Thursday, June 4, 2015 |
| 08:00–08:30 | Registration |
| 08:30–09:00 | Opening remarks |
| 09:00–10:00 | Joint *SEM and SemEval keynote talk by Marco Baroni, “Playing ficles and running with the corbons: What (multimodal) distributional semantic models learn during their childhood” |
| | Track I - Text Similarity and Question Answering (Part 1)
(chair: Octavian Popescu) |
| 10:00–10:15 | SemEval-2015 Task 1: Paraphrase and Semantic Similarity in Twitter (PIT)
Wei Xu, Chris Callison-Burch and Bill Dolan |
| 10:15–10:25 | MITRE: Seven Systems for Semantic Similarity in Tweets
Guido Zarrella, John Henderson, Elizabeth M. Merkhofer and Laura Strickhart |
| 10:25–11:00 | Poster Session: Tasks 1, 2, and 3 (Part 1) |
| | CICBUAPnlp: Graph-Based Approach for Answer Selection in Community Question Answering Task
Helena Gomez, Darnes Vilariño, David Pinto and Grigori Sidorov |
| | HLTC-HKUST: A Neural Network Paraphrase Classifier using Translation Metrics, Semantic Roles and Lexical Similarity Features
Dario Bertero and Pascale Fung |
| | FBK-HLT: An Effective System for Paraphrase Identification and Semantic Similarity in Twitter
Ngoc Phuoc An Vo, Simone Magnolini and Octavian Popescu |
| | ECNU: Leveraging Word Embeddings to Boost Performance for Paraphrase in Twitter
Jiang Zhao and Man Lan |
| | ROB: Using Semantic Meaning to Recognize Paraphrases
Rob van der Goot and Gertjan van Noord |
| | AMRITA_CEN@SemEval-2015: Paraphrase Detection for Twitter using Unsupervised Feature Learning with Recursive Autoencoders
Mahalakshmi Shanumuga Sundaram, Anand Kumar Madasamy and Soman Kotti Padannayil |
| | Ebiquity: Paraphrase and Semantic Similarity in Twitter using Skipgrams
Taneeya Satyapanich, Hang Gao and Tim Finin |
| | RTM-DCU: Predicting Semantic Similarity with Referential Translation Machines
Ergun Bicici |
| | Twitter Paraphrase Identification with Simple Overlap Features and SVMs
Asli Eyecioglu and Bill Keller |
| | TKLBLIIR: Detecting Twitter Paraphrases with TweetingJay
Mladen Karan, Goran Glavaš, Jan Šnajder, Bojana Dalbelo Bašić, Ivan Vulić and Marie-Francine Moens |
| | CDTDS: Predicting Paraphrases in Twitter via Support Vector Regression
Rafael - Michael Karampatsis |
| | yiGou: A Semantic Text Similarity Computing System Based on SVM
Yang Liu, Chengjie Sun, Lei Lin and Xiaolong Wang |
| | USAAR-SHEFFIELD: Semantic Textual Similarity with Deep Regression and Machine Translation Evaluation Metrics
Liling Tan, Carolina Scarton, Lucia Specia and Josef van Genabith |
| | TrWP: Text Relatedness using Word and Phrase Relatedness
Md Rashadul Hasan Rakib, Aminul Islam and Evangelos Milios |
| | MiniExperts: An SVM Approach for Measuring Semantic Textual Similarity
Hanna Béchara, Hernani Costa, Shiva Taslimipoor, Rohit Gupta, Constantin Orasan, Gloria Corpas Pastor and Ruslan Mitkov |
| | FBK-HLT: A New Framework for Semantic Textual Similarity
Ngoc Phuoc An Vo, Simone Magnolini and Octavian Popescu |
| | UMDuluth-BlueTeam: SVCSTS - A Multilingual and Chunk Level Semantic Similarity System
Sakethram Karumuri, Viswanadh Kumar Reddy Vuggumudi and Sai Charan Raj Chitirala |
| | SemantiKLUE: Semantic Textual Similarity with Maximum Weight Matching
Nataliia Plotnikova, Gabriella Lapesa, Thomas Proisl and Stefan Evert |
| | ECNU: Using Traditional Similarity Measurements and Word Embedding for Semantic Textual Similarity Estimation
Jiang Zhao, Man Lan and Jun Feng Tian |
