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title: "University of California, Berkeley — works, page 2"
description: "107 works by researchers of University of California, Berkeley in our library · page 2 of 2, the most cited first."
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# Works of University of California, Berkeley

107 works · page 2 of 2 · the most cited first · [← University of California, Berkeley](https://md.research.elamur.ai/free/en/universities/university-of-california-berkeley/)

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101. [**Coreference Semantics from Web Features**Mohit Bansal, Dan Klein*2012 · 61 citations*](https://md.research.elamur.ai/free/en/papers/bansal-2012-coreference-semantics-web-features/)
102. [**Decentralized Entity-Level Modeling for Coreference Resolution**Greg Durrett, David Hall, Dan Klein*2013 · 48 citations*](https://md.research.elamur.ai/free/en/papers/durrett-2013-decentralized-entity-level-modeling-coreference/)
103. [**Cross-Domain Generalization of Neural Constituency Parsers**Daniel Fried, Nikita Kitaev, Dan Klein*2019 · 46 citations*](https://md.research.elamur.ai/free/en/papers/fried-2019-cross-domain-generalization-neural-constituency/)
104. [**Aligning FrameNet and WordNet based on Semantic Neighborhoods**Óscar Ferrández, Michael Ellsworth, Rafael Muñoz, et al.*2010 · 37 citations*](https://md.research.elamur.ai/free/en/papers/ferrandez-2010-aligning-framenet-wordnet-based-semantic/)
105. [**Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference**Nikita Kitaev, Dan Klein*2020 · 36 citations*](https://md.research.elamur.ai/free/en/papers/kitaev-2020-tetra-tagging-word-synchronous-parsing/)
106. [**Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches**Daniel Fried, Nicholas Tomlin, Jennifer Hu, et al.*2023 · 36 citations*](https://md.research.elamur.ai/free/en/papers/fried-2023-pragmatics-language-grounding-phenomena-tasks/)
107. [**Evaluating Machine Translation Datasets for Low-Web Data Languages: A Gendered Lens**Hellina Hailu Nigatu, Bethelhem Yemane Mamo, Bontu Fufa Balcha, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/nigatu-2026-evaluating-machine-translation-datasets-low/)

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