Compositional Morphology for Word Representations and Language Modelling

This paper presents a scalable method for integrating compositional morphological representations into a vector-based probabilistic language model. Our approach is evaluated in the context of log-bilinear language models, rendered suitably efficient for implementation inside a machine translation decoder by factoring the vocabulary. We perform both intrinsic and extrinsic evaluations, presenting results on a range of languages which demonstrate that our model learns morphological representations that both perform well on word similarity tasks and lead to substantial reductions in perplexity. When used for translation into morphologically rich languages with large vocabularies, our models obtain improvements of up to 1.2 BLEU points relative to a baseline system using back-off n-gram models.Comment: Proceedings of the 31st International Conference on Machine Learning (ICML

Zwischen Empirie und Ästhetik – Ansätze zur korpuslinguistischen Untersuchung und Bewertung von Sprachwandel

Der Beitrag beschäftigt sich mit der Frage, wie und inwieweit korpusbasierte Ansätze zur Untersuchung und Bewertung von Sprachwandel beitragen können. Die Bewertung von Sprachwandel erscheint in dieser Hinsicht interessant, da sie erstens von größerem öffentlichen Interesse ist, zweitens nicht zu den Kernthemen der Sprachwissenschaft zählt und drittens sowohl die geisteswissenschaftlichen Aspekte der Sprachwissenschaft berührt als auch die empirischen, die eher für die so genannten harten Wissenschaften typisch sind. Letzteres trifft bei der Frage nach Sprachverfall (gutem vs. schlechtem Deutsch diachron) vermutlich unbestrittener zu als bei der Frage nach richtigem vs. falschem Deutsch, da zu ihrer Beantwortung offensichtlich einerseits empirische, messbare Kriterien herangezogen werden müssen, andererseits aber auch weitere Kriterien notwendig sind und es außerdem einer Entscheidung zur Einordnung und Gewichtung der verschiedenartigen Kriterien sowie einer Begründung dieser Entscheidung bedarf. Zur Annäherung an die Fragestellung werden zunächst gängige, leicht operationalisierbare Hypothesen zu Symptomen eines potenziellen Verfalls des Deutschen auf verschiedenen DeReKo-basierten Korpora überprüft und im Hinblick auf ihre Verallgemeinerbarkeit und Tragweite diskutiert. Im zweiten Teil werden weitere empirische Ansätze zur Untersuchung von Wandel, Variation und Dynamik skizziert, die zur Diskussion spezieller Aspekte von Sprachverfall beitragen könnten. Im Schlussteil werden die vorgestellten Ansätze in den Gesamtkontext einer sprachwissenschaftlichen Untersuchung von Sprachverfall gestellt und vor dem Hintergrund seines gesellschaftlichen Diskurses reflektiert

The HeliPaD:A parsed corpus of Old Saxon

This short paper introduces the HeliPaD, a new parsed corpus of Old Saxon (Old Low German). It is annotated according to the standards of the Penn Corpora of Historical English, enriched with lemmatization and additional morphological attributes as well as textual and metrical annotation. This paper provides an overview of its main features and compares it to existing resources such as the Deutsch Diachron Digital version of the Old Saxon Heliand as part of the Referenzkorpus Altdeutsch. It closes with a roadmap for planned future expansions.publishe

Automatic recognition of speech, thought, and writing representation in German narrative texts

This article presents the main results of a project, which explored ways to recognize and classify a narrative feature—speech, thought, and writing representation (ST&WR)—automatically, using surface information and methods of computational linguistics. The task was to detect and distinguish four types—direct, free indirect, indirect, and reported ST&WR—in a corpus of manually annotated German narrative texts. Rule-based as well as machine-learning methods were tested and compared. The results were best for recognizing direct ST&WR (best F1 score: 0.87), followed by indirect (0.71), reported (0.58), and finally free indirect ST&WR (0.40). The rule-based approach worked best for ST&WR types with clear patterns, like indirect and marked direct ST&WR, and often gave the most accurate results. Machine learning was most successful for types without clear indicators, like free indirect ST&WR, and proved more stable. When looking at the percentage of ST&WR in a text, the results of machine-learning methods always correlated best with the results of manual annotation. Creating a union or intersection of the results of the two approaches did not lead to striking improvements. A stricter definition of ST&WR, which excluded borderline cases, made the task harder and led to worse results for both approaches