| | UQeResearch: Semantic Textual Similarity Quantification
Hamed Hassanzadeh, Tudor Groza, Anthony Nguyen and Jane Hunter |
| | WSL: Sentence Similarity Using Semantic Distance Between Words
Naoko Miura and Tomohiro Takagi |
| | SOPA: Random Forests Regression for the Semantic Textual Similarity task
Davide Buscaldi, Jorge Garcia Flores, Ivan V. Meza and Isaac Rodriguez |
| | MathLingBudapest: Concept Networks for Semantic Similarity
Gábor Recski and Judit Ács |
| | DCU: Using Distributional Semantics and Domain Adaptation for the Semantic Textual Similarity SemEval-2015 Task 2
Piyush Arora, Chris Hokamp, Jennifer Foster and Gareth Jones |
| | DLS@CU: Sentence Similarity from Word Alignment and Semantic Vector Composition
Md Arafat Sultan, Steven Bethard and Tamara Sumner |
| | FCICU: The Integration between Sense-Based Kernel and Surface-Based Methods to Measure Semantic Textual Similarity
Basma Hassan, Samir AbdelRahman and Reem Bahgat |
| | AZMAT: Sentence Similarity Using Associative Matrices
Evan Jaffe, Lifeng Jin, David King and Marten van Schijndel |
| | NeRoSim: A System for Measuring and Interpreting Semantic Textual Similarity
Rajendra Banjade, Nobal Bikram Niraula, Nabin Maharjan, Vasile Rus, Dan Stefanescu, Mihai Lintean and Dipesh Gautam |
| | Samsung: Align-and-Differentiate Approach to Semantic Textual Similarity
Lushan Han, Justin Martineau, Doreen Cheng and Christopher Thomas |
| | UBC: Cubes for English Semantic Textual Similarity and Supervised Approaches for Interpretable STS
Eneko Agirre, Aitor Gonzalez-Agirre, Inigo Lopez-Gazpio, Montse Maritxalar, German Rigau and Larraitz Uria |
| | ASAP-II: From the Alignment of Phrases to Textual Similarity
Ana Alves, David Simões, Hugo Gonçalo Oliveira and Adriana Ferrugento |
| | TATO: Leveraging on Multiple Strategies for Semantic Textual Similarity
Tu Thanh Vu, Quan Hung Tran and Son Bao Pham |
| | HITSZ-ICRC: Exploiting Classification Approach for Answer Selection in Community Question Answering
Yongshuai Hou, Cong Tan, Xiaolong Wang, Yaoyun Zhang, Jun Xu and Qingcai Chen |
| | QCRI: Answer Selection for Community Question Answering - Experiments for Arabic and English
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 Joty and Walid Magdy |
| | ICRC-HIT: A Deep Learning based Comment Sequence Labeling System for Answer Selection Challenge
Xiaoqiang Zhou, Baotian Hu, Jiaxin Lin, Yang xiang and Xiaolong Wang |
| | JAIST: Combining multiple features for Answer Selection in Community Question Answering
Quan Hung Tran, Vu Tran, Tu Vu, Minh Nguyen and Son Bao Pham |
| | Shiraz: A Proposed List Wise Approach to Answer Validation
Amin Heydari Alashty, Saeed Rahmani, Meysam Roostaee and Mostafa Fakhrahmad |
| | Al-Bayan: A Knowledge-based System for Arabic Answer Selection
Reham Mohamed, Maha Ragab, Heba Abdelnasser, Nagwa M. El-Makky and Marwan Torki |
| | FBK-HLT: An Application of Semantic Textual Similarity for Answer Selection in Community Question Answering
Ngoc Phuoc An Vo, Simone Magnolini and Octavian Popescu |
| | ECNU: Using Multiple Sources of CQA-based Information for Answers Selection and YES/NO Response Inference
Liang Yi, JianXiang Wang and Man Lan |
| | Voltron: A Hybrid System For Answer Validation Based On Lexical And Distance Features
Ivan Zamanov, Marina Kraeva, Nelly Hateva, Ivana Yovcheva, Ivelina Nikolova and Galia Angelova |
| | CoMiC: Adapting a Short Answer Assessment System for Answer Selection
