Abstract is missing.
- Front Matter [doi]
- Towards a Formal Distributional Semantics: Simulating Logical Calculi with TensorsEdward Grefenstette. 1-10 [doi]
- Montague Meets Markov: Deep Semantics with Probabilistic Logical FormIslam Beltagy, Cuong Chau, Gemma Boleda, Dan Garrette, Katrin Erk, Raymond J. Mooney. 11-21 [doi]
- Coarse to Fine Grained Sense Disambiguation in WikipediaHui Shen, Razvan C. Bunescu, Rada Mihalcea. 22-31 [doi]
- *SEM 2013 shared task: Semantic Textual SimilarityEneko Agirre, Daniel M. Cer, Mona T. Diab, Aitor Gonzalez-Agirre, Weiwei Guo. 32-43 [doi]
- UMBC_EBIQUITY-CORE: Semantic Textual Similarity SystemsLushan Han, Abhay L. Kashyap, Tim Finin, James Mayfield, Jonathan Weese. 44-52 [doi]
- iKernels-Core: Tree Kernel Learning for Textual SimilarityAliaksei Severyn, Massimo Nicosia, Alessandro Moschitti. 53-58 [doi]
- UNITOR-CORE_TYPED: Combining Text Similarity and Semantic Filters through SV RegressionDanilo Croce, Valerio Storch, Roberto Basili. 59-65 [doi]
- NTNU-CORE: Combining strong features for semantic similarityErwin Marsi, Hans Moen, Lars Bungum, Gleb Sizov, Björn Gambäck, André Lynum. 66-73 [doi]
- SXUCFN-Core: STS Models Integrating FrameNet Parsing InformationSai Wang, Ru Li, Ruibo Wang, Zhiqiang Wang, Xia Zhang. 74-79 [doi]
- Distinguishing Common and Proper NounsJudita Preiss, Mark Stevenson. 80-84 [doi]
- UCAM-CORE: Incorporating structured distributional similarity into STSTamara Polajnar, Laura Rimell, Douwe Kiela. 85-89 [doi]
- PolyUCOMP-CORE_TYPED: Computing Semantic Textual Similarity using Overlapped SensesJian Xu, Qin Lu. 90-95 [doi]
- HENRY-CORE: Domain Adaptation and Stacking for Text SimilarityMichael Heilman, Nitin Madnani. 96-102 [doi]
- DeepPurple: Lexical, String and Affective Feature Fusion for Sentence-Level Semantic Similarity EstimationNikolaos Malandrakis, Elias Iosif, Vassiliki Prokopi, Alexandros Potamianos, Shrikanth Narayanan. 103-108 [doi]
- UMCC_DLSI: Textual Similarity based on Lexical-Semantic featuresAlexander Chavez, Héctor Dávila, Yoan Gutiérrez, Armando Collazo, José Ignacio Abreu, Antonio Fernández Orquín, Andrés Montoyo, Rafael Muñoz. 109-118 [doi]
- BUT-TYPED: Using domain knowledge for computing typed similarityLubomir Otrusina, Pavel Smrz. 119-123 [doi]
- ECNUCS: Measuring Short Text Semantic Equivalence Using Multiple Similarity MeasurementsTiantian Zhu, Man Lan. 124-131 [doi]
- UBC_UOS-TYPED: Regression for typed-similarityEneko Agirre, Nikolaos Aletras, Aitor Gonzalez-Agirre, German Rigau, Mark Stevenson. 132-137 [doi]
- KnCe2013-CORE: Semantic Text Similarity by use of Knowledge BasesHermann Ziak, Roman Kern. 138-142 [doi]
- UPC-CORE: What Can Machine Translation Evaluation Metrics and Wikipedia Do for Estimating Semantic Textual Similarity?Alberto Barrón-Cedeño, Lluís Màrquez, María Fuentes Fort, Horacio Rodríguez, Jordi Turmo. 143-147 [doi]
- MayoClinicNLP-CORE: Semantic representations for textual similarityStephen T. Wu, Dongqing Zhu, Ben Carterette, Hongfang Liu. 148-154 [doi]
- SRIUBC-Core: Multiword Soft Similarity Models for Textual SimilarityEric Yeh. 155-161 [doi]
- LIPN-CORE: Semantic Text Similarity using n-grams, WordNet, Syntactic Analysis, ESA and Information Retrieval based FeaturesDavide Buscaldi, Joseph Le Roux, Jorge J. García Flores, Adrian Popescu. 162-168 [doi]
