We present two methods for unsupervised segmentation of words into morphemelike units. The model utilized is especially suited for languages with a rich morphology, such as Finnish. The first method is based on the Minimum Description Length (MDL) principle and works online. In the second method, Maximum Likelihood (ML) optimization is used. The quality of the segmentations is measured using an evaluation method that compares the segmentations produced to an existing morphological analysis. Experiments on both Finnish and English corpora show that the presented methods perform well compared to a current state-of-the-art system.

Morphology induction is a subproblem of important tasks like automatic learning of machine-readable dictionaries and grammar induction. Previous morphology induction approaches have relied solely on statistics of hypothesized stems and affixes to choose which affixes to consider legitimate. Relying on stem-and-affix statistics rather than semantic knowledge leads to a number of problems, such as the inappropriate use of valid affixes ("ally" stemming to "all"). We introduce a semantic-based algorithm for learning morphology which only proposes affixes when the stem and stem-plus-affix are sufficiently similar semantically. We implement our approach using Latent Semantic Analysis and show that our semantics-only approach provides morphology induction results that rival a current state-of-the-art system.

Arabic, a highly inflected language, requires good stemming for effective information retrieval, yet no standard approach to stemming has emerged. We developed several light stemmers based on heuristics and a statistical stemmer based on co-occurrence for Arabic retrieval. We compared the retrieval effectiveness of our stemmers and of a morphological analyzer on the TREC-2001 data. The best light stemmer was more effective for cross-language retrieval than a morphological stemmer which tried to find the root for each word. A repartitioning process consisting of vowel removal followed by clustering using co-occurrence analysis produced stem classes which were better than no stemming or very light stemming, but still inferior to good light stemming or morphological analysis.

We propose an algorithm to automatically induce the morphology of inflectional languages using only text corpora and no human input. Our algorithm combines cues from orthography, semantics, and syntactic distributions to induce morphological relationships in German, Dutch, and English. Using CELEX as a gold standard for evaluation, we show our algorithm to be an improvement over any knowledge-free algorithm yet proposed.

This paper presents a corpus-based algorithm capable of inducing inflectional morphological analyses of both regular and highly irregular forms (such as brought→bring) from distributional patterns in large monolingual text with no direct supervision. The algorithm combines four original alignment models based on relative corpus frequency, contextual similarity, weighted string similarity and incrementally retrained inflectional transduction probabilities. Starting with no paired <inflection,root> examples for training and no prior seeding

In this work, we describe the first public version of the Morfessor software, which is a program that takes as input a corpus of unannotated text and produces a segmentation of the word forms observed in the text. The segmentation obtained often resembles a linguistic morpheme segmentation. Morfessor is not language-dependent. The number of segments per word is not restricted to two or three as in some other existing morphology learning models. The current version of the software essentially implements two morpheme segmentation models presented earlier by us (Creutz and Lagus, 2002; Creutz, 2003). The document contains user’s instructions, as well as the mathematical formulation of the model and a description of the search algorithm used. Additionally, a few experiments on Finnish and English text corpora are reported in order to give the user some ideas of how to apply the program to his own data sets and how to evaluate the results. 1

Given the lack of word delimiters in written Japanese, word segmentation is generally considered a crucial first step in processing Japanese texts. Typical Japanese segmentation algorithms rely either on a lexicon and grammar or on pre-segmented data. In contrast, we introduce a novel statistical method utilizing unsegmented training data, with performance on kanji sequences comparable to and sometimes surpassing that of morphological analyzers over a variety of error metrics.

We present a model family called Morfessor for the unsupervised induction of a simple morphology from raw text data. The model is formulated in a probabilistic maximum a posteriori framework. Morfessor can handle highly inflecting and compounding languages where words can consist of lengthy sequences of morphemes. A lexicon of word segments, called morphs, is induced from the data. The lexicon stores information about both the usage and form of the morphs. Several instances of the model are evaluated quantitatively in a morpheme segmentation task on different sized sets of Finnish as well as English data. Morfessor is shown to perform very well compared to a widely known benchmark algorithm, in particular on Finnish data.

This article reviews research exploring the idea that simplicity does, indeed, drive a wide range of cognitive processes. We outline mathematical theory, computational results, and empirical data underpinning this viewpoint. Key words: simplicity, Kolmogorov complexity, codes, learning, induction, Bayesian inference 30-word summary:This article outlines the proposal that many aspects of cognition, from perception, to language acquisition, to high-level cognition involve finding patterns that provide the simplest explanation of available data. 3 The cognitive system finds patterns in the data that it receives. Perception involves finding patterns in the external world, from sensory input. Language acquisition involves finding patterns in linguistic input, to determine the structure of the language. High-level cognition involves finding patterns in information, to form categories, and to infer causal relations. Simplicity and the problem of induction A fundamental puzzle is what we term the problem of induction: infinitely many patterns are compatible with any finite set of data (see Box 1). So, for example, an infinity of curves pass through any finite set of points (Box 1a); an infinity of symbol sequences are compatible with any subsequence of symbols (Box 1b); infinitely many grammars are compatible with any finite set of observed sentences (Box 1c); and infinitely many perceptual organizations can fit any specific visual input (Box 1d). What principle allows the cognitive system to solve the problem of induction, and choose appropriately from these infinite sets of possibilities? Any such principle must meet two criteria: (i) it must solve the problem of induction successfully; (ii) it must explain empirical data in cognition. We argue that the best approach to (i)...

Since the experiments of Saffran et al. (1996a), there has been a great deal of interest in the question of how statistical regularities in the speech stream might be used by infants to begin to identify individual words. In this work, we use computational modeling to explore the effects of different assumptions the learner might make regarding the nature of words – in particular, how these assumptions affect the kinds of words that are segmented from a corpus of transcribed child-directed speech. We develop several models within a Bayesian ideal observer framework, and use them to examine the consequences of assuming either that words are independent units, or units that help to predict other units. We show through empirical and theoretical results that the assumption of independence causes the learner to undersegment the corpus, with many two- and three-word sequences (e.g. what’s that, do you, in the house) misidentified as individual words. In contrast, when the learner assumes that words are predictive, the resulting segmentation is far more accurate. These results indicate that taking context into account is important for a statistical word segmentation strategy to be successful, and raise the possibility that even young infants may be able to exploit more subtle statistical patterns than have usually been considered. 1