coling 2014 论文列表
Proceedings of the 8th International Workshop on Semantic Evaluation, SemEval@COLING 2014, Dublin, Ireland, August 23-24, 2014.
|
XRCE: Hybrid Classification for Aspect-based Sentiment Analysis.
V3: Unsupervised Generation of Domain Aspect Terms for Aspect Based Sentiment Analysis.
UWM: Disorder Mention Extraction from Clinical Text Using CRFs and Normalization Using Learned Edit Distance Patterns.
UWM: Applying an Existing Trainable Semantic Parser to Parse Robotic Spatial Commands.
UWB: Machine Learning Approach to Aspect-Based Sentiment Analysis.
UW-MRS: Leveraging a Deep Grammar for Robotic Spatial Commands.
UTU: Disease Mention Recognition and Normalization with CRFs and Vector Space Representations.
UTH_CCB: A report for SemEval 2014 - Task 7 Analysis of Clinical Text.
UTexas: Natural Language Semantics using Distributional Semantics and Probabilistic Logic.
USF: Chunking for Aspect-term Identification & Polarity Classification.
UoW: NLP techniques developed at the University of Wolverhampton for Semantic Similarity and Textual Entailment.
UoW: Multi-task Learning Gaussian Process for Semantic Textual Similarity.
UO_UA: Using Latent Semantic Analysis to Build a Domain-Dependent Sentiment Resource.
University_of_Warwick: SENTIADAPTRON - A Domain Adaptable Sentiment Analyser for Tweets - Meets SemEval.
UNITOR: Aspect Based Sentiment Analysis with Structured Learning.
UniPi: Recognition of Mentions of Disorders in Clinical Text.
UNIBA: Combining Distributional Semantic Models and Word Sense Disambiguation for Textual Similarity.
UNAL-NLP: Cross-Lingual Phrase Sense Disambiguation with Syntactic Dependency Trees.
UNAL-NLP: Combining Soft Cardinality Features for Semantic Textual Similarity, Relatedness and Entailment.
UMCC_DLSI: Sentiment Analysis in Twitter using Polirity Lexicons and Tweet Similarity.
UMCC_DLSI: A Probabilistic Automata for Aspect Based Sentiment Analysis.
UMCC_DLSI_SemSim: Multilingual System for Measuring Semantic Textual Similarity.
ULisboa: Identification and Classification of Medical Concepts.
UKPDIPF: Lexical Semantic Approach to Sentiment Polarity Prediction in Twitter Data.
UIO-Lien: Entailment Recognition using Minimal Recursion Semantics.
ÚFAL: Using Hand-crafted Rules in Aspect Based Sentiment Analysis on Parsed Data.
UEdin: Translating L1 Phrases in L2 Context using Context-Sensitive SMT.
UBham: Lexical Resources and Dependency Parsing for Aspect-Based Sentiment Analysis.
Turku: Broad-Coverage Semantic Parsing with Rich Features.
TUGAS: Exploiting unlabelled data for Twitter sentiment analysis.
tucSage: Grammar Rule Induction for Spoken Dialogue Systems via Probabilistic Candidate Selection.
TMUNSW: Disorder Concept Recognition and Normalization in Clinical Notes for SemEval-2014 Task 7.
TJP: Identifying the Polarity of Tweets from Contexts.
ThinkMiners: Disorder Recognition using Conditional Random Fields and Distributional Semantics.
Think Positive: Towards Twitter Sentiment Analysis from Scratch.
The Meaning Factory: Formal Semantics for Recognizing Textual Entailment and Determining Semantic Similarity.
The Impact of Z_score on Twitter Sentiment Analysis.
TeamZ: Measuring Semantic Textual Similarity for Spanish Using an Overlap-Based Approach.
TeamX: A Sentiment Analyzer with Enhanced Lexicon Mapping and Weighting Scheme for Unbalanced Data.
Team Z: Wiktionary as a L2 Writing Assistant.
TCDSCSS: Dimensionality Reduction to Evaluate Texts of Varying Lengths - an IR Approach.
SZTE-NLP: Clinical Text Analysis with Named Entity Recognition.
SZTE-NLP: Aspect level opinion mining exploiting syntactic cues.
Synalp-Empathic: A Valence Shifting Hybrid System for Sentiment Analysis.
Swiss-Chocolate: Sentiment Detection using Sparse SVMs and Part-Of-Speech n-Grams.
Supervised Methods for Aspect-Based Sentiment Analysis.
SU-FMI: System Description for SemEval-2014 Task 9 on Sentiment Analysis in Twitter.
