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ORCID*SEM@NAACL-HLT 2013: Atlanta, Georgia, USA
- Mona T. Diab, Timothy Baldwin, Marco Baroni:
Proceedings of the Second Joint Conference on Lexical and Computational Semantics, *SEM 2013, June 13-14, 2013, Atlanta, Georgia, USA. Association for Computational Linguistics 2013, ISBN 978-1-937284-48-0 - Edward Grefenstette:
Towards a Formal Distributional Semantics: Simulating Logical Calculi with Tensors. 1-10 - Islam Beltagy, Cuong Chau, Gemma Boleda, Dan Garrette, Katrin Erk, Raymond J. Mooney:
Montague Meets Markov: Deep Semantics with Probabilistic Logical Form. 11-21 - Hui Shen, Razvan C. Bunescu, Rada Mihalcea:
Coarse to Fine Grained Sense Disambiguation in Wikipedia. 22-31 - Eneko Agirre, Daniel M. Cer, Mona T. Diab, Aitor Gonzalez-Agirre, Weiwei Guo:
*SEM 2013 shared task: Semantic Textual Similarity. 32-43 - Lushan Han, Abhay L. Kashyap, Tim Finin, James Mayfield, Jonathan Weese:
UMBC_EBIQUITY-CORE: Semantic Textual Similarity Systems. 44-52 - Aliaksei Severyn, Massimo Nicosia, Alessandro Moschitti:
iKernels-Core: Tree Kernel Learning for Textual Similarity. 53-58 - Danilo Croce, Valerio Storch, Roberto Basili:
UNITOR-CORE_TYPED: Combining Text Similarity and Semantic Filters through SV Regression. 59-65 - Erwin Marsi, Hans Moen, Lars Bungum, Gleb Sizov, Björn Gambäck, André Lynum:
NTNU-CORE: Combining strong features for semantic similarity. 66-73 - Sai Wang, Ru Li, Ruibo Wang, Zhiqiang Wang, Xia Zhang:
SXUCFN-Core: STS Models Integrating FrameNet Parsing Information. 74-79 - Judita Preiss, Mark Stevenson:
Distinguishing Common and Proper Nouns. 80-84 - Tamara Polajnar, Laura Rimell, Douwe Kiela:
UCAM-CORE: Incorporating structured distributional similarity into STS. 85-89 - Jian Xu, Qin Lu:
PolyUCOMP-CORE_TYPED: Computing Semantic Textual Similarity using Overlapped Senses. 90-95 - Michael Heilman, Nitin Madnani:
HENRY-CORE: Domain Adaptation and Stacking for Text Similarity. 96-102 - Nikolaos Malandrakis, Elias Iosif, Vassiliki Prokopi, Alexandros Potamianos, Shrikanth S. Narayanan:
DeepPurple: Lexical, String and Affective Feature Fusion for Sentence-Level Semantic Similarity Estimation. 103-108 - Alexander Chavez, Héctor Dávila, Yoan Gutiérrez, Armando Collazo, José Ignacio Abreu, Antonio Fernández Orquín, Andrés Montoyo, Rafael Muñoz:
UMCC_DLSI: Textual Similarity based on Lexical-Semantic features. 109-118 - Lubomír Otrusina, Pavel Smrz:
BUT-TYPED: Using domain knowledge for computing typed similarity. 119-123 - Tiantian Zhu, Man Lan:
ECNUCS: Measuring Short Text Semantic Equivalence Using Multiple Similarity Measurements. 124-131 - Eneko Agirre, Nikolaos Aletras, Aitor Gonzalez-Agirre, German Rigau, Mark Stevenson:
UBC_UOS-TYPED: Regression for typed-similarity. 132-137 - Hermann Ziak, Roman Kern:
KnCe2013-CORE: Semantic Text Similarity by use of Knowledge Bases. 138-142 - Alberto Barrón-Cedeño, Lluís Màrquez, María Fuentes Fort, Horacio Rodríguez, Jordi Turmo:
UPC-CORE: What Can Machine Translation Evaluation Metrics and Wikipedia Do for Estimating Semantic Textual Similarity? 143-147 - Stephen T. Wu, Dongqing Zhu, Ben Carterette, Hongfang Liu:
MayoClinicNLP-CORE: Semantic representations for textual similarity. 148-154 - Eric Yeh:
SRIUBC-Core: Multiword Soft Similarity Models for Textual Similarity. 155-161 - Davide Buscaldi, Joseph Le Roux, Jorge J. García Flores, Adrian Popescu:
