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Ungoliant: An optimized pipeline for the generation of a very large-scale multilingual web corpus
(2021)
Since the introduction of large language models in Natural Language Processing, large raw corpora have played a crucial role in Computational Linguistics. However, most of these large raw corpora are either available only for English or not available to the general public due to copyright issues. Nevertheless, there are some examples of freely available multilingual corpora for training Deep Learning NLP models, such as the OSCAR and Paracrawl corpora. However, they have quality issues, especially for low-resource languages. Moreover, recreating or updating these corpora is very complex. In this work, we try to reproduce and improve the goclassy pipeline used to create the OSCAR corpus. We propose a new pipeline that is faster, modular, parameterizable, and well documented. We use it to create a corpus similar to OSCAR but larger and based on recent data. Also, unlike OSCAR, the metadata information is at the document level. We release our pipeline under an open source license and publish the corpus under a research-only license.
We present WOMBAT, a Python tool which supports NLP practitioners in accessing word embeddings from code. WOMBAT addresses common research problems, including unified access, scaling, and robust and reproducible preprocessing. Code that uses WOMBAT for accessing word embeddings is not only cleaner, more readable, and easier to reuse, but also much more efficient than code using standard in-memory methods: a Python script using WOMBAT for evaluating seven large word embedding collections (8.7M embedding vectors in total) on a simple SemEval sentence similarity task involving 250 raw sentence pairs completes in under ten seconds end-to-end on a standard notebook computer.
This paper addresses the task of finding antecedents for locally uninstantiated arguments. To resolve such null instantiations, we develop a weakly supervised approach that investigates and combines a number of linguistically motivated strategies that are inspired by work on semantic role labeling and corefence resolution. The performance of the system is competitive with the current state-of-the-art supervised system.
The Stuttgart-Tübingen Tagset (STTS) is a widely used POS annotation scheme for German which provides 54 different tags for the analysis on the part of speech level. The tagset, however, does not distinguish between adverbs and different types of particles used for expressing modality, intensity, graduation, or to mark the focus of the sentence. In the paper, we present an extension to the STTS which provides tags for a more fine-grained analysis of modification, based on a syntactic perspective on parts of speech. We argue that the new classification not only enables us to do corpus-based linguistic studies on modification, but also improves statistical parsing. We give proof of concept by training a data-driven dependency parser on data from the TiGer treebank, providing the parser a) with the original STTS tags and b) with the new tags. Results show an improved labelled accuracy for the new, syntactically motivated classification.
When comparing different tools in the field of natural language processing (NLP), the quality of their results usually has first priority. This is also true for tokenization. In the context of large and diverse corpora for linguistic research purposes, however, other criteria also play a role – not least sufficient speed to process the data in an acceptable amount of time. In this paper we evaluate several state-ofthe-art tokenization tools for German – including our own – with regard to theses criteria. We conclude that while not all tools are applicable in this setting, no compromises regarding quality need to be made.
In the paper we investigate the impact of data size on a Word Sense Disambiguation task (WSD). We question the assumption that the knowledge acquisition bottleneck, which is known as one of the major challenges for WSD, can be solved by simply obtaining more and more training data. Our case study on 1,000 manually annotated instances of the German verb drohen (threaten) shows that the best performance is not obtained when training on the full data set, but by carefully selecting new training instances with regard to their informativeness for the learning process (Active Learning). We present a thorough evaluation of the impact of different sampling methods on the data sets and propose an improved method for uncertainty sampling which dynamically adapts the selection of new instances to the learning progress of the classifier, resulting in more robust results during the initial stages of learning. A qualitative error analysis identifies problems for automatic WSD and discusses the reasons for the great gap in performance between human annotators and our automatic WSD system.
Researchers in Natural Language Processing rely on availability of data and software, ideally under open licenses, but little is done to actively encourage it. In fact, the current Copyright framework grants exclusive rights to authors to copy their works, make them available to the public and make derivative works (such as annotated language corpora). Moreover, in the EU databases are protected against unauthorized extraction and re-utilization of their contents. Therefore, proper public licensing plays a crucial role in providing access to research data. A public license is a license that grants certain rights not to one particular user, but to the general public (everybody). Our article presents a tool that we developed and whose purpose is to assist the user in the licensing process. As software and data should be licensed under different licenses, the tool is composed of two separate parts: Data and Software. The underlying logic as well as elements of the graphic interface are presented below.
The Partitur Format at BAS
(1997)
Most spoken language resources are produced and disseminated together with symbolic information relating to the speech signal. These are for instance orthographic transcript labeling and segmentation on the phonologic phoneti prosodic phrasal level. Most of the known formats for these symbolic data are defined in a ‘closed form’ that is not fexible enough to allow simple and platform independent processing and easy extensions.
At the Bavarian Archive for Speech Signals (BAS) a new format has been developed and used over the last few years that shows some significant advantages over other existing formats. This paper describes the basic principles behind this format discusses briefly the advantages and gives detailed definitions of the description levels used so far.
The goal of the MULI (MUltiLingual Information structure) project is to empirically analyse information structure in German and English newspaper texts. In contrast to other projects in which information structure is annotated and investigated (e.g. in the Prague Dependency Treebank, which mirrors the basic information about the topic-focus articulation of the sentence), we do not annotate theory-biased categories like topic-focus or theme-rheme. Trying to be as theory-independent as possible, we annotate those features which are relevant to information structure and on the basis of which typical patterns, co-occurrences or correlations can be determined. We distinguish between three annotation levels: syntax, discourse and prosody. The data is based on the TIGER Corpus for German and the Penn Treebank for English, since the existing information on part-of-speech and syntactic structure can be re-used for our purposes. The actual annotation of an English example sequence illustrates our choice of categories on each level. Their combination offers the possibility to investigate how information structure is realised and can be interpreted.
This dissertation investigates discourse-pragmatic differences between variably linked arguments appearing in alternating argument structure constructions in the sense of Goldberg (1995) and Kay (manuscript). The properties that are studied include givenness, pragmatic relation (topic/focus), salience of referents, animacy, and others. They derive from the literature on sentence-type constructions such as topicalization and from research on the referential properties of NP form types.
The research carried out here has multiple uses. At the most basic level, it serves as an empirical check on existing characterizations of the pragmatic properties of the relevant arguments that are the result of syntactic and semantic analysis based on introspection alone. For instance, for the epistemic raising alternation involving verbs like seem, the predicted topicality difference between the subjects of the raised and unraised constructions (Langacker 1995) could not be confirmed.
This dissertation also addresses the question what kinds of pragmatic factors, if any, are relevant to argument structure constructions. Based on the evidence of the dative alternation, it does not seem to be the case that the kind of pragmatic influences on argument structure constructions are different or limited compared to the ones found to be relevant to sentence-type constructions.
The kind of research undertaken here can also inform the syntactic and semantic analysis of constructions. In the case of the dative alternation, the discourse-pragmatic characteristics of the variably linked arguments provide evidence that Basilico’s (1998) analysis of the difference between the alternates in terms of VP-shells and a difference between thetic and categorical ‘inner’ predication, on the one hand does not account for all the data and on the other can be re-stated in pragmatic terms other than the thetic-categorical distinction.
In addition to studies of valence alternations, this dissertation also discusses various null instantiation phenomena, which provide further evidence for the need to specify discourse-pragmatic properties as part of argument structure constructions and lexical entries.
Finally, it is suggested that the use of randomly sampled corpus data and statistical modelling throughout this dissertation improves both empirical and analytical coverage.