Paper 1: Understanding Human Language: Can NLP and Deep Learning Help?
Paper 2: Evaluating the word-expert approach for Named-Entity Disambiguation.
Paper 3: A Fast Unified Model for Parsing and Sentence Understanding.
Paper 4: Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models.
Paper 5: Improving Coreference Resolution by Learning Entity-Level Distributed Representations.
Paper 6: Learning Language Games through Interaction.
Paper 7: A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task.
Paper 8: Compression of Neural Machine Translation Models via Pruning.
Paper 9: Natural language translation at the intersection of AI and HCI.
Paper 10: Computational Linguistics and Deep Learning.
Paper 11: Natural Language Translation at the Intersection of AI and HCI.
Paper 12: Text to 3D Scene Generation with Rich Lexical Grounding.
Paper 13: Leveraging Linguistic Structure For Open Domain Information Extraction.
Paper 14: Robust Subgraph Generation Improves Abstract Meaning Representation Parsing.
Paper 15: Entity-Centric Coreference Resolution with Model Stacking.
Paper 16: Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks.
Paper 17: Deep Neural Language Models for Machine Translation.
Paper 18: Forum77: An Analysis of an Online Health Forum Dedicated to Addiction Recovery.
Paper 19: A large annotated corpus for learning natural language inference.
Paper 20: Effective Approaches to Attention-based Neural Machine Translation.
Paper 21: Distributed Representations of Words to Guide Bootstrapped Entity Classifiers.
Paper 22: Tree-Structured Composition in Neural Networks without Tree-Structured Architectures.
Paper 23: On-the-Job Learning with Bayesian Decision Theory.
Paper 24: Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks.
Paper 25: Text to 3D Scene Generation with Rich Lexical Grounding.
Paper 26: Robust Subgraph Generation Improves Abstract Meaning Representation Parsing.
Paper 27: On-the-Job Learning with Bayesian Decision Theory.
Paper 28: Tree-structured composition in neural networks without tree-structured architectures.
Paper 29: Effective Approaches to Attention-based Neural Machine Translation.
Paper 30: A large annotated corpus for learning natural language inference.
Paper 31: Research and applications: Induced lexico-syntactic patterns improve information extraction from online medical forums.
Paper 32: Cross-lingual Projected Expectation Regularization for Weakly Supervised Learning.
Paper 33: Grounded Compositional Semantics for Finding and Describing Images with Sentences.
Paper 34: The Stanford CoreNLP Natural Language Processing Toolkit.
Paper 35: Robust Logistic Regression using Shift Parameters.
Paper 36: Faster Phrase-Based Decoding by Refining Feature State.
Paper 37: Two Knives Cut Better Than One: Chinese Word Segmentation with Dual Decomposition.
Paper 38: Word Segmentation of Informal Arabic with Domain Adaptation.
Paper 39: TransPhoner: automated mnemonic keyword generation.
Paper 40: Improved Pattern Learning for Bootstrapped Entity Extraction.
Paper 41: NaturalLI: Natural Logic Inference for Common Sense Reasoning.
Paper 42: Human Effort and Machine Learnability in Computer Aided Translation.
Paper 43: Combining Distant and Partial Supervision for Relation Extraction.
Paper 44: Modeling Biological Processes for Reading Comprehension.
Paper 45: A Fast and Accurate Dependency Parser using Neural Networks.
Paper 46: Glove: Global Vectors for Word Representation.
Paper 47: Learning Spatial Knowledge for Text to 3D Scene Generation.
Paper 48: A Gold Standard Dependency Corpus for English.
Paper 49: Event Extraction Using Distant Supervision.
Paper 50: Universal Stanford dependencies: A cross-linguistic typology.
Paper 51: Global Belief Recursive Neural Networks.
Paper 52: Simple MAP Inference via Low-Rank Relaxations.
Paper 53: Learning Distributed Representations for Structured Output Prediction.
Paper 54: On being the right scale: sizing large collections of 3D models.
