Distributed Representations of Sentences and Documents

Abstract

Many machine learning algorithms require the input to be represented as a fixed-length feature vector. When it comes to texts, one of the most common fixed-length features is bag-of-words. Despite their popularity, bag-of-words features have two major weaknesses: they lose the ordering of the words and they also ignore semantics of the words. For example, "powerful," "strong" and "Paris" are equally distant. In this paper, we propose Paragraph Vector, an unsupervised algorithm that learns fixed-length feature representations from variable-length pieces of texts, such as sentences, paragraphs, and documents. Our algorithm represents each document by a dense vector which is trained to predict words in the document. Its construction gives our algorithm the potential to overcome the weaknesses of bag-of-words models. Empirical results show that Paragraph Vectors outperform bag-of-words models as well as other techniques for text representations. Finally, we achieve new state-of-the-art results on several text classification and sentiment analysis tasks.

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• Distributed Representations of Words and Phrases and their Compositionality
• Natural Language Processing (almost) from Scratch
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• A Cross-lingual Natural Language Processing Framework for Infodemic Management
• PAUSE: Positive and Annealed Unlabeled Sentence Embedding
• Joint Management and Analysis of Textual Documents and Tabular Data within the AUDAL Data Lake
• A Unified Framework for Cross-Domain and Cross-System Recommendations
• Recurrent Neural Network from Adder's Perspective: Carry-lookahead RNN
• Estimating Fund-Raising Performance for Start-up Projects from a Market Graph Perspective
• A Gaze into the Internal Logic of Graph Neural Networks, with Logic
• Selecting Walk Schemes for Database Embedding
• Inductive Subgraph Embedding for Link Prediction
• WikiContradiction: Detecting Self-Contradiction Articles on Wikipedia
• Social Fraud Detection Review: Methods, Challenges and Analysis
• Two-level monotonic multistage recommender systems
• Expert Knowledge-Guided Length-Variant Hierarchical Label Generation for Proposal Classification
• Teaching a Massive Open Online Course on Natural Language Processing
• A Bi-Encoder LSTM Model For Learning Unstructured Dialogs
• Social media data reveals signal for public consumer perceptions
• Cosine Similarity of Multimodal Content Vectors for TV Programmes
• Learning from students' perception on professors through opinion mining
• A computational model implementing subjectivity with the 'Room Theory'. The case of detecting Emotion from Text
• Unsupervised Hierarchical Graph Representation Learning by Mutual Information Maximization
• Multimodal Prediction based on Graph Representations
• Text classification with pixel embedding
• Distributed representation of patients and its use for medical cost prediction
• Evaluation Benchmarks and Learning Criteria for Discourse-Aware Sentence Representations
• Bayes EMbedding (BEM): Refining Representation by Integrating Knowledge Graphs and Behavior-specific Networks
• CARL: Aggregated Search with Context-Aware Module Embedding Learning
• Not All Bugs Are the Same: Understanding, Characterizing, and Classifying the Root Cause of Bugs
• Learning Representations from Product Titles for Modeling Shopping Transactions
• ATM:Adversarial-neural Topic Model
• Segmentation-free Compositional $n$-gram Embedding
• Parameterized Neural Network Language Models for Information Retrieval
• Multi-view and Multi-source Transfers in Neural Topic Modeling with Pretrained Topic and Word Embeddings
• An Edit-centric Approach for Wikipedia Article Quality Assessment
• A Simple and Effective Approach for Fine Tuning Pre-trained Word Embeddings for Improved Text Classification
• Can x2vec Save Lives? Integrating Graph and Language Embeddings for Automatic Mental Health Classification
• Learning Outside the Box: Discourse-level Features Improve Metaphor Identification
• Dual Attention Model for Citation Recommendation
• Automated Discovery and Classification of Training Videos for Career Progression
• Predicting User Engagement Status for Online Evaluation of Intelligent Assistants
• Exploiting News Article Structure for Automatic Corpus Generation of Entailment Datasets
• Deep Learning Meets Projective Clustering
• Corruption Is Not All Bad: Incorporating Discourse Structure into Pre-training via Corruption for Essay Scoring
• Latte-Mix: Measuring Sentence Semantic Similarity with Latent Categorical Mixtures
• Seeker: Real-Time Interactive Search
• REMOD: Relation Extraction for Modeling Online Discourse
• Enhancing Interpretable Clauses Semantically using Pretrained Word Representation
• Holmes: An Efficient and Lightweight Semantic Based Anomalous Email Detector
• Audio Caption in a Car Setting with a Sentence-Level Loss
• A Study into patient similarity through representation learning from medical records
• Using Paragraph Vectors to improve our existing code review assisting tool-CRUSO
• Self-citation Analysis using Sentence Embeddings
• Graph Embedding via Diffusion-Wavelets-Based Node Feature Distribution Characterization
• MIC: Model-agnostic Integrated Cross-channel Recommenders
• TransAug: Translate as Augmentation for Sentence Embeddings
• Clustering Vietnamese Conversations From Facebook Page To Build Training Dataset For Chatbot
• XtraLibD: Detecting Irrelevant Third-Party libraries in Java and Python Applications
• Classifying Math KCs via Task-Adaptive Pre-Trained BERT
• Interpretable Structure-aware Document Encoders with Hierarchical Attention
