Zusammenfassung. Mit der lexikalischen Datenbank dlexDB stellen wir der psychologischen und linguistischen Forschung im World Wide Web online statistische Kennwerte fur eine Vielzahl von verarbeitungsrelevanten Merkmalen von Wortern zur Verfugung. Diese Kennwerte umfassen die durch CELEX (Baayen, Piepenbrock und Gulikers, 1995) bekannten Variablen der Haufigkeiten von Wortformen und Lemmata in Texten geschriebener Sprache. Daruber hinaus berechnen wir eine Reihe neuer Kennwerte wie die Haufigkeiten von Silben, Morphemen, Zeichenfolgen und Mehrwortverbindungen sowie Wortahnlichkeitsmase. Die Datengrundlage bildet das Kernkorpus des Digitalen Worterbuchs der deutschen Sprache (DWDS) mit uber 100 Millionen laufenden Wortern. Wir illustrieren die Validitat dieser Kennwerte mit neuen Ergebnissen zu ihrem Einfluss auf Fixationsdauern beim Lesen von Satzen.

dlexDB : eine lexikalische Datenbank für die psychologische und linguistische Forschung

This article introduces childLex, an online database of German read by children. childLex is based on a corpus of children's books and comprises 10 million words that were syntactically annotated and lemmatized. childLex reports linguistic norms for lexical, superlexical, and sublexical variables in three different age groups: 6-8 (grades 1-2), 9-10 (grades 3-4), and 11-12 years (grades 5-6). Here, we describe how childLex was collected and analyzed. In addition, we provide information about the distributions of word frequency, word length, and orthographic neighborhood size, as well as their intercorrelations. Finally, we explain how childLex can be accessed using a Web interface.

childLex: A lexical database of German read by children

Eye fixation durations during normal reading correlate with processing difficulty, but the specific cognitive mechanisms reflected in these measures are not well understood. This study finds support in German readers' eye fixations for two distinct difficulty metrics: surprisal, which reflects the change in probabilities across syntactic analyses as new words are integrated; and retrieval, which quantifies comprehension difficulty in terms of working memory constraints. We examine the predictions of both metrics using a family of dependency parsers indexed by an upper limit on the number of candidate syntactic analyses they retain at successive words. Surprisal models all fixation measures and regression probability. By contrast, retrieval does not model any measure in serial processing. As more candidate analyses are considered in parallel at each word, retrieval can account for the same measures as surprisal. This pattern suggests an important role for ranked parallelism in theories of sentence comprehe...

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Parallel Processing and Sentence Comprehension Difficulty.

Linguistic similarity is multi-faceted. For instance, two words may be similar with respect to semantics, syntax, or morphology inter alia. Continuous word-embeddings have been shown to capture most of these shades of similarity to some degree. This work considers guiding word-embeddings with morphologically annotated data, a form of semisupervised learning, encouraging the vectors to encode a word’s morphology, i.e., words close in the embedded space share morphological features. We extend the log-bilinear model to this end and show that indeed our learned embeddings achieve this, using German as a case study.

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Morphological Word-Embeddings

