iamkissg
  • PaperHighlights
  • 2019
    • 03
      • Not All Contexts Are Created Equal Better Word Representations with Variable Attention
      • Learning Context-Sensitive Word Embeddings with Neural Tensor Skip-Gram Model
      • Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet
      • pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference
      • Contextual Word Representations: A Contextual Introduction
      • Not All Neural Embeddings are Born Equal
      • High-risk learning: acquiring new word vectors from tiny data
      • Learning word embeddings from dictionary definitions only
      • Dependency-Based Word Embeddings
    • 02
      • Improving Word Embedding Compositionality using Lexicographic Definitions
      • From Word Embeddings To Document Distances
      • Progressive Growing of GANs for Improved Quality, Stability, and Variation
      • Retrofitting Word Vectors to Semantic Lexicons
      • Bag of Tricks for Image Classification with Convolutional Neural Networks
      • Multi-Task Deep Neural Networks for Natural Language Understanding
      • Snapshot Ensembles: Train 1, get M for free
      • EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks
      • Counter-fitting Word Vectors to Linguistic Constraints
      • AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling
      • Learning semantic similarity in a continuous space
      • Progressive Neural Networks
      • BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
      • Language Models are Unsupervised Multitask Learners
    • 01
      • Querying Word Embeddings for Similarity and Relatedness
      • Data Distillation: Towards Omni-Supervised Learning
      • A Rank-Based Similarity Metric for Word Embeddings
      • Dict2vec: Learning Word Embeddings using Lexical Dictionaries
      • Graph Convolutional Networks for Text Classification
      • Improving Distributional Similarity with Lessons Learned from Word Embeddings
      • Real-time Personalization using Embeddings for Search Ranking at Airbnb
      • Glyce: Glyph-vectors for Chinese Character Representations
      • Auto-Encoding Dictionary Definitions into Consistent Word Embeddings
      • Distilling the Knowledge in a Neural Network
      • Uncovering divergent linguistic information in word embeddings with lessons for intrinsic and extrin
      • The (Too Many) Problems of Analogical Reasoning with Word Vectors
      • Linear Ensembles of Word Embedding Models
      • Intrinsic Evaluation of Word Vectors Fails to Predict Extrinsic Performance
      • Dynamic Meta-Embeddings for Improved Sentence Representations
  • 2018
    • 11
      • Think Globally, Embed Locally — Locally Linear Meta-embedding of Words
      • Learning linear transformations between counting-based and prediction-based word embeddings
      • Learning Word Meta-Embeddings by Autoencoding
      • Learning Word Meta-Embeddings
      • Frustratingly Easy Meta-Embedding – Computing Meta-Embeddings by Averaging Source Word Embeddings
    • 6
      • Universal Language Model Fine-tuning for Text Classification
      • Semi-supervised sequence tagging with bidirectional language models
      • Consensus Attention-based Neural Networks for Chinese Reading Comprehension
      • Attention-over-Attention Neural Networks for Reading Comprehension
      • Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
      • Convolutional Neural Networks for Sentence Classification
      • Deep contextualized word representations
      • Neural Architectures for Named Entity Recognition
      • Improving Language Understanding by Generative Pre-Training
      • A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence C
      • Teaching Machines to Read and Comprehend
    • 5
      • Text Understanding with the Attention Sum Reader Network
      • Effective Approaches to Attention-based Neural Machine Translation
      • Distance-based Self-Attention Network for Natural Language Inference
      • Deep Residual Learning for Image Recognition
      • U-Net: Convolutional Networks for Biomedical Image Segmentation
      • Memory Networks
      • Neural Machine Translation by Jointly Learning to Align and Translate
      • Convolutional Sequence to Sequence Learning
      • An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
      • Graph Attention Networks
      • Attention is All You Need
      • DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding
      • A Structured Self-attentive Sentence Embedding
      • Hierarchical Attention Networks for Document Classification
      • Grammar as a Foreign Language
      • Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
      • Transforming Auto-encoders
      • Self-Attention with Relative Position Representations
    • 1
      • 20180108-20180114
  • 2017
    • 12
      • 20171218-2017124 论文笔记
    • 11
      • 20171127-20171203 论文笔记 1
      • 20171106-20171126 论文笔记
      • 20171030-20171105 论文笔记 1
  • Paper Title as Note Title
Powered by GitBook
On this page
  • 要点
  • 备注

Was this helpful?

  1. 2019
  2. 01

Dict2vec: Learning Word Embeddings using Lexical Dictionaries

PreviousA Rank-Based Similarity Metric for Word EmbeddingsNextGraph Convolutional Networks for Text Classification

Last updated 5 years ago

Was this helpful?

论文地址:

要点

本文改进了 word2vec 算法, 方法上有点像 Airbnb 对 skip-gram 的改造: 在 sampling 上做文章.

对 word embeddings 的改进, 一些方法在训练完毕之后, 使用 WordNet 等数据包含的语义信息来进行 post-processing, 本文则直接对学习过程进行了改造, 而且用的是从在线词典(牛津/剑桥/柯林斯/美国在线词典)爬取的定义数据.

对于词典类数据的使用, 假设或者说共识都是, 它们包含了丰富的语义信息(semantic information). 本文也不外如是, 通过对词典的利用, 在无监督地学习过程中, 添加了一些学习规则, 起到一些监督的作用. 具体而言, 文章将互相出现在对方定义中的单词对称为 strong pairs, 将只有一方出现在另一方定义中的单词对称为 weak pairs, 并为它们分配了不同的权值(作为模型的超参数).

Strong pairs 与 weak pairs 控制着 sampling 过程: 每次都从 target word 的 strong pairs 和 weak pairs 中采样作为 postive samples, 而进行 negative sampling 时, 避免从以上两个集合中采样. 该过程, 强化了相似/相关单词间的联系, 也避免了随机 negative sampling 的误采样(和 Airbnb 将 booked listing 作为 global context, 增加同一市场的 negative samples 等等的做法, 有异曲同工之妙).

根据以上描述, 显然, 目标函数由 3 部分组成, 第一部分是 target word 与 context words, 第二部分是 postive samples, 第三部分是 negative samples. 各司其职.

文章的实验发现用在线词典的效果居然比 WordNet 还要更好一些, 比起不用 strong/weak pairs 要好得更多. Dict2vec 在训练时间上, 是 word2vec 的约 1/4, 是 fasttext 的约 1/3, 这让人挺惊讶的.

备注

本文提供的爬虫是一个很方便的工具.

http://aclweb.org/anthology/D17-1024