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Since 2013 representatives of several French and German CMC corpus projects have developed three customizations of the TEI-P5 standard for text encoding in order to adapt the encoding schema and models provided by the TEI to the structural peculiarities of CMC discourse. Based on the three schema versions, a 4th version has been created which takes into account the experiences from encoding our corpora and which is specifically designed for the submission of a feature request to the TEI council. On our poster we would present the structure of this schema and its relations (commonalities and differences) to the previous schemas.
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 paper is concerned with a novel methodology for generating phonetic questions used in tree-based state tying for speech recognition. In order to implement a speech recognition system, language-dependent knowledge which goes beyond annotated material is usually required. The approach presented here generates phonetic questions for decision trees are based on a feature table that summarizes the articulatory characteristics of each sound. On the one hand, this method allows better language-specific triphone models to be defined given only a feature-table as linguistic input. On the other hand, the feature-table approach facilitates efficient definition of triphone models for other languages since again only a feature table for this language is required. The approach is exemplified with speech recognition systems for English and Thai.
This paper describes the lexical database tool LOLA (Linguistic-Oriented Lexical database Approach) which has been developed for the construction and maintenance of lexicons for the machine translation system LMT. First, the requirements such a tool should meet are discussed, then LMT and the lexical information it requires, and some issues concerning vocabulary acquisition are presented. Afterwards the architecture and the components of the LOLA system are described and it is shown how we tried to meet the requirements worked out earlier. Although LOLA originally has been designed and implemented for the German-English LMT prototype, it aimed from the beginning at a representation of lexical data that can be reused for other LMT or MT prototypes or even other NLP applications. A special point of discussion will therefore be the adaptability of the tool and its components as well as the reusability of the lexical data stored in the database for the lexicon development for LMT or for other applications.
CoMParS is a resource under construction in the context of the long-term project German Grammar in European Comparison (GDE) at the IDS Mannheim. The principal goal of GDE is to create a novel contrastive grammar of German against the background of other European languages. Alongside German, which is the central focus, the core languages for comparison are English, French, Hungarian and Polish, representing different typological classes. Unlike traditional contrastive grammars available for German, which usually cover language pairs and are based on formal grammatical categories, the new GDE grammar is developed in the spirit of functionalist typology. This implies that, instead of formal criteria, cognitively motivated functional domains in terms of Givón (1984) are used as tertia comparationis. The purpose of CoMParS is to document the empirical basis of the theoretical assumptions of GDE-V and to illustrate the otherwise rather abstract content of grammar books by as many as possible naturally occurring and adequately presented multilingual examples, including information on their use in specific contexts and registers. These examples come from existing parallel corpora, and our presentation will focus on the legal aspects and consequences of this choice of language data.
This article describes a series of ongoing efforts at the Stanford Literary Lab to manage a large collection of literary corpora (~40 billion words). This work is marked by a tension between two competing requirements – the corpora need to be merged together into higher-order collections that can be analyzed as units; but, at the same time, it’s also necessary to preserve granular access to the original metadata and relational organization of each individual corpus. We describe a set of data management practices that try to accommodate both of these requirements – Apache Spark is used to index data as Parquet tables on an HPC cluster at Stanford. Crucially, the approach distinguishes between what we call “canonical” and “combined” corpora, a variation on the well-established notion of a “virtual corpus” (Kupietz et al., 2014; Jakubíek et al., 2014; van Uytvanck, 2010).
This paper outlines the broad research context and rationale for a new international comparable corpus (ICC). The ICC is to be largely modelled on the text categories and their quantities the International Corpus of English with only a few changes. The corpus will initially begin with nine European languages but others may join in due course. The paper reports on those and other agreements made at the inaugural planning meeting in Prague on 22-23 June 2017. It also sets out the project’s goals for its first two years.
We investigate whether non-configurational languages, which display more word order variation than configurational ones, require more training data for a phenomenon to be parsed successfully. We perform a tightly controlled study comparing the dative alternation for English (a configurational language), German, and Russian (both non-configurational). More specifically, we compare the performance of a dependency parser when only canonical word order is present with its performance on data sets when all word orders are present. Our results show that for all languages, canonical data not only is easier to parse, but there exists no direct correspondence between the size of training sets containing free(er) word order variation and performance.
We present two collections of lexical items with idiosyncratic distribution. The collections document the behavior of German and English bound words (BW, such as English “headway”), i.e., words which can only occur in one expression (“make headway”). BWs are a problem for both general and idiomatic dictionaries since it is unclear whether they have an independent lexical status and to what extent the expressions in which they occur are typical idiomatic expressions. We propose a system which allows us to document the information about BWs from dictionaries and linguistic literature, together with corpus data and example queries for major text corpora. We present our data structure and point to other phraseologically oriented collections. We will also show differences between the German and the English collection.
The classification of verbs in Levin's (1993) English Verb Classes and Alternations: A preliminary Investigation, on the basis of both intuitive semantic grouping and their participation in valence alternations, is often used by the NLP community as evidence of the semantic similarity of verbs (Jing & McKeown 1998; Lapata & Brew 1999; Kohl et al. 1998). In this paper, we compare the Levin classification with the work of the FrameNet project (Fillmore & Baker 2001), where words (not just verbs) are grouped according to the conceptual structures (frames) that underlie them and their combinatorial patterns are inductively derived from corpus evidence. This means that verbs grouped together in FrameNet (FN) might be semantically similar but have different (or no) alternations, and that verbs which share the same alternation might be represented in two different semantic frames.