Search Results - "Schütze, Hinrich"

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  1. 1

    Position Information in Transformers: An Overview by Dufter, Philipp, Schmitt, Martin, Schütze, Hinrich

    “…Transformers are arguably the main workhorse in recent natural language processing research. By definition, a Transformer is invariant with respect to…”
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    Journal Article
  2. 2

    Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP by Schick, Timo, Udupa, Sahana, Schütze, Hinrich

    “…⚠ This paper contains prompts and model outputs that are offensive in nature. When trained on large, unfiltered crawls from the Internet, language models pick…”
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    Journal Article
  3. 3

    AutoExtend: Combining Word Embeddings with Semantic Resources by Rothe, Sascha, Schütze, Hinrich

    “…We present , a system that combines word embeddings with semantic resources by learning embeddings for non-word objects like synsets and entities and learning…”
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    Journal Article
  4. 4

    Bi-directional recurrent neural network with ranking loss for spoken language understanding by Ngoc Thang Vu, Gupta, Pankaj, Adel, Heike, Schutze, Hinrich

    “…This paper presents our latest investigation of recurrent neural networks for the slot filling task of spoken language understanding. We implement a…”
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    Conference Proceeding Journal Article
  5. 5

    Explaining pretrained language models' understanding of linguistic structures using construction grammar by Weissweiler, Leonie, Hofmann, Valentin, Köksal, Abdullatif, Schütze, Hinrich

    Published in Frontiers in artificial intelligence (12-10-2023)
    “…Construction Grammar (CxG) is a paradigm from cognitive linguistics emphasizing the connection between syntax and semantics. Rather than rules that operate on…”
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    Journal Article
  6. 6

    Statistical Models for Unsupervised, Semi-Supervised, and Supervised Transliteration Mining by Sajjad, Hassan, Schmid, Helmut, Fraser, Alexander, Schütze, Hinrich

    “…We present a generative model that efficiently mines transliteration pairs in a consistent fashion in three different settings: unsupervised, semi-supervised,…”
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    Journal Article
  7. 7

    Exploring the relationship between intonation and the lexicon: Evidence for lexicalised storage of intonation by Schweitzer, Katrin, Walsh, Michael, Calhoun, Sasha, Schütze, Hinrich, Möbius, Bernd, Schweitzer, Antje, Dogil, Grzegorz

    Published in Speech communication (01-02-2015)
    “…•Prosodic features are shown to depend on distributional properties at the word level.•Increasing frequency of word-accent pairs results in greater accent…”
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    Journal Article
  8. 8

    Measuring and Improving Consistency in Pretrained Language Models by Elazar, Yanai, Kassner, Nora, Ravfogel, Shauli, Ravichander, Abhilasha, Hovy, Eduard, Schütze, Hinrich, Goldberg, Yoav

    “…of a model—that is, the invariance of its behavior under meaning-preserving alternations in its input—is a highly desirable property in natural language…”
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    Journal Article
  9. 9

    Placing language in an integrated understanding system: Next steps toward human-level performance in neural language models by McClelland, James L., Hill, Felix, Rudolph, Maja, Baldridge, Jason, Schütze, Hinrich

    “…Language is crucial for human intelligence, but what exactly is its role? We take language to be a part of a system for understanding and communicating about…”
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    Journal Article
  10. 10

    Creating an online dictionary of abbreviations from MEDLINE by Chang, Jeffrey T, Schütze, Hinrich, Altman, Russ B

    “…The growth of the biomedical literature presents special challenges for both human readers and automatic algorithms. One such challenge derives from the common…”
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    Journal Article
  11. 11

    ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs by Yin, Wenpeng, Schütze, Hinrich, Xiang, Bing, Zhou, Bowen

    “…How to model a pair of sentences is a critical issue in many NLP tasks such as answer selection (AS), paraphrase identification (PI) and textual entailment…”
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    Journal Article
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  13. 13

    GAPSCORE: finding gene and protein names one word at a time by Chang, Jeffrey T., Schütze, Hinrich, Altman, Russ B.

    Published in Bioinformatics (22-01-2004)
    “…Motivation: New high-throughput technologies have accelerated the accumulation of knowledge about genes and proteins. However, much knowledge is still stored…”
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    Journal Article
  14. 14

    Syllable frequency effects in a context-sensitive segment production model by Wade, Travis, Dogil, Grzegorz, Schütze, Hinrich, Walsh, Michael, Möbius, Bernd

    Published in Journal of phonetics (01-04-2010)
    “…In this study we describe a new model of how phonetic knowledge guides speech production. In the Context Sequence model, target acoustic patterns are…”
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    Journal Article
  15. 15

    A cooccurrence-based thesaurus and two applications to information retrieval by Schütze, Hinrich, Pedersen, Jan O.

    Published in Information processing & management (01-05-1997)
    “…This paper presents a new method for computing a thesaurus from a text corpus. Each word is represented as a vector in a multi-dimensional space that captures…”
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    Journal Article
  16. 16

    True Few-Shot Learning with Prompts—A Real-World Perspective by Schick, Timo, Schütze, Hinrich

    “…Prompt-based approaches excel at few-shot learning. However, Perez et al. ( ) recently cast doubt on their performance as they had difficulty getting good…”
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    Journal Article
  17. 17

    Corpus-Level Fine-Grained Entity Typing by Yaghoobzadeh, Yadollah, Adel, Heike, Schuetze, Hinrich

    “…Extracting information about entities remains an important research area. This paper addresses the problem of corpus-level entity typing, i.e., inferring from…”
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    Journal Article
  18. 18

    Type-aware Convolutional Neural Networks for Slot Filling by Adel, Heike, Schuetze, Hinrich

    “…The slot filling task aims at extracting answers for queries about entities from text, such as "Who founded Apple". In this paper, we focus on the relation…”
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    Journal Article
  19. 19

    Attentive Convolution: Equipping CNNs with RNN-style Attention Mechanisms by Yin, Wenpeng, Schütze, Hinrich

    “…In NLP, convolutional neural networks (CNNs) have benefited less than recurrent neural networks (RNNs) from attention mechanisms. We hypothesize that this is…”
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    Journal Article
  20. 20

    Joint Semantic Synthesis and Morphological Analysis of the Derived Word by Cotterell, Ryan, Schütze, Hinrich

    “…Much like sentences are composed of words, words themselves are composed of smaller units. For example, the English word can be analyzed as + + . However, this…”
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    Journal Article