Hinrich Schütze

Biographic Data

ID1719423
NAMEHinrich Schütze
GIVEN NAMESHinrich
FAMILY NAMESchütze
SIGNATURESCHÜTZE H
AFFILIATIONSLudwig-Maximilians-Universität München
VERIFIEDNo
TOTAL WORKS10
TOTAL CITATIONS6
WORKS AS AUTHOR10
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2007
LATEST PUBLICATION YEAR2025
H-INDEX2

  • Problem Solving Through Human–AI Preference-based Cooperation

    Open AccessSubhabrata Dutta, Timo Kaufmann et al.ARTICLEComputational Linguistics2025

    While there is a widespread belief that artificial general intelligence—or even superhuman AI—is imminent, complex problems in expert domains are far from being solved. We argue that such problems require human–AI cooperation and that the current state of the art in generative AI is unable to play the role of a reliable partner due to a multitude of shortcomings, including difficulty in keeping track of a complex solution artifact (e.g., a softwa…

  • Position Information in Transformers

    Open AccessPhilipp Dufter, Martin Schmitt et al.ARTICLEComputational Linguistics2022Cited by: 1References: 1

    Transformers are arguably the main workhorse in recent natural language processing research. By definition, a Transformer is invariant with respect to reordering of the input. However, language is inherently sequential and word order is essential to the semantics and syntax of an utterance. In this article, we provide an overview and theoretical comparison of existing methods to incorporate position information into Transformer models. The object…

  • Statistical Models for Unsupervised, Semi-Supervised, and Supervised Transliteration Mining

    Open AccessHassan Sajjad, Helmut Schmid et al.ARTICLEComputational Linguistics2017References: 2

    We present a generative model that efficiently mines transliteration pairs in a consistent fashion in three different settings: unsupervised, semi-supervised, and supervised transliteration mining. The model interpolates two sub-models, one for the generation of transliteration pairs and one for the generation of non-transliteration pairs (i.e., noise). The model is trained on noisy unlabeled data using the EM algorithm. During training the trans…

  • AutoExtend

    Open AccessSascha Rothe, Hinrich SchützeARTICLEComputational Linguistics2017References: 6

    We present AutoExtend, a system that combines word embeddings with semantic resources by learning embeddings for non-word objects like synsets and entities and learning word embeddings that incorporate the semantic information from the resource. The method is based on encoding and decoding the word embeddings and is flexible in that it can take any word embeddings as input and does not need an additional training corpus. The obtained embeddings l…

  • The Operation Sequence Model—Combining N-Gram-Based and Phrase-Based Statistical Machine Translation

    Open AccessNadir Durrani, Helmut Schmid et al.ARTICLEComputational Linguistics2015Cited by: 1References: 3

    In this article, we present a novel machine translation model, the Operation Sequence Model (OSM), which combines the benefits of phrase-based and N-gram-based statistical machine translation (SMT) and remedies their drawbacks. The model represents the translation process as a linear sequence of operations. The sequence includes not only translation operations but also reordering operations. As in N-gram-based SMT, the model is: (i) based on mini…

  • Knowledge Sources for Constituent Parsing of German, a Morphologically Rich and Less-Configurational Language

    Open AccessAlexander Fraser, Helmut Schmid et al.ARTICLEComputational Linguistics2012References: 1

    We study constituent parsing of German, a morphologically rich and less-configurational language. We use a probabilistic context-free grammar treebank grammar that has been adapted to the morphologically rich properties of German by markovization and special features added to its productions. We evaluate the impact of adding lexical knowledge. Then we examine both monolingual and bilingual approaches to parse reranking. Our reranking parser is th…

  • Half-Context Language Models

    Open AccessHinrich Schütze, Michael WalshARTICLEComputational Linguistics2011References: 1

    This article investigates the effects of different degrees of contextual granularity on language model performance. It presents a new language model that combines clustering and half-contextualization, a novel representation of contexts. Half-contextualization is based on the half-context hypothesis that states that the distributional characteristics of a word or bigram are best represented by treating its context distribution to the left and rig…

  • Syllable frequency effects in a context-sensitive segment production model

    Open AccessTravis Wade, Grzegorz Dogil et al.ARTICLEJournal of Phonetics2010Cited by: 2References: 42

  • Introduction to Information Retrieval

    Open AccessChristopher D Manning, Prabhakar Raghavan et al.BOOKIntroduction to Information…2008

    Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections.…

  • Prepositional Phrase Attachment without Oracles

    Open AccessMichaela Atterer, Hinrich SchützeARTICLEComputational Linguistics2007Cited by: 2

    Work on prepositional phrase (PP) attachment resolution generally assumes that there is an oracle that provides the two hypothesized structures that we want to choose between. The information that there are two possible attachment sites and the information about the lexical heads of those phrases is usually extracted from gold-standard parse trees. We show that the performance of reattachment methods is higher with such an oracle than without. Be…