Paper 55: Predictive translation memory: a mixed-initiative system for human language translation.
Paper 56: Recursive Neural Networks for Learning Logical Semantics.
Paper 57: Learning Distributed Word Representations for Natural Logic Reasoning.
Paper 58: Parsing Models for Identifying Multiword Expressions.
Paper 59: Effective Bilingual Constraints for Semi-Supervised Learning of Named Entity Recognizers.
Paper 60: Fast and Adaptive Online Training of Feature-Rich Translation Models.
Paper 61: Parsing with Compositional Vector Grammars.
Paper 62: Joint Word Alignment and Bilingual Named Entity Recognition Using Dual Decomposition.
Paper 63: The efficacy of human post-editing for language translation.
Paper 64: Better Word Representations with Recursive Neural Networks for Morphology.
Paper 65: Philosophers are Mortal: Inferring the Truth of Unseen Facts.
Paper 66: Feature Noising for Log-Linear Structured Prediction.
Paper 67: Bilingual Word Embeddings for Phrase-Based Machine Translation.
Paper 68: Fast dropout training.
Paper 69: Topic Model Diagnostics: Assessing Domain Relevance via Topical Alignment.
Paper 70: Learning a Product of Experts with Elitist Lasso.
Paper 71: Effect of Non-linear Deep Architecture in Sequence Labeling.
Paper 72: Deep Learning for NLP (without Magic).
Paper 73: Named Entity Recognition with Bilingual Constraints.
Paper 74: Reasoning With Neural Tensor Networks for Knowledge Base Completion.
Paper 75: Zero-Shot Learning Through Cross-Modal Transfer.
Paper 76: Stanford's 2013 KBP System.
Paper 77: Learning New Facts From Knowledge Bases With Neural Tensor Networks and Semantic Word Vectors.
Paper 78: Zero-Shot Learning Through Cross-Modal Transfer.
Paper 79: Robust Logistic Regression using Shift Parameters.
Paper 80: Cross-lingual Pseudo-Projected Expectation Regularization for Weakly Supervised Learning.
Paper 81: Relaxations for inference in restricted Boltzmann machines.
Paper 82: Combining joint models for biomedical event extraction.
Paper 83: Did It Happen? The Pragmatic Complexity of Veridicality Assessment.
Paper 84: "Without the clutter of unimportant words": Descriptive keyphrases for text visualization.
Paper 85: Deep Learning for NLP (without Magic).
Paper 86: Baselines and Bigrams: Simple, Good Sentiment and Topic Classification.
Paper 87: Improving Word Representations via Global Context and Multiple Word Prototypes.
Paper 88: Termite: visualization techniques for assessing textual topic models.
Paper 89: Interpretation and trust: designing model-driven visualizations for text analysis.
Paper 90: Short message communications: users, topics, and in-language processing.
Paper 91: Multi-instance Multi-label Learning for Relation Extraction.
Paper 92: Learning Constraints for Consistent Timeline Extraction.
Paper 93: Probabilistic Finite State Machines for Regression-based MT Evaluation.
Paper 94: Semantic Compositionality through Recursive Matrix-Vector Spaces.
Paper 95: SUTime: A library for recognizing and normalizing time expressions.
Paper 96: Entity Clustering Across Languages.
Paper 97: Parsing Time: Learning to Interpret Time Expressions.
Paper 98: Convolutional-Recursive Deep Learning for 3D Object Classification.
Paper 99: Event Extraction as Dependency Parsing.
Paper 100: Part-of-Speech Tagging from 97% to 100%: Is It Time for Some Linguistics?
Paper 101: Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions.
Paper 102: Multiword Expression Identification with Tree Substitution Grammars: A Parsing tour de force with French.
Paper 103: Risk analysis for intellectual property litigation.
Paper 104: Parsing Natural Scenes and Natural Language with Recursive Neural Networks.
Paper 105: Analyzing the Dynamics of Research by Extracting Key Aspects of Scientific Papers.