Björn Rudzewitz and Ramon Ziai |
| 10:30–11:00 | Coffee Break and Poster Session |
| | Track I - Text Similarity and Question Answering (Part 2)
(chair: Carmen Banea) |
| 11:00–11:15 | SemEval-2015 Task 2: Semantic Textual Similarity, English, Spanish and Pilot on Interpretability
Eneko Agirre, Carmen Banea, Claire Cardie, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Inigo Lopez-Gazpio, Montse Maritxalar, Rada Mihalcea, German Rigau, Larraitz Uria and Janyce Wiebe |
| 11:15–11:25 | ExB Themis: Extensive Feature Extraction from Word Alignments for Semantic Textual Similarity
Christian Hänig, Robert Remus and Xose de la Puente |
| 11:25–11:40 | SemEval-2015 Task 3: Answer Selection in Community Question Answering
Preslav Nakov, Lluís Màrquez, Walid Magdy, Alessandro Moschitti, Jim Glass and Bilal Randeree |
| 11:40–11:50 | VectorSLU: A Continuous Word Vector Approach to Answer Selection in Community Question Answering Systems
Yonatan Belinkov, Mitra Mohtarami, Scott Cyphers and James Glass |
| 11:50–12:30 | Poster Session: Tasks 1, 2, and 3 (Part 2) |
| 12:30–13:30 | Lunch Break |
| | Track IV - Word Sense Disambiguation and Induction
(chair: David Jurgens) |
| 13:30–13:45 | SemEval-2015 Task 13: Multilingual All-Words Sense Disambiguation and Entity Linking
Andrea Moro and Roberto Navigli |
| 13:45–13:55 | LIMSI: Translations as Source of Indirect Supervision for Multilingual All-Words Sense Disambiguation and Entity Linking
Marianna Apidianaki and Li Gong |
| 13:55–14:10 | SemEval-2015 Task 14: Analysis of Clinical Text
Noémie Elhadad, Sameer Pradhan, Sharon Gorman, Suresh Manandhar, Wendy Chapman and Guergana Savova |
| 14:10–14:20 | UTH-CCB: The Participation of the SemEval 2015 Challenge – Task 14
Jun Xu, Yaoyun Zhang, Jingqi Wang, Yonghui Wu, Min Jiang, Ergin Soysal and Hua Xu |
| 14:20–14:35 | SemEval-2015 Task 15: A CPA dictionary-entry-building task
Vít Baisa, Jane Bradbury, Silvie Cinkova, Ismail El Maarouf, Adam Kilgarriff and Octavian Popescu |
| 14:35–14:45 | BLCUNLP: Corpus Pattern Analysis for Verbs Based on Dependency Chain
Yukun Feng, Qiao Deng and Dong Yu |
| 14:45–16:00 | Poster Session: Tasks 13, 14, and 15 |
| | WSD-games: a Game-Theoretic Algorithm for Unsupervised Word Sense Disambiguation
Rocco Tripodi and Marcello Pelillo |
| | DFKI: Multi-objective Optimization for the Joint Disambiguation of Entities and Nouns & Deep Verb Sense Disambiguation
Dirk Weissenborn, Feiyu Xu and Hans Uszkoreit |
| | EBL-Hope: Multilingual Word Sense Disambiguation Using a Hybrid Knowledge-Based Technique
Eniafe Festus Ayetiran and Guido Boella |
| | VUA-background : When to Use Background Information to Perform Word Sense Disambiguation
Marten Postma, Ruben Izquierdo and Piek Vossen |
| | TeamUFAL: WSD+EL as Document Retrieval
Petr Fanta, Roman Sudarikov and Ondrej Bojar |
| | EL92: Entity Linking Combining Open Source Annotators via Weighted Voting
Pablo Ruiz and Thierry Poibeau |
| | UNIBA: Combining Distributional Semantic Models and Sense Distribution for Multilingual All-Words Sense Disambiguation and Entity Linking
Pierpaolo Basile, Annalina Caputo and Giovanni Semeraro |
| | SUDOKU: Treating Word Sense Disambiguation & Entitiy Linking as a Deterministic Problem - via an Unsupervised & Iterative Approach
Steve L. Manion |
| | TeamHCMUS: Analysis of Clinical Text
Nghia Huynh and Quoc Ho |
