Search Results for author:
Found 35 papers, 15 papers with code
Learning and Evaluating Character Representations in Novels
We address the problem of learning fixed-length vector representations of characters in novels.
Does it pay to optimize AUC?
To better understand the value of optimizing for AUC, we present an efficient algorithm, namely AUC-opt, to find the provably optimal AUC linear classifier in $\mathbb{R}^2$, which runs in $\mathcal{O}(n_+ n_- \log (n_+ n_-))$ where $n_+$ and $n_-$ are the number of positive and negative samples respectively.
Provable Fairness for Neural Network Models using Formal Verification
Machine learning models are increasingly deployed for critical decision-making tasks, making it important to verify that they do not contain gender or racial biases picked up from training data.
Hierarchies over Vector Space: Orienting Word and Graph Embeddings
Word and graph embeddings are widely used in deep learning applications.
Why do embedding spaces look as they do?
The power of embedding representations is a curious phenomenon.
What time is it? Temporal Analysis of Novels
Recognizing the flow of time in a story is a crucial aspect of understanding it.
Chapter Captor: Text Segmentation in Novels
Books are typically segmented into chapters and sections, representing coherent subnarratives and topics.
Online AUC Optimization for Sparse High-Dimensional Datasets
The Area Under the ROC Curve (AUC) is a widely used performance measure for imbalanced classification arising from many application domains where high-dimensional sparse data is abundant.
The Trumpiest Trump? Identifying a Subject's Most Characteristic Tweets
Indeed, we demonstrate a statistically significant correlation between this score and tweet popularity (likes/replies/retweets) for 13 of the 15 celebrities in our study.
Fast and Accurate Network Embeddings via Very Sparse Random Projection
Two key features of FastRP are: 1) it explicitly constructs a node similarity matrix that captures transitive relationships in a graph and normalizes matrix entries based on node degrees; 2) it utilizes very sparse random projection, which is a scalable optimization-free method for dimension reduction.
The Secret Lives of Names? Name Embeddings from Social Media
We find it interesting that adding name embeddings can further improve the performances of models using demographic features, which are traditionally used for lifespan modeling.
MediaRank: Computational Ranking of Online News Sources
We base our algorithmic analysis on four properties journalists have established to be associated with reporting quality: peer reputation, reporting bias / breadth, bottomline financial pressure, and popularity.
Enhanced Network Embeddings via Exploiting Edge Labels
Network embedding methods aim at learning low-dimensional latent representation of nodes in a network.
Social and Information Networks Physics and Society
Multi-view Models for Political Ideology Detection of News Articles
A news article's title, content and link structure often reveal its political ideology.
Learning to Represent Bilingual Dictionaries
Bilingual word embeddings have been widely used to capture the similarity of lexical semantics in different human languages.
A Tutorial on Network Embeddings
We further demonstrate the applications of network embeddings, and conclude the survey with future work in this area.
Social and Information Networks
Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment
Since many multilingual KGs also provide literal descriptions of entities, in this paper, we introduce an embedding-based approach which leverages a weakly aligned multilingual KG for semi-supervised cross-lingual learning using entity descriptions.
Syntax-Directed Variational Autoencoder for Structured Data
Deep generative models have been enjoying success in modeling continuous data.
Nationality Classification Using Name Embeddings
Through our analysis of 57M contact lists from a major Internet company, we are able to design a fine-grained nationality classifier covering 39 groups representing over 90% of the world population.
HARP: Hierarchical Representation Learning for Networks
We present HARP, a novel method for learning low dimensional embeddings of a graph's nodes which preserves higher-order structural features.
Social and Information Networks
Latent Human Traits in the Language of Social Media: An Open-Vocabulary Approach
Taking advantage of linguistic information available through Facebook, we study the process of inferring a new set of potential human traits based on unprompted language use.
Recognizing Descriptive Wikipedia Categories for Historical Figures
Wikipedia is a useful knowledge source that benefits many applications in language processing and knowledge representation.
False-Friend Detection and Entity Matching via Unsupervised Transliteration
Transliterations play an important role in multilingual entity reference resolution, because proper names increasingly travel between languages in news and social media.
On the Convergent Properties of Word Embedding Methods
Do word embeddings converge to learn similar things over different initializations?
Don't Walk, Skip! Online Learning of Multi-scale Network Embeddings
We present Walklets, a novel approach for learning multiscale representations of vertices in a network.
Social and Information Networks Physics and Society
Freshman or Fresher? Quantifying the Geographic Variation of Internet Language
Our analysis of British and American English over a period of 100 years reveals that semantic variation between these dialects is shrinking.
Statistically Significant Detection of Linguistic Change
We propose a new computational approach for tracking and detecting statistically significant linguistic shifts in the meaning and usage of words.
POLYGLOT-NER: Massive Multilingual Named Entity Recognition
We describe a system that builds Named Entity Recognition (NER) annotators for 40 major languages using Wikipedia and Freebase.
Exploring the power of GPU's for training Polyglot language models
We evaluate the performance of training the model on the GPU and present optimizations that boost the performance on the GPU. One of the key optimizations, we propose increases the performance of a function involved in calculating and updating the gradient by approximately 50 times on the GPU for sufficiently large batch sizes.
DeepWalk: Online Learning of Social Representations
We present DeepWalk, a novel approach for learning latent representations of vertices in a network.
Ranked #1 on
Link Property Prediction
on ogbl-ddi
Inducing Language Networks from Continuous Space Word Representations
Recent advancements in unsupervised feature learning have developed powerful latent representations of words.
Polyglot: Distributed Word Representations for Multilingual NLP
We quantitatively demonstrate the utility of our word embeddings by using them as the sole features for training a part of speech tagger for a subset of these languages.
The Expressive Power of Word Embeddings
We seek to better understand the difference in quality of the several publicly released embeddings.
SpeedRead: A Fast Named Entity Recognition Pipeline
Online content analysis employs algorithmic methods to identify entities in unstructured text.
