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Recent approaches to Word Sense Disambiguation (WSD) generally fall into two classes: (1) information-intensive approaches and (2) information-poor approaches. Our hypothesis is that for memory-based learning (MBL), a reduced amount of data is more beneficial than the full range of features used in the past. Our experiments show that MBL combined with a restricted set of features and a feature selection method that minimizes the feature set leads to competitive results, outperforming all systems that participated in the SENSEVAL-3 competition on the Romanian data. Thus, with this specific method, a tightly controlled feature set improves the accuracy of the classifier, reaching 74.0% in the fine-grained and 78.7% in the coarse-grained evaluation.
This paper profiles significant differences in syntactic distribution and differences in word class frequencies for two treebanks of spoken and written German: the TüBa-D/S, a treebank of transliterated spontaneous dialogs, and the TüBa-D/Z treebank of newspaper articles published in the German daily newspaper ´die tageszeitung´(taz). The approach can be used more generally as a means of distinguishing and classifying language corpora of different genres.
This paper profiles significant differences in syntactic distribution and differences in word class frequencies for two treebanks of spoken and written German: the TüBa-D/S, a treebank of transliterated spontaneous dialogues, and the TüBa-D/Z treebank of newspaper articles published in the German daily newspaper die tageszeitung´(taz). The approach can be used more generally as a means of distinguishing and classifying language corpora of different genres.
This paper provides an overview of current research on a hybrid and robust parsing architecture for the morphological, syntactic and semantic annotation of German text corpora. The novel contribution of this research lies not in the individual parsing modules, each of which relies on state-of-the-art algorithms and techniques. Rather what is new about the present approach is the combination of these modules into a single architecture. This combination provides a means to significantly optimize the performance of each component, resulting in an increased accuracy of annotation.
This paper reports on the SYN-RA (SYNtax-based Reference Annotation) project, an on-going project of annotating German newspaper texts with referential relations. The project has developed an inventory of anaphoric and coreference relations for German in the context of a unified, XML-based annotation scheme for combining morphological, syntactic, semantic, and anaphoric information. The paper discusses how this unified annotation scheme relates to other formats currently discussed in the literature, in particular the annotation graph model of Bird and Liberman (2001) and the pie-in-thesky scheme for semantic annotation.
The purpose of this paper is to describe recent developments in the morphological, syntactic, and semantic annotation of the TüBa-D/Z treebank of German. The TüBa-D/Z annotation scheme is derived from the Verbmobil treebank of spoken German [4, 10], but has been extended along various dimensions to accommodate the characteristics of written texts. TüBa-D/Z uses as its data source the "die tageszeitung" (taz) newspaper corpus. The Verbmobil treebank annotation scheme distinguishes four levels of syntactic constituency: the lexical level, the phrasal level, the level of topological fields, and the clausal level. The primary ordering principle of a clause is the inventory of topological fields, which characterize the word order regularities among different clause types of German, and which are widely accepted among descriptive linguists of German [3, 6]. The TüBa-D/Z annotation relies on a context-free backbone (i.e. proper trees without crossing branches) of phrase structure combined with edge labels that specify the grammatical function of the phrase in question. The syntactic annotation scheme of the TüBa-D/Z is described in more detail in [12, 11]. TüBa-D/Z currently comprises approximately 15 000 sentences, with approximately 7 000 sentences being in the correction phase. The latter will be released along with an updated version of the existing treebank before the end of this year. The treebank is available in an XML format, in the NEGRA export format [1] and in the Penn treebank bracketing format. The XML format contains all types of information as described above, the NEGRA export format contains all sentenceinternal information while the Penn treebank format includes only those layers of information that can be expressed as pure tree structures. Over the course of the last year, more fine grained linguistic annotations have been added along the following dimensions: 1. the basic Stuttgart-Tübingen tagset, STTS, [9] labels have been enriched by relevant features of inflectional morphology, 2. named entity information has been encoded as part of the syntactic annotation, and 3. a set of anaphoric and coreference relations has been added to link referentially dependent noun phrases. In the following sections, we will describe each of these innovations in turn and will demonstrate how the additional annotations can be incorporated into one comprehensive annotation scheme.
Part-of-Speech tagging is generally performed by Markov models, based on bigram or trigram models. While Markov models have a strong concentration on the left context of a word, many languages require the inclusion of right context for correct disambiguation. We show for German that the best results are reached by a combination of left and right context. If only left context is available, then changing the direction of analysis and going from right to left improves the results. In a version of MBT (Daelemans et al., 1996) with default parameter settings, the inclusion of the right context improved POS tagging accuracy from 94.00% to 96.08%, thus corroborating our hypothesis. The version with optimized parameters reaches 96.73%.
Maschinelles Lernen wird häufig zur effzienten Annotation großer Datenmengen eingesetzt. Die Forschung zu maschinellen Lernverfahren beschränkt sich i.a. darauf unterschiedliche Lernverfahren zu vergelichen oder die optimale größe der Trainingsdaten zu bestimmen. Bisher wurde jedoch nicht untersucht, in wie weit sich linguistisches Wissen bei der Aufgabendefinition positiv auswirken kann. Dies soll hier anhand des Lernens von Base-Nominalphrasen mit drei unterschiedlichen Definitionen untersucht werden. Die Definitionen unterscheiden sich im Grad der linguistisch motivierten Erweiterungen, die zu einer eher praktisch motivierten ersten Definition hinzu kamen. Die Untersuchungen ergaben, dass sich die Anzahl der falsch klasssifizierten Wörter um ein Drittel reduzieren lässt.
In the last decade, the Penn treebank has become the standard data set for evaluating parsers. The fact that most parsers are solely evaluated on this specific data set leaves the question unanswered how much these results depend on the annotation scheme of the treebank. In this paper, we will investigate the influence which different decisions in the annotation schemes of treebanks have on parsing. The investigation uses the comparison of similar treebanks of German, NEGRA and TüBa-D/Z, which are subsequently modified to allow a comparison of the differences. The results show that deleted unary nodes and a flat phrase structure have a negative influence on parsing quality while a flat clause structure has a positive influence.