Introduction

This monograph is aimed at the examination of derivational networks across European languages. The concept of a derivational network is not new. The first ideas of network regularities and the network organization of derivational morphology can be traced back to the 1960s in relation to the Dokulilean tradition in word-formation. Unfortunately, apart from an outline of general principles, very little has been done in the field since. In recent years, however, we have been witnessing a growing interest in derivational paradigms and larger derivational systems based on them.This article has been supported by the Spanish State Research Agency (SRA, Ministry of Economy and Enterprise) and European Regional Development Fund (ERDF) (Ref. FFI2017-89665-P)

Assessing the Featural Organisation of Paradigms with Distributional Methods

In this paper, we apply distributional methods to Czech data to compare the predictions of two views of inflectional paradigms, as systems of orthogonal morphosyntactic feature oppositions, or as systems of multilateral contrasts between pairs of morphologically related words, not necessarily reducible to orthogonal features. We define two predictive tasks that probe what it means for two pairs of paradigm cells to contrast in the same features: in the first, we train a~classifier to discriminate between two paradigm cells; in the second, we train a family of models to predict the vector of the word in one cell from that of the word in another cell. By varying the choice of training and test data, we show that (i)~a~model trained on data that contrast in a manner orthogonal to its test data performs on average at chance level, while (ii)~a~model trained on data that contrast in a~manner parallel to its test data performs on average better than chance but still worse than a model trained on the same pair of cell used for testing. This is incompatible with the predictions of a reductive view of paradigms as systems of feature contrasts

Statistical dependency parsing of Turkish

This paper presents results from the first statistical dependency parser for Turkish. Turkish is a free-constituent order language with complex agglutinative inflectional and derivational morphology and presents interesting challenges for statistical parsing, as in general, dependency relations are between “portions” of words called inflectional groups. We have explored statistical models that use different representational units for parsing. We have used the Turkish Dependency Treebank to train and test our parser but have limited this initial exploration to that subset of the treebank sentences with only left-to-right non-crossing dependency links. Our results indicate that the best accuracy in terms of the dependency relations between inflectional groups is obtained when we use inflectional groups as units in parsing, and when contexts around the dependent are employed

UniMorph 4.0:Universal Morphology

The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements made on several fronts over the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 67 new languages, including 30 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g. missing gender and macron information. We have also amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet

The Morphology/Syntax Interface: Evidence from Possessive Adjectives in Slavonic

P[ossessive] A[djective]s in Slavonic, formed from nouns via suffixation, show unusual syntactic behavior. In Upper Sorbian, the form of attributive modifiers, relative pronouns, and personal pronouns can be controlled by the syntactic features of the noun underlying the PA. Control of attributive modifiers gives rise to phrases in which word structure and phrase structure do not match. The fact that the underlying noun is available for syntactic purposes suggests that PA formation is an inflectional process, while other factors (such as change of word-class membership) point just as clearly to a derivational process. It thus appears that any sharp differentiation between inflectional and derivational morphology must be abandoned. Data presented from all thirteen Slavonic languages, based on extensive work with native speakers, show that the control possibilities of the PA vary considerably. However, control of the attributive modifier is possible only if control of the relative pronoun is also possible, and that in turn only if control of the personal pronoun is possible. This result is subsumed under the constraints of the Agreement Hierarchy.</p

Character-Level Models versus Morphology in Semantic Role Labeling

Character-level models have become a popular approach specially for their accessibility and ability to handle unseen data. However, little is known on their ability to reveal the underlying morphological structure of a word, which is a crucial skill for high-level semantic analysis tasks, such as semantic role labeling (SRL). In this work, we train various types of SRL models that use word, character and morphology level information and analyze how performance of characters compare to words and morphology for several languages. We conduct an in-depth error analysis for each morphological typology and analyze the strengths and limitations of character-level models that relate to out-of-domain data, training data size, long range dependencies and model complexity. Our exhaustive analyses shed light on important characteristics of character-level models and their semantic capability.Comment: Accepted for publication at the 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018