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SemEval@NAACL-HLT 2015: Denver, Colorado, USA
- Daniel M. Cer, David Jurgens, Preslav Nakov, Torsten Zesch:
Proceedings of the 9th International Workshop on Semantic Evaluation, SemEval@NAACL-HLT 2015, Denver, Colorado, USA, June 4-5, 2015. The Association for Computer Linguistics 2015, ISBN 978-1-941643-40-2 - Wei Xu, Chris Callison-Burch, Bill Dolan:
SemEval-2015 Task 1: Paraphrase and Semantic Similarity in Twitter (PIT). 1-11 - Guido Zarrella, John C. Henderson, Elizabeth M. Merkhofer, Laura Strickhart:
MITRE: Seven Systems for Semantic Similarity in Tweets. 12-17 - Helena Gómez-Adorno, Darnes Vilariño, David Pinto, Grigori Sidorov:
CICBUAPnlp: Graph-Based Approach for Answer Selection in Community Question Answering Task. 18-22 - Dario Bertero, Pascale Fung:
HLTC-HKUST: A Neural Network Paraphrase Classifier using Translation Metrics, Semantic Roles and Lexical Similarity Features. 23-28 - Ngoc Phuoc An Vo, Simone Magnolini, Octavian Popescu:
FBK-HLT: An Effective System for Paraphrase Identification and Semantic Similarity in Twitter. 29-33 - Jiang Zhao, Man Lan:
ECNU: Leveraging Word Embeddings to Boost Performance for Paraphrase in Twitter. 34-39 - Rob van der Goot, Gertjan van Noord:
ROB: Using Semantic Meaning to Recognize Paraphrases. 40-44 - Mahalakshmi Shanumuga Sundaram, Anand Kumar Madasamy
, Soman Kotti Padannayil:
AMRITA_CEN$@$SemEval-2015: Paraphrase Detection for Twitter using Unsupervised Feature Learning with Recursive Autoencoders. 45-50 - Taneeya Satyapanich, Hang Gao, Tim Finin:
Ebiquity: Paraphrase and Semantic Similarity in Twitter using Skipgrams. 51-55 - Ergun Biçici:
RTM-DCU: Predicting Semantic Similarity with Referential Translation Machines. 56-63 - Asli Eyecioglu
Twitter Paraphrase Identification with Simple Overlap Features and SVMs. 64-69 - Mladen Karan, Goran Glavas, Jan Snajder, Bojana Dalbelo Basic, Ivan Vulic, Marie-Francine Moens:
TKLBLIIR: Detecting Twitter Paraphrases with TweetingJay. 70-74 - Rafael-Michael Karampatsis:
CDTDS: Predicting Paraphrases in Twitter via Support Vector Regression. 75-79 - Yang Liu, Chengjie Sun, Lei Lin, Xiaolong Wang:
yiGou: A Semantic Text Similarity Computing System Based on SVM. 80-84 - Liling Tan, Carolina Scarton, Lucia Specia, Josef van Genabith:
USAAR-SHEFFIELD: Semantic Textual Similarity with Deep Regression and Machine Translation Evaluation Metrics. 85-89 - Md. Rashadul Hasan Rakib, Aminul Islam, Evangelos E. Milios:
TrWP: Text Relatedness using Word and Phrase Relatedness. 90-95 - Hanna Béchara, Hernani Costa, Shiva Taslimipoor, Rohit Gupta, Constantin Orasan, Gloria Corpas Pastor
MiniExperts: An SVM Approach for Measuring Semantic Textual Similarity. 96-101 - Ngoc Phuoc An Vo, Simone Magnolini, Octavian Popescu:
FBK-HLT: A New Framework for Semantic Textual Similarity. 102-106 - Sakethram Karumuri, Viswanadh Kumar Reddy Vuggumudi, Sai Charan Raj Chitirala:
UMDuluth-BlueTeam: SVCSTS - A Multilingual and Chunk Level Semantic Similarity System. 107-110 - Nataliia Plotnikova, Gabriella Lapesa, Thomas Proisl, Stefan Evert:
SemantiKLUE: Semantic Textual Similarity with Maximum Weight Matching. 111-116 - Jiang Zhao, Man Lan, Junfeng Tian:
ECNU: Using Traditional Similarity Measurements and Word Embedding for Semantic Textual Similarity Estimation. 117-122 - Hamed Hassanzadeh