LIPN-CORE: Semantic Text Similarity using n-grams, WordNet, Syntactic Analysis, ESA and Information Retrieval based Features. 162-168 - Annalina Caputo, Pierpaolo Basile, Giovanni Semeraro:
UNIBA-CORE: Combining Strategies for Semantic Textual Similarity. 169-175 - Md. Arafat Sultan, Steven Bethard, Tamara Sumner:
DLS$@$CU-CORE: A Simple Machine Learning Model of Semantic Textual Similarity. 176-180 - Paul Greiner, Thomas Proisl, Stefan Evert, Besim Kabashi:
KLUE-CORE: A regression model of semantic textual similarity. 181-186 - Sara Noeman:
IBM_EG-CORE: Comparing multiple Lexical and NE matching features in measuring Semantic Textual similarity. 187-193 - Sergio Jiménez, Claudia Jeanneth Becerra, Alexander F. Gelbukh:
SOFTCARDINALITY-CORE: Improving Text Overlap with Distributional Measures for Semantic Textual Similarity. 194-201 - Ehsan Shareghi, Sabine Bergler:
CLaC-CORE: Exhaustive Feature Combination for Measuring Textual Similarity. 202-206 - Spandana Gella, Bahar Salehi, Marco Lui, Karl Grieser, Paul Cook, Timothy Baldwin:
UniMelb_NLP-CORE: Integrating predictions from multiple domains and feature sets for estimating semantic textual similarity. 207-215 - Avishek Dan, Pushpak Bhattacharyya:
CFILT-CORE: Semantic Textual Similarity using Universal Networking Language. 216-220 - Carmen Banea, Yoonjung Choi, Lingjia Deng, Samer Hassan, Michael Mohler, Bishan Yang, Claire Cardie, Rada Mihalcea, Janyce Wiebe:
CPN-CORE: A Text Semantic Similarity System Infused with Opinion Knowledge. 221-228 - Fernando Sánchez-Vega, Manuel Montes-y-Gómez, Paolo Rosso, Luis Villaseñor Pineda:
INAOE_UPV-CORE: Extracting Word Associations from Document Corpora to estimate Semantic Textual Similarity. 229-233 - Ergun Biçici, Josef van Genabith:
CNGL-CORE: Referential Translation Machines for Measuring Semantic Similarity. 234-240 - Yoav Goldberg, Jon Orwant:
A Dataset of Syntactic-Ngrams over Time from a Very Large Corpus of English Books. 241-247 - Spandana Gella, Paul Cook, Bo Han:
Unsupervised Word Usage Similarity in Social Media Texts. 248-253 - David A. Forsyth:
More Words and Bigger Pictures. 254 - Sabine Schulte im Walde, Stefan Müller, Stephen Roller:
Exploring Vector Space Models to Predict the Compositionality of German Noun-Noun Compounds. 255-265 - Bahar Salehi, Paul Cook:
Predicting the Compositionality of Multiword Expressions Using Translations in Multiple Languages. 266-275 - Ekaterina Shutova:
Metaphor Identification as Interpretation. 276-285 - Andrea Horbach, Alexis Palmer, Manfred Pinkal:
Using the text to evaluate short answers for reading comprehension exercises. 286-295 - H. Andrew Schwartz, Johannes C. Eichstaedt, Eduardo Blanco, Lukasz Dziurzynski, Margaret L. Kern, Stephanie Ramones, Martin E. P. Seligman, Lyle H. Ungar:
Choosing the Right Words: Characterizing and Reducing Error of the Word Count Approach. 296-305 - Michael Roth, Anette Frank:
Automatically Identifying Implicit Arguments to Improve Argument Linking and Coherence Modeling. 306-316 - Mikhail Kozhevnikov, Ivan Titov:
Bootstrapping Semantic Role Labelers from Parallel Data. 317-327 - Qingqing Cai, Alexander Yates:
Semantic Parsing Freebase: Towards Open-domain Semantic Parsing. 328-338
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