This paper deals with the representation of multi-level linguistic annotations. It proposes an XML-based, generic stand-off architecture and presents an example instantiation. Application scenarios that profit from this architecture are sketched out. In recent years, corpus linguistics has become more and more important to a broad community, including people working in theoretical, applied and computational linguistics. To many of them, speech and text corpora represent a rich source of data and phenomena, forming the basis of their research. Benefit from such data is even more important if the data is annotated by suitable information, allowing for fast and effective retrieval of relevant data. Whereas corpora of the first generation featured part-of-speech and syntactic annotations (e.g. PennTreebank [MSM93], TIGER corpus [BDE04]), the focus has now switched to properties beyond the (morpho-)syntactic level. Recent corpora are annotated by semantic information (PropBank [KP02], FrameNet [JPB03], SALSA [EKPP03]), pragmatic information (Penn Discourse TreeBank [MPJW04], RST Discourse Treebank [CMO03], Potsdam Commentary Corpus [Ste04]), and dialogue structure (Switchboard SWBD-DAMSL [JSB97]). Annotations often have to be carried out manually — reliable (semi-)automatic tools exist only for the annotation of part of speech and syntax, and are restricted to well-researched languages like English or German. Moreover, hand-annotated training material is a prerequisite for the development of automatic tools. As a consequence, corpora and annotations ought to be reusable so that a large community can profit from the data. To this end, various standardization efforts have been launched. Standardization of linguistic data concerns (see, e.g., [Sch05]): (i) The physical data structure: here, XML has become the widely-recognized standard format. (ii) The logical data structure: i.e., the data models that are used to model the phenomena and their properties (e.g. hierarchical structures like trees or graphs for syntax annotations 1The research reported in this paper was jointly financed by the German Research Foundation (DFG, SFB632) and the Federal Ministry of Education and Research (BMBF grant no. 03WKH22). Many thanks go to my colleagues, especially Michael Götze, for helpful discussions of the topics addressed in this paper. vs. time-aligned tiers for speech and dialogue annotations). Examples of data models are annotation graphs [BL01] and the NITE Object Model [CKO03b]. (iii) Content: in several initiatives, XML applications for specific linguistic annotations have been developed. For instance, TEI2 (“Text Encoding Initiative”, [SB94]) defines highly-detailed DTDs for encoding all kinds of bibliographic and other information; XCES3 (“XML-based Corpus Encoding Standard”) provides DTDs for the annotation of chunks, alignment, etc. More recently, however, it has been recognized that these standardized DTDs often do not meet application-specific needs. Hence, abstract, generic XML formats have been proposed that allow for the formal integration of application-specific annotations [IR01]. For the conceptual integration of specific annotations, so-called data category repositories as well as linguistic ontologies have been developed. They define reference categories, with precise semantics and examples, that specific annotation tags ought to be mapped to (see, e.g., DOLCE4, “Descriptive Ontology for Linguistic and Cognitive Engineering”). This papers deals with the formal integration of specific annotations. It first addresses the subject of stand-off architecture (sec. 1). We then propose an XML-based representation of linguistic annotation and present an example application (instantiation) in some detail (sec. 2). We also sketch out some application scenarios that profit from such a flexible architecture (sec. 3) and address related approaches (sec. 4). 1 Stand-off Architecture As early as in the mid-nineties, the topic of “stand-off annotation” has been discussed (see, e.g., [TM97]). This term describes the situation where primary data (e.g., the source text) and annotations of this data are stored in separate files. Stand-off annotation might seem problematic, because there is no immediate connection between the text and its annotation; hence, whenever the source text is modified, extra care has to be taken to synchronize its annotation. Similarly, human inspection of the data becomes cumbersome. On the other hand, however, stand-off annotation has the great advantage of leaving the source text untouched. It thus allows for annotating text that cannot be modified for whatever reasons, e.g., because it is a text available on the Internet. Moreover, whereas XML as such does not easily account for overlapping segments and conflicting hierarchies,5 they can be marked in a natural way in stand-off annotation: by distributing annotations over different files. That is, not only is the source text separated from its annotations, but individual annotations are separated from each other as well. This way, annotations at different levels can be created and modified independently of each other. Finally, competing, alternative annotations can even be represented, e.g. variants of part-of-speech annotations that are output of different tools. 2http://www.tei-c.org/ 3http://www.cs.vassar.edu/XCES/ 4http://www.loa-cnr.it/DOLCE.html 5Different methods have been proposed to accommodate conflicting markup into XML. We will come back to them below. One of the first proposals for stand-off annotation of linguistic corpora is [DBD98]. An ISO working group is currently developing the stand-off based LAF6 (“Linguistic Annotation Framework” [IRdlC03]). Some recent corpora like the ANC (“American National Corpus” [RI04]) are encoded in stand-off architecture. In our approach presented in this paper, we also subscribe to the principles of stand-off annotation. 2 A Generic XML Format Our format defines generic XML elements like <mark> (markable), <feat> (feature), and <struct> (structure), which indicate which data type the annotation conforms to. We assume that primary data is stored in a file that optionally specifies a header, followed by a tag <body>, which contains the source text. Annotations are stored in separate files; they may refer to the source text or to other annotations. These relations are encoded by means of XLinks and XPointers. We distinguish three different types of annotations: markables, structures, and features. (i) Markables: <mark> tags specify text positions or spans of text (or spans of other markables) that can be annotated by linguistic information. For instance, <mark> tags might indicate tokens by specifying ranges of the source text, cf. fig. 1. (ii) Structures: <struct> tags are special types of markables. Similar to <mark> tags, they specify objects that then can serve as anchors for annotations. Whereas <mark> tags define simple types of anchors (flat spans of text or markables), a <struct> tag represents a complex anchor involving relations between arbitrarily many markables (including <struct> elements). Relations (<rel>) can be further specified by an attribute type, e.g. as undirected or directed (= pointers). Put differently, a <structList> specifies a complete tree or graph, which consists of single tree fragments specified by the <struct> tags, cf. fig. 1. (iii) Features: <feat> tags specify information annotated to markables or structures, which are referred to by xlink attributes. The type of information (e.g., “part of speech”) is encoded by an attribute type, cf. fig. 2. For instance, the information encoded by the first <feat> in fig. 2 can be paraphrased as follows: Take the token that is defined by the tag <mark> with the ID attribute id="tok 1" and assign the part of speech “ART” (article) to that token. We intend to adopt the idea of [CKO03a] by assuming that admissible feature values (such as “NN”, normal/common noun, or “NE”, named entity) may be complex types and are organized in a type hierarchy. For instance, “NN” and “NE” might be subtypes of the more general type “N”, noun. <feat> tags then point to some type in the hierarchy (which is stored separately), thus specifying the value of the annotated property, cf. fig. 3.7 6ISO Technical TC37/SC4, http://www.tc37sc4.org 7Type hierarchies have to be defined by the user or they may be derived from annotation schemes that incorporate hierarchies, cf. the schemes used by the annotation tool MMAX. In case no hierarchy is defined, the features will be organized in a flat list. The stand-off architecture allows the user to experiment with different hierarchies. Further examples of annotations are sketched out below. They illustrate that annotations may stem from different sources (see the attribute source) and encode various types of information. Categorial annotation (anchored to constituents) <header sfb id="rabin1.const cat" type="categories" source="TIGERcorpus"/> <featList xml:base="rabin1.const.xml"> <feat xlink:href="#syn 1" value="PN"/>  <feat xlink:href="#syn 2" value="PP"/>  ... Coreference annotation, marking coreferential expressions such as pronouns (referred to xlink:href attributes) and their antecedents (identified by target attributes) <header sfb id="rabin1.coref" type="coreference" source="MMAXcoref"/> <featList> <feat xlink:href="rabin1.tok.xml#tok 19" (sein) target="rabin1.const.xml#syn 9" (Der Rabin-Attentäter Jigal Amir) value="identity"/> ... Document structure: headers, paragraphs, lists, etc. (anchored to markables that refer to tokens) <header sfb id="rabin1.div" type="divisions"/> <markList xmlns:xlink="http://www.w3.org/1999/xlink" xml:base="rabin1.tok.xml"> <mark id="div 1" xlink:href="#xpointer(id(’tok 1’)/range-to(id(’tok 390’)"/> <mark id="div 2" xlink:href="#xpointer(id(’tok 1’)/range-to(id(’tok 89’)"/> ... <header sfb id="rabin1.div docstr" type="documentStructure"/> <featL

