Translating prepositions is a difficult and under-studied problem in SMT. We present a novel method to improve the translation of prepositions by using noun classes to model their selectional preferences. We compare three variants of noun class information: (i) classes induced from the lexical resource GermaNet or obtained from clusterings based on either (ii) window information or (iii) syntactic features. Furthermore, we experiment with PP rule generalization. While we do not significantly improve over the baseline, our results demonstrate that (i) integrating selectional preferences as rigid class annotation in the parse tree is sub-optimal, and that (ii) clusterings based on window co-occurrence are more robust than syntax-based clusters or GermaNet classes for the task of modeling selectional preferences.

We present a method for the extraction of synonyms for German particle verbs based on a word-aligned German-English parallel corpus: by translating the particle verb to a pivot, which is then translated back, a set of synonym candidates can be extracted and ranked according to the respective translation probabilities. In order to deal with separated particle verbs, we apply re-ordering rules to the German part of the data. In our evaluation against a gold standard, we compare different pre-processing strategies (lemmatized vs. inflected forms) and introduce language model scores of synonym candidates in the context of the input particle verb as well as distributional similarity as additional re-ranking criteria. Our evaluation shows that distributional similarity as a re-ranking feature is more robust than language model scores and leads to an improved ranking of the synonym candidates. In addition to evaluating against a gold standard, we also present a small-scale manual evaluation.

In this paper, we present and evaluate an approach for the compositional alignment of compound nouns using comparable corpora from technical domains. The task of term alignment consists in relating a source language term to its translation in a list of target language terms with the help of a bilingual dictionary. Compound splitting allows to transform a compound into a sequence of components which can be translated separately and then related to multi-word target language terms. We present and evaluate a method for compound splitting, and compare two strategies for term alignment (bag-of-word vs. pattern-based). The simple word-based approach leads to a considerable amount of erroneous alignments, whereas the pattern-based approach reaches a decent precision. We also assess the reasons for alignment failures: in the comparable corpora used for our experiments, a substantial number of terms has no translation in the target language data; furthermore, the non-isomorphic structures of source and target language terms cause alignment failures in many cases.

We describe a new method for extracting Negative Polarity Item candidates (NPI candidates) from dependency-parsed German text corpora. Semi-automatic extraction of NPIs is a challenging task since NPIs do not have uniform categorical or other syntactic properties that could be used for detecting them; they occur as single words or as multi-word expressions of almost any syntactic category. Their defining property is of a semantic nature, they may only occur in the scope of negation and related semantic operators. In contrast to an earlier approach to NPI extraction from corpora, we specifically target multi-word expressions. Besides applying statistical methods to measure the co-occurrence of our candidate expressions with negative contexts, we also apply linguistic criteria in an attempt to determine to which degree they are idiomatic. Our method is evaluated by comparing the set of NPIs we found with the most comprehensive electronic list of German NPIs, which currently contains 165 entries. Our method retrieved 142 NPIs, 114 of which are new.

We report about tools for the extraction of German multiword expressions (MWEs) from text corpora; we extract word pairs, but also longer MWEs of different patterns, e.g. verb-noun structures with an additional prepositional phrase or adjective. Next to standard association-based extraction, we focus on morpho-syntactic, syntactic and lexical-choice features of the MWE candidates. A broad range of such properties (e.g. number and definiteness of nouns, adjacency of the MWE’s components and their position in the sentence, preferred lexical modifiers, etc.) along with relevant example sentences, are extracted from dependency-parsed text and stored in a data base. A sample precision evaluation and an analysis of extraction errors are provided along with the discussion of our extraction architecture. We furthermore measure the contribution of the features to the precision of the extraction: by using both morpho-syntactic and syntactic features, we achieve a higher precision in the identification of idiomatic MWEs, than by using only properties of one type.

Most of the research on the extraction of idiomatic multiword expressions (MWEs) focused on the acquisition of MWE types. In the present work we investigate whether a text instance of a potentially idiomatic MWE is actually used idiomatically in a given context or not. Inspired by the dataset provided by (Cook et al., 2008), we manually analysed 9,700 instances of potentially idiomatic prepositionnoun- verb triples (a frequent pattern among German MWEs) to identify, on token level, idiomatic vs. literal uses. In our dataset, all sentences are provided along with their morpho-syntactic properties. We describe our data extraction and annotation steps, and we discuss quantitative results from both EUROPARL and a German newspaper corpus. We discuss the relationship between idiomaticity and morpho-syntactic fixedness, and we address issues of ambiguity between literal and idiomatic use of MWEs. Our data show that EUROPARL is particularly well suited for MWE extraction, as most MWEs in this corpus are indeed used only idiomatically.

We present and partially evaluate procedures for the extraction of noun+verb collocation candidates from German text corpora, along with their morphosyntactic preferences, especially for the active vs. passive voice. We start from tokenized, tagged, lemmatized and chunked text, and we use extraction patterns formulated in the CQP corpus query language. We discuss the results of a precision evaluation, on administrative texts from the European Union: we find a considerable amount of specialized collocations, as well as general ones and complex predicates; overall the precision is considerably higher than that of a statistical extractor used as a baseline.

We present the main findings and preliminary results of an ongoing project aimed at developing a system for collocation extraction based on contextual morpho-syntactic properties. We explored two hybrid extraction methods: the first method applies language-indepedent statistical techniques followed by a linguistic filtering, while the second approach, available only for German, is based on a set of lexico-syntactic patterns to extract collocation candidates. To define extraction and filtering patterns, we studied a specific collocation category, the Verb-Noun constructions, using a model inspired by the systemic functional grammar, proposing three level analysis: lexical, functional and semantic criteria. From tagged and lemmatized corpus, we identify some contextual morpho-syntactic properties helping to filter the output of the statistical methods and to extract some potential interesting VN constructions (complex predicates vs complex predicators). The extracted candidates are validated and classified manually.