Marrying Universal Dependencies and Universal Morphology

The Universal Dependencies (UD) and Universal Morphology (UniMorph) projects each present schemata for annotating the morphosyntactic details of language. Each project also provides corpora of annotated text in many languages - UD at the token level and UniMorph at the type level. As each corpus is built by different annotators, language-specific decisions hinder the goal of universal schemata. With compatibility of tags, each project's annotations could be used to validate the other's. Additionally, the availability of both type- and token-level resources would be a boon to tasks such as parsing and homograph disambiguation. To ease this interoperability, we present a deterministic mapping from Universal Dependencies v2 features into the UniMorph schema. We validate our approach by lookup in the UniMorph corpora and find a macro-average of 64.13% recall. We also note incompatibilities due to paucity of data on either side. Finally, we present a critical evaluation of the foundations, strengths, and weaknesses of the two annotation projects.Comment: UDW1

Knowledge- and Labor-Light Morphological Analysis

We describe a knowledge and labor-light system for morphological analysis of fusional languages, exemplified by analysis of Czech. Our approach takes the middle road between completely unsupervised systems on the one hand and systems with extensive manually-created resources on the other. For the majority of languages and applications neither of these extreme approaches seems warranted. The knowledge-free approach lacks precision and the knowledge- intensive approach is usually too costly. We show that a system using a little knowledge can be effective. This is done by creating an open, flexible, fast, portable system for morphological analysis. Time needed for adjusting the system to a new language constitutes a fraction of the time needed for systems with extensive manually created resources: days instead of years. We tested this for Russian, Portuguese and Catalan.The work described in this paper was partially supported by NSF CAREER Award 0347799

Irish treebanking and parsing: a preliminary evaluation

Language resources are essential for linguistic research and the development of NLP applications. Low- density languages, such as Irish, therefore lack significant research in this area. This paper describes the early stages in the development of new language resources for Irish – namely the first Irish dependency treebank and the first Irish statistical dependency parser. We present the methodology behind building our new treebank and the steps we take to leverage upon the few existing resources. We discuss language specific choices made when defining our dependency labelling scheme, and describe interesting Irish language characteristics such as prepositional attachment, copula and clefting. We manually develop a small treebank of 300 sentences based on an existing POS-tagged corpus and report an inter-annotator agreement of 0.7902. We train MaltParser to achieve preliminary parsing results for Irish and describe a bootstrapping approach for further stages of development

Further Results and Analysis of Icelandic Part of Speech Tagging

Data driven POS tagging has achieved good performance for English, but can still lag behind linguistic rule based taggers for morphologically complex languages, such as Icelandic. We extend a statistical tagger to handle fine grained tagsets and improve over the best Icelandic POS tagger. Additionally, we develop a case tagger for non-local case and gender decisions. An error analysis of our system suggests future directions. This paper presents further results and analysis to the original work (Dredze and Wallenberg, 2008)

The Parallel Meaning Bank: Towards a Multilingual Corpus of Translations Annotated with Compositional Meaning Representations

The Parallel Meaning Bank is a corpus of translations annotated with shared, formal meaning representations comprising over 11 million words divided over four languages (English, German, Italian, and Dutch). Our approach is based on cross-lingual projection: automatically produced (and manually corrected) semantic annotations for English sentences are mapped onto their word-aligned translations, assuming that the translations are meaning-preserving. The semantic annotation consists of five main steps: (i) segmentation of the text in sentences and lexical items; (ii) syntactic parsing with Combinatory Categorial Grammar; (iii) universal semantic tagging; (iv) symbolization; and (v) compositional semantic analysis based on Discourse Representation Theory. These steps are performed using statistical models trained in a semi-supervised manner. The employed annotation models are all language-neutral. Our first results are promising.Comment: To appear at EACL 201

Compiling and annotating a learner corpus for a morphologically rich language: CzeSL, a corpus of non-native Czech

Learner corpora, linguistic collections documenting a language as used by learners, provide an important empirical foundation for language acquisition research and teaching practice. This book presents CzeSL, a corpus of non-native Czech, against the background of theoretical and practical issues in the current learner corpus research. Languages with rich morphology and relatively free word order, including Czech, are particularly challenging for the analysis of learner language. The authors address both the complexity of learner error annotation, describing three complementary annotation schemes, and the complexity of description of non-native Czech in terms of standard linguistic categories. The book discusses in detail practical aspects of the corpus creation: the process of collection and annotation itself, the supporting tools, the resulting data, their formats and search platforms. The chapter on use cases exemplifies the usefulness of learner corpora for teaching, language acquisition research, and computational linguistics. Any researcher developing learner corpora will surely appreciate the concluding chapter listing lessons learned and pitfalls to avoid