UQeResearch: Semantic Textual Similarity Quantification. 123-127 - Naoko Miura, Tomohiro Takagi:
WSL: Sentence Similarity Using Semantic Distance Between Words. 128-131 - Davide Buscaldi, Jorge García Flores, Iván V. Meza, Isaac Rodriguez:
SOPA: Random Forests Regression for the Semantic Textual Similarity task. 132-137 - Gábor Recski
MathLingBudapest: Concept Networks for Semantic Similarity. 138-142 - Piyush Arora, Chris Hokamp, Jennifer Foster, Gareth J. F. Jones:
DCU: Using Distributional Semantics and Domain Adaptation for the Semantic Textual Similarity SemEval-2015 Task 2. 143-147 - Md. Arafat Sultan, Steven Bethard, Tamara Sumner:
DLS$@$CU: Sentence Similarity from Word Alignment and Semantic Vector Composition. 148-153 - Basma Hassan
FCICU: The Integration between Sense-Based Kernel and Surface-Based Methods to Measure Semantic Textual Similarity. 154-158 - Evan Jaffe, Lifeng Jin, David King, Marten van Schijndel
AZMAT: Sentence Similarity Using Associative Matrices. 159-163 - Rajendra Banjade, Nobal Bikram Niraula, Nabin Maharjan, Vasile Rus, Dan Stefanescu, Mihai C. Lintean, Dipesh Gautam:
NeRoSim: A System for Measuring and Interpreting Semantic Textual Similarity. 164-171 - Lushan Han, Justin Martineau, Doreen Cheng, Christopher Thomas:
Samsung: Align-and-Differentiate Approach to Semantic Textual Similarity. 172-177 - Eneko Agirre, Aitor Gonzalez-Agirre, Iñigo Lopez-Gazpio, Montse Maritxalar
UBC: Cubes for English Semantic Textual Similarity and Supervised Approaches for Interpretable STS. 178-183 - Ana Alves
ASAP-II: From the Alignment of Phrases to Textual Similarity. 184-189 - Tu Thanh Vu, Quan Hung Tran, Son Bao Pham:
TATO: Leveraging on Multiple Strategies for Semantic Textual Similarity. 190-195 - Yongshuai Hou, Cong Tan, Xiaolong Wang, Yaoyun Zhang, Jun Xu, Qingcai Chen:
HITSZ-ICRC: Exploiting Classification Approach for Answer Selection in Community Question Answering. 196-202 - Massimo Nicosia, Simone Filice, Alberto Barrón-Cedeño, Iman Saleh, Hamdy Mubarak, Wei Gao, Preslav Nakov, Giovanni Da San Martino, Alessandro Moschitti, Kareem Darwish, Lluís Màrquez, Shafiq R. Joty, Walid Magdy:
QCRI: Answer Selection for Community Question Answering - Experiments for Arabic and English. 203-209 - Xiaoqiang Zhou, Baotian Hu, Jiaxin Lin, Yang Xiang, Xiaolong Wang:
ICRC-HIT: A Deep Learning based Comment Sequence Labeling System for Answer Selection Challenge. 210-214 - Quan Hung Tran, Vu D. Tran
JAIST: Combining multiple features for Answer Selection in Community Question Answering. 215-219 - Amin Heydari Alashty, Saeed Rahmani, Meysam Roostaee, Seyed Mostafa Fakhrahmad
Shiraz: A Proposed List Wise Approach to Answer Validation. 220-225 - Reham Mohamed, Maha Ragab, Heba Abdelnasser, Nagwa M. El-Makky, Marwan Torki:
Al-Bayan: A Knowledge-based System for Arabic Answer Selection. 226-230 - Ngoc Phuoc An Vo, Simone Magnolini, Octavian Popescu:
FBK-HLT: An Application of Semantic Textual Similarity for Answer Selection in Community Question Answering. 231-235 - Liang Yi, Jianxiang Wang, Man Lan:
ECNU: Using Multiple Sources of CQA-based Information for Answers Selection and YES/NO Response Inference. 236-241 - Ivan Zamanov, Marina Kraeva, Nelly Hateva, Ivana Yovcheva, Ivelina Nikolova, Galia Angelova:
Voltron: A Hybrid System For Answer Validation Based On Lexical And Distance Features. 242-246 - Björn Rudzewitz, Ramon Ziai:
CoMiC: Adapting a Short Answer Assessment System for Answer Selection. 247-251 - Eneko Agirre, Carmen Banea, Claire Cardie, Daniel M. Cer, Mona T. Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Iñigo Lopez-Gazpio, Montse Maritxalar
SemEval-2015 Task 2: Semantic Textual Similarity, English, Spanish and Pilot on Interpretability. 252-263 - Christian Hänig, Robert Remus, Xose de la Puente:
ExB Themis: Extensive Feature Extraction from Word Alignments for Semantic Textual Similarity. 264-268 - Preslav Nakov, Lluís Màrquez, Walid Magdy, Alessandro Moschitti, James R. Glass, Bilal Randeree:
SemEval-2015 Task 3: Answer Selection in Community Question Answering. 269-281 - Yonatan Belinkov, Mitra Mohtarami, Scott Cyphers, James R. Glass:
VectorSLU: A Continuous Word Vector Approach to Answer Selection in Community Question Answering Systems. 282-287 - Andrea Moro, Roberto Navigli:
SemEval-2015 Task 13: Multilingual All-Words Sense Disambiguation and Entity Linking. 288-297 - Marianna Apidianaki
LIMSI: Translations as Source of Indirect Supervision for Multilingual All-Words Sense Disambiguation and Entity Linking. 298-302 - Noémie Elhadad, Sameer Pradhan, Sharon Lipsky Gorman, Suresh Manandhar, Wendy W. Chapman, Guergana K. Savova:
SemEval-2015 Task 14: Analysis of Clinical Text. 303-310 - Jun Xu, Yaoyun Zhang, Jingqi Wang, Yonghui Wu, Min Jiang, Ergin Soysal, Hua Xu:
UTH-CCB: The Participation of the SemEval 2015 Challenge - Task 14. 311-314 - Vít Baisa, Jane Bradbury, Silvie Cinková, Ismaïl El Maarouf, Adam Kilgarriff, Octavian Popescu:
SemEval-2015 Task 15: A CPA dictionary-entry-building task. 315-324 - Yukun Feng, Qiao Deng, Dong Yu:
BLCUNLP: Corpus Pattern Analysis for Verbs Based on Dependency Chain. 325-328 - Rocco Tripodi, Marcello Pelillo:
WSD-games: a Game-Theoretic Algorithm for Unsupervised Word Sense Disambiguation. 329-334 - Dirk Weissenborn, Feiyu Xu, Hans Uszkoreit:
DFKI: Multi-objective Optimization for the Joint Disambiguation of Entities and Nouns & Deep Verb Sense Disambiguation. 335-339 - Eniafe Festus Ayetiran, Guido Boella:
EBL-Hope: Multilingual Word Sense Disambiguation Using a Hybrid Knowledge-Based Technique. 340-344 - Marten Postma, Rubén Izquierdo, Piek Vossen:
VUA-background : When to Use Background Information to Perform Word Sense Disambiguation. 345-349 - Petr Fanta, Roman Sudarikov, Ondrej Bojar
TeamUFAL: WSD+EL as Document Retrieval. 350-354 - Pablo Ruiz, Thierry Poibeau:
EL92: Entity Linking Combining Open Source Annotators via Weighted Voting. 355-359 - Pierpaolo Basile, Annalina Caputo, Giovanni Semeraro:
UNIBA: Combining Distributional Semantic Models and Sense Distribution for Multilingual All-Words Sense Disambiguation and Entity Linking. 360-364 - Steve L. Manion:
SUDOKU: Treating Word Sense Disambiguation & Entitiy Linking as a Deterministic Problem - via an Unsupervised & Iterative Approach. 365-369 - Nghia Huynh, Quoc Ho:
TeamHCMUS: Analysis of Clinical Text. 370-374 - Kai Hakala:
UTU: Adapting Biomedical Event Extraction System to Disorder Attribute Detection. 375-379 - Maryna Chernyshevich, Vadim Stankevitch:
IHS-RD-Belarus: Identification and Normalization of Disorder Concepts in Clinical Notes. 380-384 - Omid Ghiasvand, Rohit J. Kate:
UWM: A Simple Baseline Method for Identifying Attributes of Disease and Disorder Mentions in Clinical Text. 385-388 - Goran Glavas:
TAKELAB: Medical Information Extraction and Linking with MINERAL. 389-393 - Jitendra Jonnagaddala, Siaw-Teng Liaw, Pradeep Kumar Ray
TMUNSW: Identification of Disorders and Normalization to SNOMED-CT Terminology in Unstructured Clinical Notes. 394-398 - Kristina Doing-Harris, Sean Igo, Jianlin Shi, John F. Hurdle:
UtahPOET: Disorder Mention Identification and Context Slot Filling with Cognitive Inspiration. 399-405 - André Leal, Bruno Martins, Francisco M. Couto:
ULisboa: Recognition and Normalization of Medical Concepts. 406-411 - Parth Pathak, Pinal Patel, Vishal Panchal, Sagar Soni, Kinjal Dani, Amrish Patel, Narayan Choudhary:
ezDI: A Supervised NLP System for Clinical Narrative Analysis. 412-416 - James Gung, John David Osborne, Steven Bethard:
CUAB: Supervised Learning of Disorders and their Attributes using Relations. 417-421 - Sérgio Matos, José Sequeira, José Luís Oliveira:
BioinformaticsUA: Machine Learning and Rule-Based Recognition of Disorders and Clinical Attributes from Patient Notes. 422-426 - Asma Ben Abacha, Aikaterini Karanasiou, Yassine Mrabet, Júlio Cesar dos Reis:
LIST-LUX: Disorder Identification from Clinical Texts. 427-432 - Chad Mills, Gina-Anne Levow:
CMILLS: Adapting Semantic Role Labeling Features to Dependency Parsing. 433-437 - Ted Pedersen:
Duluth: Word Sense Discrimination in the Service of Lexicography. 438-442 - Irene Russo, Tommaso Caselli, Carlo Strapparava:
SemEval-2015 Task 9: CLIPEval Implicit Polarity of Events. 443-450 - Sara Rosenthal, Preslav Nakov, Svetlana Kiritchenko, Saif M. Mohammad, Alan Ritter, Veselin Stoyanov:
SemEval-2015 Task 10: Sentiment Analysis in Twitter. 451-463 - Aliaksei Severyn, Alessandro Moschitti:
UNITN: Training Deep Convolutional Neural Network for Twitter Sentiment Classification. 464-469 - Aniruddha Ghosh, Guofu Li, Tony Veale, Paolo Rosso, Ekaterina Shutova, John A. Barnden, Antonio Reyes:
SemEval-2015 Task 11: Sentiment Analysis of Figurative Language in Twitter. 470-478 - Canberk Özdemir, Sabine Bergler:
CLaC-SentiPipe: SemEval2015 Subtasks 10 B, E, and Task 11. 479-485 - Maria Pontiki
SemEval-2015 Task 12: Aspect Based Sentiment Analysis. 486-495 - Zhiqiang Toh, Jian Su:
NLANGP: Supervised Machine Learning System for Aspect Category Classification and Opinion Target Extraction. 496-501 - Mauro Dragoni:
SHELLFBK: An Information Retrieval-based System For Multi-Domain Sentiment Analysis. 502-509 - Abeed Sarker
DIEGOLab: An Approach for Message-level Sentiment Classification in Twitter. 510-514 - Li Dong, Furu Wei, Yichun Yin, Ming Zhou, Ke Xu:
Splusplus: A Feature-Rich Two-stage Classifier for Sentiment Analysis of Tweets. 515-519 - Ayushi Dalmia, Manish Gupta, Vasudeva Varma:
IIIT-H at SemEval 2015: Twitter Sentiment Analysis - The Good, the Bad and the Neutral! 520-526 - Sebastian Ebert, Ngoc Thang Vu, Hinrich Schütze:
CIS-positive: A Combination of Convolutional Neural Networks and Support Vector Machines for Sentiment Analysis in Twitter. 527-532 - Milagros Fernández Gavilanes, Tamara Álvarez-López, Jonathan Juncal-Martínez, Enrique Costa-Montenegro, Francisco Javier González-Castaño:
GTI: An Unsupervised Approach for Sentiment Analysis in Twitter. 533-538 - Héctor Cerezo-Costas, Diego Celix-Salgado:
Gradiant-Analytics: Training Polarity Shifters with CRFs for Message Level Polarity Detection. 539-544 - Peijia Li, Weiqun Xu, Chenglong Ma, Jia Sun, Yonghong Yan:
IOA: Improving SVM Based Sentiment Classification Through Post Processing. 545-550 - Huizhi Liang, Richard Fothergill, Timothy Baldwin:
RoseMerry: A Baseline Message-level Sentiment Classification System. 551-555 - Esteban Castillo, Ofelia Cervantes, Darnes Vilariño, David Báez, J. Alfredo Sánchez:
UDLAP: Sentiment Analysis Using a Graph-Based Representation. 556-560 - Zhihua Zhang, GuoShun Wu, Man Lan:
ECNU: Multi-level Sentiment Analysis on Twitter Using Traditional Linguistic Features and Word Embedding Features. 561-567 - Hussam Hamdan, Patrice Bellot, Frédéric Béchet:
Lsislif: Feature Extraction and Label Weighting for Sentiment Analysis in Twitter. 568-573 - Mayte Giménez, Ferran Pla, Lluís-F. Hurtado:
ELiRF: A SVM Approach for SA tasks in Twitter at SemEval-2015. 574-581 - Matthias Hagen, Martin Potthast, Michel Büchner, Benno Stein:
Webis: An Ensemble for Twitter Sentiment Detection. 582-589 - Satarupa Guha, Aditya Joshi, Vasudeva Varma:
Sentibase: Sentiment Analysis in Twitter on a Budget. 590-594 - Pierpaolo Basile, Nicole Novielli:
UNIBA: Sentiment Analysis of English Tweets Combining Micro-blogging, Lexicon and Semantic Features. 595-600 - Ayush Kumar, Vamsi Krishna, Asif Ekbal:
IITPSemEval: Sentiment Discovery from 140 Characters. 601-607 - Fatih Uzdilli, Martin Jaggi, Dominic Egger, Pascal Julmy, Leon Derczynski, Mark Cieliebak
Swiss-Chocolate: Combining Flipout Regularization and Random Forests with Artificially Built Subsystems to Boost Text-Classification for Sentiment. 608-612 - Silvio Amir, Wang Ling, Ramón Fernandez Astudillo, Bruno Martins, Mário J. Silva, Isabel Trancoso:
INESC-ID: A Regression Model for Large Scale Twitter Sentiment Lexicon Induction. 613-618 - Nataliia Plotnikova, Micha Kohl, Kevin Volkert, Stefan Evert, Andreas Lerner, Natalie Dykes, Heiko Ermer:
KLUEless: Polarity Classification and Association. 619-625 - Ruth Talbot, Chloe Acheampong, Richard Wicentowski:
SWASH: A Naive Bayes Classifier for Tweet Sentiment Identification. 626-630 - Richard Wicentowski:
SWATCS65: Sentiment Classification Using an Ensemble of Class Projects. 631-635 - Yousef Alhessi, Richard Wicentowski:
SWATAC: A Sentiment Analyzer using One-Vs-Rest Logistic Regression. 636-639 - William Boag, Peter Potash, Anna Rumshisky:
TwitterHawk: A Feature Bucket Based Approach to Sentiment Analysis. 640-646 - Prerna Chikersal, Soujanya Poria, Erik Cambria
SeNTU: Sentiment Analysis of Tweets by Combining a Rule-based Classifier with Supervised Learning. 647-651 - Ramón Fernandez Astudillo, Silvio Amir, Wang Ling, Bruno Martins, Mário J. Silva, Isabel Trancoso:
INESC-ID: Sentiment Analysis without Hand-Coded Features or Linguistic Resources using Embedding Subspaces. 652-656 - Richard Townsend, Adam Tsakalidis, Yiwei Zhou, Bo Wang, Maria Liakata, Arkaitz Zubiaga, Alexandra I. Cristea
WarwickDCS: From Phrase-Based to Target-Specific Sentiment Recognition. 657-663 - Xu Han, Binyang Li, Jing Ma
UIR-PKU: Twitter-OpinMiner System for Sentiment Analysis in Twitter at SemEval 2015. 664-668 - Riley Collins, Daniel May, Noah Weinthal, Richard Wicentowski:
SWAT-CMW: Classification of Twitter Emotional Polarity using a Multiple-Classifier Decision Schema and Enhanced Emotion Tagging. 669-672 - Hongzhi Xu, Enrico Santus, Anna Laszlo, Chu-Ren Huang
LLT-PolyU: Identifying Sentiment Intensity in Ironic Tweets. 673-678 - Hoang Long Nguyen, Duc Nguyen Trung, Dosam Hwang, Jason J. Jung:
KELabTeam: A Statistical Approach on Figurative Language Sentiment Analysis in Twitter. 679-683 - Cynthia Van Hee
LT3: Sentiment Analysis of Figurative Tweets: piece of cake #NotReally. 684-688 - Parth Gupta, Jon Ander Gómez
PRHLT: Combination of Deep Autoencoders with Classification and Regression Techniques for SemEval-2015 Task 11. 689-693 - Delia Irazú Hernández Farías, Emilio Sulis, Viviana Patti, Giancarlo Ruffo, Cristina Bosco:
ValenTo: Sentiment Analysis of Figurative Language Tweets with Irony and Sarcasm. 694-698 - Sarah McGillion, Héctor Martínez Alonso, Barbara Plank:
CPH: Sentiment analysis of Figurative Language on Twitter #easypeasy #not. 699-703 - Francesco Barbieri, Francesco Ronzano, Horacio Saggion:
UPF-taln: SemEval 2015 Tasks 10 and 11. Sentiment Analysis of Literal and Figurative Language in Twitter. 704-708 - Maria Karanasou, Christos Doulkeridis, Maria Halkidi:
DsUniPi: An SVM-based Approach for Sentiment Analysis of Figurative Language on Twitter. 709-713 - Aitor García Pablos, Montse Cuadros, German Rigau
V3: Unsupervised Aspect Based Sentiment Analysis for SemEval2015 Task 12. 714-718 - Orphée De Clercq, Marjan Van de Kauter, Els Lefever, Véronique Hoste:
LT3: Applying Hybrid Terminology Extraction to Aspect-Based Sentiment Analysis. 719-724 - Anderson Uilian Kauer, Viviane Pereira Moreira:
UFRGS: Identifying Categories and Targets in Customer Reviews. 725-729 - Salud M. Jiménez-Zafra, Eugenio Martínez-Cámara, María Teresa Martín-Valdivia, Luis Alfonso Ureña López:
SINAI: Syntactic Approach for Aspect-Based Sentiment Analysis. 730-735 - Zhihua Zhang, Man Lan:
ECNU: Extracting Effective Features from Multiple Sequential Sentences for Target-dependent Sentiment Analysis in Reviews. 736-741 - Ravikanth Repaka, Ranga Reddy Pallelra, Akshay Reddy Koppula, Venkata Subhash Movva:
UMDuluth-CS8761-12: A Novel Machine Learning Approach for Aspect Based Sentiment Analysis. 742-747 - Iñaki San Vicente, Xabier Saralegi, Rodrigo Agerri
EliXa: A Modular and Flexible ABSA Platform. 748-752 - Hussam Hamdan, Patrice Bellot
Lsislif: CRF and Logistic Regression for Opinion Target Extraction and Sentiment Polarity Analysis. 753-758 - Satarupa Guha, Aditya Joshi, Vasudeva Varma:
SIEL: Aspect Based Sentiment Analysis in Reviews. 759-766 - José Saias:
Sentiue: Target and Aspect based Sentiment Analysis in SemEval-2015 Task 12. 767-771 - Zhifei Zhang, Jian-Yun Nie, Hongling Wang:
TJUdeM: A Combination Classifier for Aspect Category Detection and Sentiment Polarity Classification. 772-777 - Anne-Lyse Minard, Manuela Speranza, Eneko Agirre, Itziar Aldabe
SemEval-2015 Task 4: TimeLine: Cross-Document Event Ordering. 778-786 - Tommaso Caselli, Antske Fokkens, Roser Morante, Piek Vossen:
SPINOZA_VU: An NLP Pipeline for Cross Document TimeLines. 787-791 - Hector Llorens, Nathanael Chambers, Naushad UzZaman, Nasrin Mostafazadeh, James F. Allen, James Pustejovsky:
SemEval-2015 Task 5: QA TempEval - Evaluating Temporal Information Understanding with Question Answering. 792-800 - Paramita Mirza, Anne-Lyse Minard:
HLT-FBK: a Complete Temporal Processing System for QA TempEval. 801-805 - Steven Bethard, Leon Derczynski, Guergana Savova, James Pustejovsky, Marc Verhagen:
SemEval-2015 Task 6: Clinical TempEval. 806-814 - Sumithra Velupillai, Danielle L. Mowery, Samir E. AbdelRahman, Lee M. Christensen, Wendy W. Chapman:
BluLab: Temporal Information Extraction for the 2015 Clinical TempEval Challenge. 815-819 - Borja Navarro, Estela Saquete:
GPLSIUA: Combining Temporal Information and Topic Modeling for Cross-Document Event Ordering. 820-824 - Bilel Moulahi, Jannik Strötgen, Michael Gertz, Lynda Tamine:
HeidelToul: A Baseline Approach for Cross-document Event Ordering. 825-829 - Yongshuai Hou, Cong Tan, Qingcai Chen, Xiaolong Wang:
HITSZ-ICRC: An Integration Approach for QA TempEval Challenge. 830-834 - Hegler Tissot, Genevieve Gorrell
UFPRSheffield: Contrasting Rule-based and Support Vector Machine Approaches to Time Expression Identification in Clinical TempEval. 835-839 - Haritz Salaberri, Iker Salaberri, Olatz Arregi
IXAGroupEHUDiac: A Multiple Approach System towards the Diachronic Evaluation of Texts. 840-845 - Liling Tan, Noam Ordan:
USAAR-CHRONOS: Crawling the Web for Temporal Annotations. 846-850 - Marcos Zampieri, Alina Maria Ciobanu, Vlad Niculae, Liviu P. Dinu:
AMBRA: A Ranking Approach to Temporal Text Classification. 851-855 - Haritz Salaberri, Olatz Arregi
IXAGroupEHUSpaceEval: (X-Space) A WordNet-based approach towards the Automatic Recognition of Spatial Information following the ISO-Space Annotation Scheme. 856-861 - Jennifer D'Souza, Vincent Ng:
UTD: Ensemble-Based Spatial Relation Extraction. 862-869 - Octavian Popescu, Carlo Strapparava:
SemEval 2015, Task 7: Diachronic Text Evaluation. 870-878 - Terrence Szymanski, Gerard Lynch:
UCD : Diachronic Text Classification with Character, Word, and Syntactic N-grams. 879-883 - James Pustejovsky, Parisa Kordjamshidi, Marie-Francine Moens, Aaron Levine, Seth Dworman, Zachary Yocum:
SemEval-2015 Task 8: SpaceEval. 884-894 - Eric Nichols, Fadi Botros:
SpRL-CWW: Spatial Relation Classification with Independent Multi-class Models. 895-901 - Georgeta Bordea, Paul Buitelaar, Stefano Faralli
SemEval-2015 Task 17: Taxonomy Extraction Evaluation (TExEval). 902-910 - Gregory Grefenstette:
INRIASAC: Simple Hypernym Extraction Methods. 911-914 - Stephan Oepen, Marco Kuhlmann, Yusuke Miyao, Daniel Zeman, Silvie Cinková, Dan Flickinger, Jan Hajic, Zdenka Uresová:
SemEval 2015 Task 18: Broad-Coverage Semantic Dependency Parsing. 915-926 - Yantao Du, Fan Zhang, Xun Zhang, Weiwei Sun, Xiaojun Wan:
Peking: Building Semantic Dependency Graphs with a Hybrid Parser. 927-931 - Liling Tan, Rohit Gupta, Josef van Genabith:
USAAR-WLV: Hypernym Generation with Deep Neural Nets. 932-937 - Bamfa Ceesay, Wen-Juan Hou:
NTNU: An Unsupervised Knowledge Approach for Taxonomy Extraction. 938-943 - Els Lefever:
LT3: A Multi-modular Approach to Automatic Taxonomy Construction. 944-948 - Luis Espinosa Anke, Horacio Saggion, Francesco Ronzano:
TALN-UPF: Taxonomy Learning Exploiting CRF-Based Hypernym Extraction on Encyclopedic Definitions. 949-954 - Guillaume Cleuziou, Davide Buscaldi, Gaël Dias, Vincent Levorato, Christine Largeron:
QASSIT: A Pretopological Framework for the Automatic Construction of Lexical Taxonomies from Raw Texts. 955-959 - Guntis Barzdins, Peteris Paikens, Didzis Gosko:
Riga: from FrameNet to Semantic Frames with C6.0 Rules. 960-964 - Jenna Kanerva, Juhani Luotolahti, Filip Ginter:
Turku: Semantic Dependency Parsing as a Sequence Classification. 965-969 - Mariana S. C. Almeida, André F. T. Martins:
Lisbon: Evaluating TurboSemanticParser on Multiple Languages and Out-of-Domain Data. 970-973
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