SSMT: A Machine Translation Evaluation View To Paragraph-to-Sentence Semantic Similarity.
SNAP: A Multi-Stage XML-Pipeline for Aspect Based Sentiment Analysis.
SINAI: Voting System for Twitter Sentiment Analysis.
SINAI: Voting System for Aspect Based Sentiment Analysis.
SimCompass: Using Deep Learning Word Embeddings to Assess Cross-level Similarity.
ShrdLite: Semantic Parsing Using a Handmade Grammar.
SentiKLUE: Updating a Polarity Classifier in 48 Hours.
Senti.ue: Tweet Overall Sentiment Classification Approach for SemEval-2014 Task 9.
Sensible: L2 Translation Assistance by Emulating the Manual Post-Editing Process.
SemantiKLUE: Robust Semantic Similarity at Multiple Levels Using Maximum Weight Matching.
SeemGo: Conditional Random Fields Labeling and Maximum Entropy Classification for Aspect Based Sentiment Analysis.
SAP-RI: Twitter Sentiment Analysis in Two Days.
SAP-RI: A Constrained and Supervised Approach for Aspect-Based Sentiment Analysis.
SAIL: Sentiment Analysis using Semantic Similarity and Contrast Features.
SAIL-GRS: Grammar Induction for Spoken Dialogue Systems using CF-IRF Rule Similarity.
SA-UZH: Verb-based Sentiment Analysis.
RTRGO: Enhancing the GU-MLT-LT System for Sentiment Analysis of Short Messages.
RTM-DCU: Referential Translation Machines for Semantic Similarity.
RoBox: CCG with Structured Perceptron for Supervised Semantic Parsing of Robotic Spatial Commands.
RelAgent: Entity Detection and Normalization for Diseases in Clinical Records: a Linguistically Driven Approach.
Priberam: A Turbo Semantic Parser with Second Order Features.
Potsdam: Semantic Dependency Parsing by Bidirectional Graph-Tree Transformations and Syntactic Parsing.
Peking: Profiling Syntactic Tree Parsing Techniques for Semantic Graph Parsing.
OPI: Semeval-2014 Task 3 System Description.
NTNU: Measuring Semantic Similarity with Sublexical Feature Representations and Soft Cardinality.
NRC-Canada-2014: Recent Improvements in the Sentiment Analysis of Tweets.
NRC-Canada-2014: Detecting Aspects and Sentiment in Customer Reviews.
NILC_USP: Aspect Extraction using Semantic Labels.
NILC_USP: An Improved Hybrid System for Sentiment Analysis in Twitter Messages.
MindLab-UNAL: Comparing Metamap and T-mapper for Medical Concept Extraction in SemEval 2014 Task 7.
Meerkat Mafia: Multilingual and Cross-Level Semantic Textual Similarity Systems.
LyS: Porting a Twitter Sentiment Analysis Approach from Spanish to English.
LT3: Sentiment Classification in User-Generated Content Using a Rich Feature Set.
LIPN: Introducing a new Geographical Context Similarity Measure and a Statistical Similarity Measure based on the Bhattacharyya coefficient.
Linköping: Cubic-Time Graph Parsing with a Simple Scoring Scheme.
KUNLPLab: Sentiment Analysis on Twitter Data.
KUL-Eval: A Combinatory Categorial Grammar Approach for Improving Semantic Parsing of Robot Commands using Spatial Context.
Kea: Sentiment Analysis of Phrases Within Short Texts.
JU-Evora: A Graph Based Cross-Level Semantic Similarity Analysis using Discourse Information.
JU_CSE: A Conditional Random Field (CRF) Based Approach to Aspect Based Sentiment Analysis.
JOINT_FORCES: Unite Competing Sentiment Classifiers with Random Forest.
IxaMed: Applying Freeling and a Perceptron Sequential Tagger at the Shared Task on Analyzing Clinical Texts.
IUCL: Combining Information Sources for SemEval Task 5.
iTac: Aspect Based Sentiment Analysis using Sentiment Trees and Dictionaries.
INSIGHT Galway: Syntactic and Lexical Features for Aspect Based Sentiment Analysis.
Indian Institute of Technology-Patna: Sentiment Analysis in Twitter.
In-House: An Ensemble of Pre-Existing Off-the-Shelf Parsers.
Illinois-LH: A Denotational and Distributional Approach to Semantics.
IITPatna: Supervised Approach for Sentiment Analysis in Twitter.
IITP: Supervised Machine Learning for Aspect based Sentiment Analysis.