| | UTU: Adapting Biomedical Event Extraction System to Disorder Attribute Detection
Kai Hakala |
| | IHS-RD-Belarus: Identification and Normalization of Disorder Concepts in Clinical Notes
Maryna Chernyshevich and Vadim Stankevitch |
| | UWM: A Simple Baseline Method for Identifying Attributes of Disease and Disorder Mentions in Clinical Text
Omid Ghiasvand and Rohit Kate |
| | TAKELAB: Medical Information Extraction and Linking with MINERAL
Goran Glavaš |
| | TMUNSW: Identification of Disorders and Normalization to SNOMED-CT Terminology in Unstructured Clinical Notes
Jitendra Jonnagaddala, Siaw-Teng Liaw, Pradeep Ray, Manish Kumar and Hong-Jie Dai |
| | UtahPOET: Disorder Mention Identification and Context Slot Filling with Cognitive Inspiration
Kristina Doing-Harris, Sean Igo, Jianlin Shi and John Hurdle |
| | ULisboa: Recognition and Normalization of Medical Concepts
André Leal, Bruno Martins and Francisco Couto |
| | ezDI: A Supervised NLP System for Clinical Narrative Analysis
Parth Pathak, Pinal Patel, Vishal Panchal, Sagar Soni, Kinjal Dani, Amrish Patel and Narayan Choudhary |
| | CUAB: Supervised Learning of Disorders and their Attributes using Relations
James Gung, John Osborne and Steven Bethard |
| | BioinformaticsUA: Machine Learning and Rule-Based Recognition of Disorders and Clinical Attributes from Patient Notes
Sérgio Matos, José Sequeira and José Luís Oliveira |
| | LIST-LUX: Disorder Identification from Clinical Texts
Asma Ben Abacha, Aikaterini Karanasiou, Yassine Mrabet and Julio Cesar Dos Reis |
| | CMILLS: Adapting Semantic Role Labeling Features to Dependency Parsing
Chad Mills and Gina-Anne Levow |
| | Duluth: Word Sense Discrimination in the Service of Lexicography
Ted Pedersen |
| 15:30–16:00 | Coffee Break and Poster Session |
| | Track III - Sentiment
(chair: Richard Wicentowski) |
| 16:00–16:15 | SemEval-2015 Task 9: CLIPEval Implicit Polarity of Events
Irene Russo, Tommaso Caselli and Carlo Strapparava |
| 16:15–16:30 | SemEval-2015 Task 10: Sentiment Analysis in Twitter
Sara Rosenthal, Preslav Nakov, Svetlana Kiritchenko, Saif Mohammad, Alan Ritter and Veselin Stoyanov |
| 16:30–16:40 | UNITN: Training Deep Convolutional Neural Network for Twitter Sentiment Classification
Aliaksei Severyn and Alessandro Moschitti |
| 16:40–16:55 | SemEval-2015 Task 11: Sentiment Analysis of Figurative Language in Twitter
Aniruddha Ghosh, Guofu Li, Tony Veale, Paolo Rosso, Ekaterina Shutova, John Barnden and Antonio Reyes |
| 16:55–17:05 | CLaC-SentiPipe: SemEval2015 Subtasks 10 B,E, and Task 11
Canberk Özdemir and Sabine Bergler |
| 17:05–17:20 | SemEval-2015 Task 12: Aspect Based Sentiment Analysis
Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, Suresh Manandhar and Ion Androutsopoulos |
| 17:20–17:30 | NLANGP: Supervised Machine Learning System for Aspect Category Classification and Opinion Target Extraction
Zhiqiang Toh and Jian Su |
| 17:30–18:30 | Poster Session: Tasks 9, 10, 11, and 12 |
| | SHELLFBK: An Information Retrieval-based System For Multi-Domain Sentiment Analysis
Mauro Dragoni |
| | DIEGOLab: An Approach for Message-level Sentiment Classification in Twitter
Abeed Sarker, Azadeh Nikfarjam, Davy Weissenbacher and Graciela Gonzalez |
| | Splusplus: A Feature-Rich Two-stage Classifier for Sentiment Analysis of Tweets
Li Dong, Furu Wei, Yichun Yin, Ming Zhou and Ke Xu |
| | IIIT-H at SemEval 2015: Twitter Sentiment Analysis – The Good, the Bad and the Neutral!