• Parameter-Efficient Neural Question Answering Models via Graph-Enriched Document Representations
• Online Handbook of Argumentation for AI: Volume 2
• Seed Words Based Data Selection for Language Model Adaptation
• Graph Convolutional Network with Generalized Factorized Bilinear Aggregation
• Unsupervised Identification of Study Descriptors in Toxicology Research: An Experimental Study
• DiffQue: Estimating Relative Difficulty of Questions in Community Question Answering Services
• Latin writing styles analysis with Machine Learning: New approach to old questions
• SAFE: Self-Attentive Function Embeddings for Binary Similarity
• Unsupervised Keyphrase Extraction by Jointly Modeling Local and Global Context
• STRASS: A Light and Effective Method for Extractive Summarization Based on Sentence Embeddings
• When Product Search Meets Collaborative Filtering: A Hierarchical Heterogeneous Graph Neural Network Approach
• Towards Informative Tagging of Code Fragments to Support the Investigation of Code Clones
• Inferring Substitutable and Complementary Products with Knowledge-Aware Path Reasoning based on Dynamic Policy Network
• Perceived and Intended Sarcasm Detection with Graph Attention Networks
• A Robotic Dating Coaching System Leveraging Online Communities Posts
• Improving Clinical Outcome Predictions Using Convolution over Medical Entities with Multimodal Learning
• Fake News Data Collection and Classification: Iterative Query Selection for Opaque Search Engines with Pseudo Relevance Feedback
• What Makes a Star Teacher? A Hierarchical BERT Model for Evaluating Teacher's Performance in Online Education
• Feature Learning for Meta-Paths in Knowledge Graphs
• Multi-Channel Sequential Behavior Networks for User Modeling in Online Advertising
• Learning Embeddings of Directed Networks with Text-Associated Nodes---with Applications in Software Package Dependency Networks
• Educational Content Linking for Enhancing Learning Need Remediation in MOOCs
• Encouraging Paragraph Embeddings to Remember Sentence Identity Improves Classification
• Open-domain Topic Identification of Out-of-domain Utterances using Wikipedia
• Graph Classification Based on Skeleton and Component Features
• Metaknowledge Extraction Based on Multi-Modal Documents
• Wider Vision: Enriching Convolutional Neural Networks via Alignment to External Knowledge Bases
• The Analysis from Nonlinear Distance Metric to Kernel-based Drug Prescription Prediction System
• A Topological Approach to Compare Document Semantics Based on a New Variant of Syntactic N-grams
• Mining Program Properties From Neural Networks Trained on Source Code Embeddings
• Analyzing the Hidden Activations of Deep Policy Networks: Why Representation Matters
• Graph Convolutional Network for Swahili News Classification
• Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks
• Recommendation Chart of Domains for Cross-Domain Sentiment Analysis:Findings of A 20 Domain Study
• How Does That Sound? Multi-Language SpokenName2Vec Algorithm Using Speech Generation and Deep Learning
• Utilizing FastText for Venue Recommendation
• SentPWNet: A Unified Sentence Pair Weighting Network for Task-specific Sentence Embedding
• Hybrid Improved Document-level Embedding (HIDE)
• DrugDBEmbed : Semantic Queries on Relational Database using Supervised Column Encodings
• Security Apps under the Looking Glass: An Empirical Analysis of Android Security Apps
• Modeling Coherency in Generated Emails by Leveraging Deep Neural Learners
• Automatic Story Construction from News Articles in an Online Fashion
• Novel Approaches to Accelerating the Convergence Rate of Markov Decision Process for Search Result Diversification
• Contextual Document Similarity for Content-based Literature Recommender Systems
• Beyond Next Item Recommendation: Recommending and Evaluating List of Sequences
• Can questions summarize a corpus? Using question generation for characterizing COVID-19 research
• Modeling Dyadic Conversations for Personality Inference
• PaRe: A Paper-Reviewer Matching Approach Using a Common Topic Space
• Lifelong Neural Topic Learning in Contextualized Autoregressive Topic Models of Language via Informative Transfers
• Similarity Driven Approximation for Text Analytics
• Correlations between Word Vector Sets
• Hierarchical Semantic Correspondence Learning for Post-Discharge Patient Mortality Prediction
• Towards Successful Social Media Advertising: Predicting the Influence of Commercial Tweets
• A neural document language modeling framework for spoken document retrieval
• An End-to-End Framework for Cold Question Routing in Community Question Answering Services
• Document Network Embedding: Coping for Missing Content and Missing Links
• Estimating Early Fundraising Performance of Innovations via Graph-based Market Environment Model
• Comparison of Turkish Word Representations Trained on Different Morphological Forms
• Semantic Relatedness for Keyword Disambiguation: Exploiting Different Embeddings
• Learning Directly from Grammar Compressed Text
• Wikibook-Bot - Automatic Generation of a Wikipedia Book
• It's Only Words And Words Are All I Have
• Neural-Network Guided Expression Transformation
• SECTOR: A Neural Model for Coherent Topic Segmentation and Classification
• A New Approach for Topic Detection using Adaptive Neural Networks
• Document Similarity for Texts of Varying Lengths via Hidden Topics
• Hamming Sentence Embeddings for Information Retrieval
• Text Classification Components for Detecting Descriptions and Names of CAD models
• How Sequence-to-Sequence Models Perceive Language Styles?
• Twitter Sentiment on Affordable Care Act using Score Embedding
• Boosting the Rating Prediction with Click Data and Textual Contents
• Sequential Learning of Convolutional Features for Effective Text Classification