IITP: A Supervised Approach for Disorder Mention Detection and Disambiguation.
IHS R&D Belarus: Cross-domain extraction of product features using CRF.
HulTech: A General Purpose System for Cross-Level Semantic Similarity based on Anchor Web Counts.
haLF: Comparing a Pure CDSM Approach with a Standard Machine Learning System for RTE.
GPLSI: Supervised Sentiment Analysis in Twitter using Skipgrams.
FBK-TR: SVM for Semantic Relatedeness and Corpus Patterns for RTE.
FBK-TR: Applying SVM with Multiple Linguistic Features for Cross-Level Semantic Similarity.
ezDI: A Hybrid CRF and SVM based Model for Detecting and Encoding Disorder Mentions in Clinical Notes.
ECNU: One Stone Two Birds: Ensemble of Heterogenous Measures for Semantic Relatedness and Textual Entailment.
ECNU: Leveraging on Ensemble of Heterogeneous Features and Information Enrichment for Cross Level Semantic Similarity Estimation.
ECNU: Expression- and Message-level Sentiment Orientation Classification in Twitter Using Multiple Effective Features.
ECNU: A Combination Method and Multiple Features for Aspect Extraction and Sentiment Polarity Classification.
Duluth : Measuring Cross-Level Semantic Similarity with First and Second Order Dictionary Overlaps.
DLS$@$CU: Sentence Similarity from Word Alignment.
DLIREC: Aspect Term Extraction and Term Polarity Classification System.
DIT: Summarisation and Semantic Expansion in Evaluating Semantic Similarity.
DCU: Aspect-based Polarity Classification for SemEval Task 4.
DAEDALUS at SemEval-2014 Task 9: Comparing Approaches for Sentiment Analysis in Twitter.
Copenhagen-Malmö: Tree Approximations of Semantic Parsing Problems.
Coooolll: A Deep Learning System for Twitter Sentiment Classification.
COMMIT-P1WP3: A Co-occurrence Based Approach to Aspect-Level Sentiment Analysis.
Columbia NLP: Sentiment Detection of Sentences and Subjective Phrases in Social Media.
CNRC-TMT: Second Language Writing Assistant System Description.
CMUQ$@$Qatar: Using Rich Lexical Features for Sentiment Analysis on Twitter.
CMUQ-Hybrid: Sentiment Classification By Feature Engineering and Parameter Tuning.
CMU: Arc-Factored, Discriminative Semantic Dependency Parsing.
Citius: A Naive-Bayes Strategy for Sentiment Analysis on English Tweets.
CISUC-KIS: Tackling Message Polarity Classification with a Large and Diverse Set of Features.
CECL: a New Baseline and a Non-Compositional Approach for the Sick Benchmark.
BUAP: Polarity Classification of Short Texts.
BUAP: Evaluating Features for Multilingual and Cross-Level Semantic Textual Similarity.
BUAP: Evaluating Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual Entailment.
Blinov: Distributed Representations of Words for Aspect-Based Sentiment Analysis at SemEval 2014.
BioinformaticsUA: Concept Recognition in Clinical Narratives Using a Modular and Highly Efficient Text Processing Framework.
Biocom Usp: Tweet Sentiment Analysis with Adaptive Boosting Ensemble.
Biocom_Usp: Tweet Sentiment Analysis with Adaptive Boosting Ensemble.
Bielefeld SC: Orthonormal Topic Modelling for Grammar Induction.
AUEB: Two Stage Sentiment Analysis of Social Network Messages.
AT&T: The Tag&Parse Approach to Semantic Parsing of Robot Spatial Commands.
ASAP: Automatic Semantic Alignment for Phrases.
Alpage: Transition-based Semantic Graph Parsing with Syntactic Features.
AI-KU: Using Co-Occurrence Modeling for Semantic Similarity.
SemEval-2014 Task 10: Multilingual Semantic Textual Similarity.
SemEval-2014 Task 9: Sentiment Analysis in Twitter.
SemEval 2014 Task 8: Broad-Coverage Semantic Dependency Parsing.
SemEval-2014 Task 7: Analysis of Clinical Text.
SemEval-2014 Task 6: Supervised Semantic Parsing of Robotic Spatial Commands.
SemEval 2014 Task 5 - L2 Writing Assistant.
SemEval-2014 Task 4: Aspect Based Sentiment Analysis.
SemEval-2014 Task 3: Cross-Level Semantic Similarity.
SemEval-2014 Task 2: Grammar Induction for Spoken Dialogue Systems.
SemEval-2014 Task 1: Evaluation of Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual Entailment.