Fine-grain morphological analyzer and part-of-speech tagger for Arabic text

Morphological analyzers and part-of-speech taggers are key technologies for most text analysis applications. Our aim is to develop a part-of-speech tagger for annotating a wide range of Arabic text formats, domains and genres including both vowelized and non-vowelized text. Enriching the text with linguistic analysis will maximize the potential for corpus re-use in a wide range of applications. We foresee the advantage of enriching the text with part-of-speech tags of very fine-grained grammatical distinctions, which reflect expert interest in syntax and morphology, but not specific needs of end-users, because end-user applications are not known in advance. In this paper we review existing Arabic Part-of-Speech Taggers and tag-sets, and illustrate four different Arabic PoS tag-sets for a sample of Arabic text from the Quran. We describe the detailed fine-grained morphological feature tag set of Arabic, and the fine-grained Arabic morphological analyzer algorithm. We faced practical challenges in applying the morphological analyzer to the 100-million-word Web Arabic Corpus: we had to port the software to the National Grid Service, adapt the analyser to cope with spelling variations and errors, and utilise a Broad-Coverage Lexical Resource combining 23 traditional Arabic lexicons. Finally we outline the construction of a Gold Standard for comparative evaluation

A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-of-Speech Tagging

In this paper, we propose a new approach to construct a system of transformation rules for the Part-of-Speech (POS) tagging task. Our approach is based on an incremental knowledge acquisition method where rules are stored in an exception structure and new rules are only added to correct the errors of existing rules; thus allowing systematic control of the interaction between the rules. Experimental results on 13 languages show that our approach is fast in terms of training time and tagging speed. Furthermore, our approach obtains very competitive accuracy in comparison to state-of-the-art POS and morphological taggers.Comment: Version 1: 13 pages. Version 2: Submitted to AI Communications - the European Journal on Artificial Intelligence. Version 3: Resubmitted after major revisions. Version 4: Resubmitted after minor revisions. Version 5: to appear in AI Communications (accepted for publication on 3/12/2015

An automatic part-of-speech tagger for Middle Low German

Syntactically annotated corpora are highly important for enabling large-scale diachronic and diatopic language research. Such corpora have recently been developed for a variety of historical languages, or are still under development. One of those under development is the fully tagged and parsed Corpus of Historical Low German (CHLG), which is aimed at facilitating research into the highly under-researched diachronic syntax of Low German. The present paper reports on a crucial step in creating the corpus, viz. the creation of a part-of-speech tagger for Middle Low German (MLG). Having been transmitted in several non-standardised written varieties, MLG poses a challenge to standard POS taggers, which usually rely on normalized spelling. We outline the major issues faced in the creation of the tagger and present our solutions to them

Skandalisierung korpuslinguistisch. Ein empirisch-linguistischer Blick auf die Berichterstattung zur "Wulff-Affäre"

Abstract What kind of language is typical of the mass media coverage of a scandal? Which linguistic means have the effect of scandalizing a public person like a politician? The study presented in this paper uses corpus linguistic methods to investigate a specific scandal in recent German politics: Federal President Christian Wulff was accused by the media of not having informed transparently about a private loan he had taken out. Further allegations of corruption presented by the media resulted in his resignation after only two months. The paper shows how automatic language pattern analyses of a corpus of articles about Wulff in two leading German newspapers reveal the typical patterns used in a discourse of scandalization. Additionally, also characteristic grammar features like tempus forms are incorporated in the analysis. The texts scandalizing a person use more patterned language than other news texts on politics. The patterns reflect the characteristic speech acts of scandalizing like accusing, informing, or speculating on the political future of the scandalized person, but also speech acts like defending and regretting. The use of tempus shows that scandalizing texts are more dynamic and vivid compared to other political coverage