The Trans-Saharan Book Trade: Manuscript Culture, Arabic Literacy and Intellectual History in Muslim Africa, edited by Graziano Krätli and Ghislaine Lydon

First author draf

Towards a user-friendly webservice architecture for statistical machine translation in the PANACEA project

This paper presents a webservice architecture for Statistical Machine Translation aimed at non-technical users. A workflow editor allows a user to combine different webservices using a graphical user interface. In the current state of this project, the webservices have been implemented for a range of sentential and sub-sentential aligners. The advantage of a common interface and a common data format allows the user to build workflows exchanging different aligners

What's in a compound? Review article on Lieber and Štekauer (eds) 2009. 'The Oxford Handbook of Compounding'

The Oxford Handbook of Compounding surveys a variety of theoretical and descriptive issues, presenting overviews of compounding in a number of frameworks and sketches of compounding in a number of languages. Much of the book deals with Germanic noun–noun compounding. I take up some of the theoretical questions raised surrounding such constructions, in particular, the notion of attributive modification in noun-headed compounds. I focus on two issues. The first is the semantic relation between the head noun and its nominal modifier. Several authors repeat the argument that there is a small(-ish) fixed number of general semantic relations in noun–noun compounds (‘Lees's solution’), but I argue that the correct way to look at such compounds is what I call ‘Downing's solution’, in which we assume that the relation is specified pragmatically, and hence could be any relation at all. The second issue is the way that adjectives modify nouns inside compounds. Although there are languages in which compounded adjectives modify just as they do in phrases (Chukchee, Arleplog Swedish), in general the adjective has a classifier role and not that of a compositional attributive modifier. Thus, even if an English (or German) adjective–noun compound looks compositional, it isn't

Evaluating Unsupervised Dutch Word Embeddings as a Linguistic Resource

Word embeddings have recently seen a strong increase in interest as a result of strong performance gains on a variety of tasks. However, most of this research also underlined the importance of benchmark datasets, and the difficulty of constructing these for a variety of language-specific tasks. Still, many of the datasets used in these tasks could prove to be fruitful linguistic resources, allowing for unique observations into language use and variability. In this paper we demonstrate the performance of multiple types of embeddings, created with both count and prediction-based architectures on a variety of corpora, in two language-specific tasks: relation evaluation, and dialect identification. For the latter, we compare unsupervised methods with a traditional, hand-crafted dictionary. With this research, we provide the embeddings themselves, the relation evaluation task benchmark for use in further research, and demonstrate how the benchmarked embeddings prove a useful unsupervised linguistic resource, effectively used in a downstream task.Comment: in LREC 201

Skip-Thought Vectors

We describe an approach for unsupervised learning of a generic, distributed sentence encoder. Using the continuity of text from books, we train an encoder-decoder model that tries to reconstruct the surrounding sentences of an encoded passage. Sentences that share semantic and syntactic properties are thus mapped to similar vector representations. We next introduce a simple vocabulary expansion method to encode words that were not seen as part of training, allowing us to expand our vocabulary to a million words. After training our model, we extract and evaluate our vectors with linear models on 8 tasks: semantic relatedness, paraphrase detection, image-sentence ranking, question-type classification and 4 benchmark sentiment and subjectivity datasets. The end result is an off-the-shelf encoder that can produce highly generic sentence representations that are robust and perform well in practice. We will make our encoder publicly available.Comment: 11 page