Ayushi Dalmia, Manish Gupta and Vasudeva Varma |
| | CIS-positive: A Combination of Convolutional Neural Networks and Support Vector Machines for Sentiment Analysis in Twitter
Sebastian Ebert, Ngoc Thang Vu and Hinrich Schütze |
| | GTI: An Unsupervised Approach for Sentiment Analysis in Twitter
Milagros Fernández-Gavilanes, Tamara Álvarez-López, Jonathan Juncal-Martínez, Enrique Costa-Montenegro and Francisco Javier González-Castaño |
| | Gradiant-Analytics: Training Polarity Shifters with CRFs for Message Level Polarity Detection
Héctor Cerezo-Costas and Diego Celix-Salgado |
| | IOA: Improving SVM Based Sentiment Classification Through Post Processing
Peijia Li, Weiqun Xu, Chenglong Ma, Jia Sun and Yonghong Yan |
| | RoseMerry: A Baseline Message-level Sentiment Classification System
Huizhi Liang, Richard Fothergill and Timothy Baldwin |
| | UDLAP: Sentiment Analysis Using a Graph-Based Representation
Esteban Castillo, Ofelia Cervantes, Darnes Vilariño, David Báez and Alfredo Sánchez |
| | ECNU: Multi-level Sentiment Analysis on Twitter Using Traditional Linguistic Features and Word Embedding Features
Zhihua Zhang, Guoshun Wu and Man Lan |
| | Lsislif: Feature Extraction and Label Weighting for Sentiment Analysis in Twitter
Hussam Hamdan, Patrice Bellot and Frederic Bechet |
| | ELiRF: A SVM Approach for SA tasks in Twitter at SemEval-2015
Mayte Giménez, Ferran Pla and Lluís-F. Hurtado |
| | Webis: An Ensemble for Twitter Sentiment Detection
Matthias Hagen, Martin Potthast, Michel Büchner and Benno Stein |
| | Sentibase: Sentiment Analysis in Twitter on a Budget
Satarupa Guha, Aditya Joshi and Vasudeva Varma |
| | UNIBA: Sentiment Analysis of English Tweets Combining Micro-blogging, Lexicon and Semantic Features
Pierpaolo Basile and Nicole Novielli |
| | IITPSemEval: Sentiment Discovery from 140 Characters
Ayush Kumar, Vamsi Krishna and Asif Ekbal |
| | Swiss-Chocolate: Combining Flipout Regularization and Random Forests with Artificially Built Subsystems to Boost Text-Classification for Sentiment
Fatih Uzdilli, Martin Jaggi, Dominic Egger, Pascal Julmy, Leon Derczynski and Mark Cieliebak |
| | INESC-ID: A Regression Model for Large Scale Twitter Sentiment Lexicon Induction
Silvio Amir, Wang Ling, Ramón Astudillo, Bruno Martins, Mario J. Silva and Isabel Trancoso |
| | KLUEless: Polarity Classification and Association
Nataliia Plotnikova, Micha Kohl, Kevin Volkert, Stefan Evert, Andreas Lerner, Natalie Dykes and Heiko Ermer |
| | SWASH: A Naive Bayes Classifier for Tweet Sentiment Identification
Ruth Talbot, Chloe Acheampong and Richard Wicentowski |
| | SWATCS65: Sentiment Classification Using an Ensemble of Class Projects
Richard Wicentowski |
| | SWATAC: A Sentiment Analyzer using One-Vs-Rest Logistic Regression
Yousef Alhessi and Richard Wicentowski |
| | TwitterHawk: A Feature Bucket Based Approach to Sentiment Analysis
William Boag, Peter Potash and Anna Rumshisky |