Paper 106: Partially labeled topic models for interpretable text mining.
Paper 107: Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection.
Paper 108: Veridicality and Utterance Understanding.
Paper 109: TopicFlow Model: Unsupervised Learning of Topic-specific Influences of Hyperlinked Documents.
Paper 110: Spectral Chinese Restaurant Processes: Nonparametric Clustering Based on Similarities.
Paper 111: Proceedings of the Fifteenth Conference on Computational Natural Language Learning, CoNLL 2011, Portland, Oregon, USA, June 23-24, 2011.
Paper 112: Stanford-UBC Entity Linking at TAC-KBP, Again.
Paper 113: Stanford's Distantly-Supervised Slot-Filling System.
Paper 114: Which words are hard to recognize? Prosodic, lexical, and disfluency factors that increase speech recognition error rates.
Paper 115: "Was It Good? It Was Provocative." Learning the Meaning of Scalar Adjectives.
Paper 116: Hierarchical Joint Learning: Improving Joint Parsing and Named Entity Recognition with Non-Jointly Labeled Data.
Paper 117: Better Arabic Parsing: Baselines, Evaluations, and Analysis.
Paper 118: Probabilistic Tree-Edit Models with Structured Latent Variables for Textual Entailment and Question Answering.
Paper 119: Viterbi Training Improves Unsupervised Dependency Parsing.
Paper 120: A Multi-Pass Sieve for Coreference Resolution.
Paper 121: Parsing to Stanford Dependencies: Trade-offs between Speed and Accuracy.
Paper 122: Phrasal: A Statistical Machine Translation Toolkit for Exploring New Model Features.
Paper 123: Subword Variation in Text Message Classification.
Paper 124: The Best Lexical Metric for Phrase-Based Statistical MT System Optimization.
Paper 125: Ensemble Models for Dependency Parsing: Cheap and Good?
Paper 126: Improved Models of Distortion Cost for Statistical Machine Translation.
Paper 127: Accurate Non-Hierarchical Phrase-Based Translation.
Paper 128: Stanford-UBC Entity Linking at TAC-KBP.
Paper 129: A Simple Distant Supervision Approach for the TAC-KBP Slot Filling Task.
Paper 130: Measuring machine translation quality as semantic equivalence: A metric based on entailment features.
Paper 131: Robust Machine Translation Evaluation with Entailment Features.
Paper 132: Quadratic-Time Dependency Parsing for Machine Translation.
Paper 133: Nested Named Entity Recognition.
Paper 134: Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora.
Paper 135: Joint Parsing and Named Entity Recognition.
Paper 136: Hierarchical Bayesian Domain Adaptation.
Paper 137: Random Walks for Text Semantic Similarity.
Paper 138: WikiWalk: Random walks on Wikipedia for Semantic Relatedness.
Paper 139: Clustering the tagged web.
Paper 140: Stanford-UBC at TAC-KBP.
Paper 141: Introduction to information retrieval.
Paper 142: A Global Joint Model for Semantic Role Labeling.
Paper 143: Enforcing Transitivity in Coreference Resolution.
Paper 144: Which Words Are Hard to Recognize? Prosodic, Lexical, and Disfluency Factors that Increase ASR Error Rates.
Paper 145: Efficient, Feature-based, Conditional Random Field Parsing.
Paper 146: Finding Contradictions in Text.
Paper 147: Modeling Semantic Containment and Exclusion in Natural Language Inference.
Paper 148: Studying the History of Ideas Using Topic Models.
Paper 149: Legal Docket Classification: Where Machine Learning Stumbles.
Paper 150: A Phrase-Based Alignment Model for Natural Language Inference.
Paper 151: A Simple and Effective Hierarchical Phrase Reordering Model.
Paper 152: Lexicon Schemas and Related Data Models: when Standards Meet Users.
Paper 153: Deciding Entailment and Contradiction with Stochastic and Edit Distance-based Alignment.
Paper 154: Robust Graph Alignment Methods for Textual Inference and Machine Reading.
