverage computational grammar for Swedish. It is developed using GF (Grammatical Framework), a functional language specialized for grammar programming. We trained and evaluated the grammar by using Talbanken, one of the largest treebanks for Swedish. As a result 65% of the Talbanken trees were translated into the GF format in the training stage and 76% of the noun phrases were parsed during the evaluation. Moreover, we obtained a language model for Swedish which we use for disambiguation.