| | SeNTU: Sentiment Analysis of Tweets by Combining a Rule-based Classifier with Supervised Learning
Prerna Chikersal, Soujanya Poria and Erik Cambria |
| | INESC-ID: Sentiment Analysis without Hand-Coded Features or Linguistic Resources using Embedding Subspaces
Ramón Astudillo, Silvio Amir, Wang Ling, Bruno Martins, Mario J. Silva and Isabel Trancoso |
| | WarwickDCS: From Phrase-Based to Target-Specific Sentiment Recognition
Richard Townsend, Adam Tsakalidis, Yiwei Zhou, Bo Wang, Maria Liakata, Arkaitz Zubiaga, Alexandra Cristea and Rob Procter |
| | UIR-PKU: Twitter-OpinMiner System for Sentiment Analysis in Twitter at SemEval 2015
Xu Han, Binyang Li, Jing Ma, Yuxiao Zhang, Gaoyan Ou, Tengjiao Wang and Kam-fai Wong |
| | SWAT-CMW: Classification of Twitter Emotional Polarity using a Multiple-Classifier Decision Schema and Enhanced Emotion Tagging
Riley Collins, Daniel May, Noah Weinthal and Richard Wicentowski |
| | LLT-PolyU: Identifying Sentiment Intensity in Ironic Tweets
Hongzhi Xu, Enrico Santus, Anna Laszlo and Chu-Ren Huang |
| | KELabTeam: A Statistical Approach on Figurative Language Sentiment Analysis in Twitter
Hoang Long Nguyen, Trung Duc Nguyen, Dosam Hwang and Jason J. Jung |
| | LT3: Sentiment Analysis of Figurative Tweets: piece of cake #NotReally
Cynthia Van Hee, Els Lefever and Veronique Hoste |
| | PRHLT: Combination of Deep Autoencoders with Classification and Regression Techniques for SemEval-2015 Task 11
Parth Gupta and Jon Ander Gómez |
| | ValenTo: Sentiment Analysis of Figurative Language Tweets with Irony and Sarcasm
Delia Irazú Hernández Farías, Emilio Sulis, Viviana Patti, Giancarlo Ruffo and Cristina Bosco |
| | CPH: Sentiment analysis of Figurative Language on Twitter #easypeasy #not
Sarah McGillion, Héctor Martínez Alonso and Barbara Plank |
| | UPF-taln: SemEval 2015 Tasks 10 and 11. Sentiment Analysis of Literal and Figurative Language in Twitter
Francesco Barbieri, Francesco Ronzano and Horacio Saggion |
| | DsUniPi: An SVM-based Approach for Sentiment Analysis of Figurative Language on Twitter
Maria Karanasou, Christos Doulkeridis and Maria Halkidi |
| | V3: Unsupervised Aspect Based Sentiment Analysis for SemEval2015 Task 12
Aitor García Pablos, Montse Cuadros and German Rigau |
| | LT3: Applying Hybrid Terminology Extraction to Aspect-Based Sentiment Analysis
Orphee De Clercq, Marjan Van de Kauter, Els Lefever and Veronique Hoste |
| | UFRGS: Identifying Categories and Targets in Customer Reviews
Anderson Kauer and Viviane Moreira |
| | SINAI: Syntactic Approach for Aspect-Based Sentiment Analysis
Salud M. Jiménez-Zafra, Eugenio Martínez-Cámara, M. Teresa Martín-Valdivia and L. Alfonso Ureña López |
| | ECNU: Extracting Effective Features from Multiple Sequential Sentences for Target-dependent Sentiment Analysis in Reviews
Zhihua Zhang and Man Lan |
| | UMDuluth-CS8761-12: A Novel Machine Learning Approach for Aspect Based Sentiment Analysis