- UNIBA-CORE: Combining Strategies for Semantic Textual SimilarityAnnalina Caputo, Pierpaolo Basile, Giovanni Semeraro. 169-175 [doi]
- DLS$@$CU-CORE: A Simple Machine Learning Model of Semantic Textual SimilarityMd. Arafat Sultan, Steven Bethard, Tamara Sumner. 176-180 [doi]
- KLUE-CORE: A regression model of semantic textual similarityPaul Greiner, Thomas Proisl, Stefan Evert, Besim Kabashi. 181-186 [doi]
- IBM_EG-CORE: Comparing multiple Lexical and NE matching features in measuring Semantic Textual similaritySara Noeman. 187-193 [doi]
- SOFTCARDINALITY-CORE: Improving Text Overlap with Distributional Measures for Semantic Textual SimilaritySergio Jiménez 0001, Claudia Jeanneth Becerra, Alexander F. Gelbukh. 194-201 [doi]
- CLaC-CORE: Exhaustive Feature Combination for Measuring Textual SimilarityEhsan Shareghi, Sabine Bergler. 202-206 [doi]
- UniMelb_NLP-CORE: Integrating predictions from multiple domains and feature sets for estimating semantic textual similaritySpandana Gella, Bahar Salehi, Marco Lui, Karl Grieser, Paul Cook, Timothy Baldwin. 207-215 [doi]
- CFILT-CORE: Semantic Textual Similarity using Universal Networking LanguageAvishek Dan, Pushpak Bhattacharyya. 216-220 [doi]
- CPN-CORE: A Text Semantic Similarity System Infused with Opinion KnowledgeCarmen Banea, Yoonjung Choi, Lingjia Deng, Samer Hassan, Michael Mohler, Bishan Yang, Claire Cardie, Rada Mihalcea, Janyce Wiebe. 221-228 [doi]
- INAOE_UPV-CORE: Extracting Word Associations from Document Corpora to estimate Semantic Textual SimilarityFernando Sánchez-Vega, Manuel Montes-y-Gómez, Paolo Rosso, Luis Villaseñor Pineda. 229-233 [doi]
- CNGL-CORE: Referential Translation Machines for Measuring Semantic SimilarityErgun Biçici, Josef van Genabith. 234-240 [doi]
- A Dataset of Syntactic-Ngrams over Time from a Very Large Corpus of English BooksYoav Goldberg, Jon Orwant. 241-247 [doi]
- Unsupervised Word Usage Similarity in Social Media TextsSpandana Gella, Paul Cook, Bo Han. 248-253 [doi]
- More Words and Bigger PicturesDavid A. Forsyth. 254 [doi]
- Exploring Vector Space Models to Predict the Compositionality of German Noun-Noun CompoundsSabine Schulte im Walde, Stefan Müller, Stephen Roller. 255-265 [doi]
- Predicting the Compositionality of Multiword Expressions Using Translations in Multiple LanguagesBahar Salehi, Paul Cook. 266-275 [doi]
- Metaphor Identification as InterpretationEkaterina Shutova. 276-285 [doi]
- Using the text to evaluate short answers for reading comprehension exercisesAndrea Horbach, Alexis Palmer, Manfred Pinkal. 286-295 [doi]
- Choosing the Right Words: Characterizing and Reducing Error of the Word Count ApproachHansen Andrew Schwartz, Johannes C. Eichstaedt, Eduardo Blanco 0002, Lukasz Dziurzynski, Margaret L. Kern, Stephanie Ramones, Martin E. P. Seligman, Lyle H. Ungar. 296-305 [doi]
- Automatically Identifying Implicit Arguments to Improve Argument Linking and Coherence ModelingMichael Roth, Anette Frank. 306-316 [doi]
- Bootstrapping Semantic Role Labelers from Parallel DataMikhail Kozhevnikov, Ivan Titov. 317-327 [doi]
- Semantic Parsing Freebase: Towards Open-domain Semantic ParsingQingqing Cai, Alexander Yates. 328-338 [doi]