XML-based Stand-off Representation and Exploitation of Multi-Level Linguistic Annotation.

This paper reports on the TIGER Treebank, a corpus of currently 40,000 syntactically annotated German newspaper sentences. We describe what kind of information is encoded in the treebank and introduce the different representation formats that are used for the annotation and exploitation of the treebank. We explain the different methods used for the annotation: interactive annotation, using the tool ANNOTATE, and LFG parsing. Furthermore, we give an account of the annotation scheme used for the TIGER treebank. This scheme is an extended and improved version of the NEGRA annotation scheme and we illustrate in detail the linguistic extensions that were made concerning the annotation in the TIGER project. The main differences are concerned with coordination, verb-subcategorization, expletives as well as proper nouns. In addition, the paper also presents the query tool TIGERSearch that was developed in the project to exploit the treebank in an adequate way. We describe the query language which was designed to facilitate a simple formulation of complex queries; furthermore, we shortly introduce TIGER in, a graphical user interface for query input. The paper concludes with a summary and some directions for future work.

TIGER: Linguistic Interpretation of a German Corpus

One of the major purposes of annotated corpora is their potential for use as databases for linguistic research. An important design criterion for corpora specifically intended for this use is the need to encode a plurality of types of information, some of which are clearly interrelated. This need can conflict with the need to produce large corpora quickly, where the encoding of redundant information or the encoding of types of information which require difficult decisions on the part of human annotators can incur undesirable costs. An example of such a conflict would be the desirability of encoding syntactic categories, syntactic functions and thematic roles in the same corpus, so that individual researchers can formulate queries in an intuitive way. In an inflecting language such as German, there is considerable overlap if all three types of information are encoded. For example, the property of being an agent is often claimed to be typical of subjects, which are in the nominative case, whereas the property of being a patient is often claimed to be typical of direct objects, which are in the accusative case. On the other hand the relationship is not one-to-one. There are subjects which are clearly not agents. There are dative objects and even accusative objects which have properties typically associated with agents. The purpose of this paper is to investigate to which extent the thematic roles agent, recipient and patient can be indirectly encoded via syntactic categories which are independently needed. The syntactic categories used are derived from both case and the ability or inability of an argument to undergo object conversion in one of the two main German passive constructions, the werden-passive and the bekommen-passive.

George Smith

Papers

TIGER: Linguistic Interpretation of a German Corpus

Encoding Thematic Roles via Syntactic Functions in a German Treebank