Ravikanth Repaka, Ranga Reddy Pallelra, Akshay Reddy Koppula and Venkata Subhash Movva |
| | EliXa: A Modular and Flexible ABSA Platform
Iñaki San Vicente, Xabier Saralegi and Rodrigo Agerri |
| | Lsislif: CRF and Logistic Regression for Opinion Target Extraction and Sentiment Polarity Analysis
Hussam Hamdan, Patrice Bellot and Frederic Bechet |
| | SIEL: Aspect Based Sentiment Analysis in Reviews
Satarupa Guha, Aditya Joshi and Vasudeva Varma |
| | Sentiue: Target and Aspect based Sentiment Analysis in SemEval-2015 Task 12
José Saias |
| | TJUdeM: A Combination Classifier for Aspect Category Detection and Sentiment Polarity Classification
Zhifei Zhang, Jian-Yun Nie and Hongling Wang |
Friday, June 5, 2015 |
| | Track II - Time and Space (Part 1)
(chair: Georgeta Bordea) |
| 09:00–09:15 | SemEval-2015 Task 4: TimeLine: Cross-Document Event Ordering
Anne-Lyse Minard, Manuela Speranza, Eneko Agirre, Itziar Aldabe, Marieke van Erp, Bernardo Magnini, German Rigau and Ruben Urizar |
| 09:15–09:25 | SPINOZA_VU: An NLP Pipeline for Cross Document TimeLines
Tommaso Caselli, Antske Fokkens, Roser Morante and Piek Vossen |
| 09:25–09:40 | SemEval-2015 Task 5: QA TempEval - Evaluating Temporal Information Understanding with Question Answering
Hector Llorens, Nathanael Chambers, Naushad UzZaman, Nasrin Mostafazadeh, James Allen and James Pustejovsky |
| 09:40–09:50 | HLT-FBK: a Complete Temporal Processing System for QA TempEval
Paramita Mirza and Anne-Lyse Minard |
| 09:50–10:05 | SemEval-2015 Task 6: Clinical TempEval
Steven Bethard, Leon Derczynski, Guergana Savova, James Pustejovsky and Marc Verhagen |
| 10:05–10:15 | BluLab: Temporal Information Extraction for the 2015 Clinical TempEval Challenge
Sumithra Velupillai, Danielle L Mowery, Samir Abdelrahman, Lee Christensen and Wendy Chapman |
| 10:15–11:00 | Poster Session: Tasks 4, 5, 6, 7, and 8 (Part 1) |
| | GPLSIUA: Combining Temporal Information and Topic Modeling for Cross-Document Event Ordering
Borja Navarro and Estela Saquete |
| | HeidelToul: A Baseline Approach for Cross-document Event Ordering
Bilel Moulahi, Jannik Strötgen, Michael Gertz and Lynda Tamine |
| | HITSZ-ICRC: An Integration Approach for QA TempEval Challenge
Yongshuai Hou, Cong Tan, Qingcai Chen and Xiaolong Wang |
| | UFPRSheffield: Contrasting Rule-based and Support Vector Machine Approaches to Time Expression Identification in Clinical TempEval
Hegler Tissot, Genevieve Gorrell, Angus Roberts, Leon Derczynski and Marcos Didonet Del Fabro |
| | IXAGroupEHUDiac: A Multiple Approach System towards the Diachronic Evaluation of Texts
Haritz Salaberri, Iker Salaberri, Olatz Arregi and Beñat Zapirain |
| | USAAR-CHRONOS: Crawling the Web for Temporal Annotations
Liling Tan and Noam Ordan |
| | AMBRA: A Ranking Approach to Temporal Text Classification
Marcos Zampieri, Alina Maria Ciobanu, Vlad Niculae and Liviu P. Dinu |