Paper 155: The Infinite Tree.
Paper 156: Regularization, adaptation, and non-independent features improve hidden conditional random fields for phone classification.
Paper 157: An Effective Two-Stage Model for Exploiting Non-Local Dependencies in Named Entity Recognition.
Paper 158: Unsupervised Discovery of a Statistical Verb Lexicon.
Paper 159: Solving the Problem of Cascading Errors: Approximate Bayesian Inference for Linguistic Annotation Pipelines.
Paper 160: Learning to recognize features of valid textual entailments.
Paper 161: Graphical Model Representations of Word Lattices.
Paper 162: Exploring the boundaries: gene and protein identification in biomedical text.
Paper 163: Natural language grammar induction with a generative constituent-context model.
Paper 164: Robust Textual Inference Via Learning and Abductive Reasoning.
Paper 165: Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling.
Paper 166: Unsupervised Learning of Field Segmentation Models for Information Extraction.
Paper 167: Joint Learning Improves Semantic Role Labeling.
Paper 168: A Joint Model for Semantic Role Labeling.
Paper 169: Robust Textual Inference via Graph Matching.
Paper 170: Deep Dependencies from Context-Free Statistical Parsers: Correcting the Surface Dependency Approximation.
Paper 171: Corpus-Based Induction of Syntactic Structure: Models of Dependency and Constituency.
Paper 172: Language Learning: Beyond Thunderdome.
Paper 173: Using Feature Conjunctions Across Examples for Learning Pairwise Classifiers.
Paper 174: Max-Margin Parsing.
Paper 175: Verb Sense and Subcategorization: Using Joint Inference to Improve Performance on Complementary Task.
Paper 176: The Leaf Path Projection View of Parse Trees: Exploring String Kernels for HPSG Parse Selection.
Paper 177: Learning random walk models for inducing word dependency distributions.
Paper 178: Accurate Unlexicalized Parsing.
Paper 179: Is it Harder to Parse Chinese, or the Chinese Treebank?
Paper 180: Named Entity Recognition with Character-Level Models.
Paper 181: A Generative Model for Semantic Role Labeling.
Paper 182: Optimizing Local Probability Models for Statistical Parsing.
Paper 183: Spectral Learning.
Paper 184: Factored A* Search for Models over Sequences and Trees.
Paper 185: A* Parsing: Fast Exact Viterbi Parse Selection.
Paper 186: Optimization, Maxent Models, and Conditional Estimation without Magic.
Paper 187: Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network.
Paper 188: Log-Linear Models for Label Ranking.
Paper 189: Extrapolation methods for accelerating PageRank computations.
Paper 190: A Generative Constituent-Context Model for Improved Grammar Induction.
Paper 191: The LinGO Redwoods Treebank: Motivation and Preliminary Applications.
Paper 192: Feature Selection for a Rich HPSG Grammar Using Decision Trees.
Paper 193: Interpreting and Extending Classical Agglomerative Clustering Algorithms using a Model-Based approach.
Paper 194: From Instance-level Constraints to Space-Level Constraints: Making the Most of Prior Knowledge in Data Clustering.
Paper 195: Fast Exact Inference with a Factored Model for Natural Language Parsing.
Paper 196: Foundations of statistical natural language processing.
Paper 197: Kirrkirr: Software for Browsing and Visual Exploration of a Structured Warlpiri Dictionary.
Paper 198: Parsing with Treebank Grammars: Empirical Bounds, Theoretical Models, and the Structure of the Penn Treebank.
Paper 199: Distributional phrase structure induction.
Paper 200: Parsing and Hypergraphs.
Paper 201: Natural Language Grammar Induction Using a Constituent-Context Model.
Paper 202: What's related? Generalizing approaches to related articles in medicine.
Paper 203: The segmentation problem in morphology learning.
Paper 204: Probabilistic Parsing Using Left Corner Language Models.
Paper 205: Automatic Acquisition of a Large Subcategorization Dictionary from Corpora.