| | IXAGroupEHUSpaceEval: (X-Space) A WordNet-based approach towards the Automatic Recognition of Spatial Information following the ISO-Space Annotation Scheme
Haritz Salaberri, Olatz Arregi and Beñat Zapirain |
| | UTD: Ensemble-Based Spatial Relation Extraction
Jennifer D’Souza and Vincent Ng |
| 10:30–11:00 | Coffee Break and Poster Session |
| | Track II - Time and Space (Part 2)
(chair: Liling Tan) |
| 11:00–11:15 | SemEval 2015, Task 7: Diachronic Text Evaluation
Octavian Popescu and Carlo Strapparava |
| 11:15–11:25 | UCD : Diachronic Text Classification with Character, Word, and Syntactic N-grams
Terrence Szymanski and Gerard Lynch |
| 11:25–11:40 | SemEval-2015 Task 8: SpaceEval
James Pustejovsky, Parisa Kordjamshidi, Marie-Francine Moens, Aaron Levine, Seth Dworman and Zachary Yocum |
| 11:40–11:50 | SpRL-CWW: Spatial Relation Classification with Independent Multi-class Models
Eric Nichols and Fadi Botros |
| 11:50–12:30 | Poster Session: Tasks 4, 5, 6, 7, and 8 (Part 2) |
| 12:30–14:00 | Lunch Break |
| | Track V - Learning Semantic Relations
(chair: Sameer Pradhan) |
| 14:00–14:15 | SemEval-2015 Task 17: Taxonomy Extraction Evaluation (TExEval)
Georgeta Bordea, Paul Buitelaar, Stefano Faralli and Roberto Navigli |
| 14:15–14:25 | INRIASAC: Simple Hypernym Extraction Methods
Gregory Grefenstette |
| 14:25–14:40 | SemEval 2015 Task 18: Broad-Coverage Semantic Dependency Parsing
Stephan Oepen, Marco Kuhlmann, Yusuke Miyao, Daniel Zeman, Silvie Cinkova, Dan Flickinger, Jan Hajic and Zdenka Uresova |
| 14:40–14:50 | Peking: Building Semantic Dependency Graphs with a Hybrid Parser
Yantao Du, Fan Zhang, Xun Zhang, Weiwei Sun and Xiaojun Wan |
| 14:50–16:00 | Poster Session: Tasks 17 and 18 |
| | USAAR-WLV: Hypernym Generation with Deep Neural Nets
Liling Tan, Rohit Gupta and Josef van Genabith |
| | NTNU: An Unsupervised Knowledge Approach for Taxonomy Extraction
Bamfa Ceesay and Wen Juan Hou |
| | LT3: A Multi-modular Approach to Automatic Taxonomy Construction
Els Lefever |
| | TALN-UPF: Taxonomy Learning Exploiting CRF-Based Hypernym Extraction on Encyclopedic Definitions
Luis Espinosa Anke, Horacio Saggion and Francesco Ronzano |
| | QASSIT: A Pretopological Framework for the Automatic Construction of Lexical Taxonomies from Raw Texts
Guillaume Cleuziou, Davide Buscaldi, Gaël Dias, Vincent Levorato and Christine Largeron |
| | Riga: from FrameNet to Semantic Frames with C6.0 Rules
Guntis Barzdins, Peteris Paikens and Didzis Gosko |
| | Turku: Semantic Dependency Parsing as a Sequence Classification
Jenna Kanerva, Juhani Luotolahti and Filip Ginter |
| | Lisbon: Evaluating TurboSemanticParser on Multiple Languages and Out-of-Domain Data
Mariana S. C. Almeida and André F. T. Martins |
| 15:30–16:00 | Coffee Break and Poster Session |
| 16:00–16:40 | SemEval-2016 Task Announcements |
| 16:40–17:40 | Closing Session (statistics, polls, questions) |