Search Results for author: Mark Dredze

Found 80 papers, 26 papers with code

Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction

2 code implementations14 Sep 2021 Mahsa Yarmohammadi, Shijie Wu, Marc Marone, Haoran Xu, Seth Ebner, Guanghui Qin, Yunmo Chen, Jialiang Guo, Craig Harman, Kenton Murray, Aaron Steven White, Mark Dredze, Benjamin Van Durme

Zero-shot cross-lingual information extraction (IE) describes the construction of an IE model for some target language, given existing annotations exclusively in some other language, typically English.

Dependency Parsing Event Extraction +2

Learning to Look Inside: Augmenting Token-Based Encoders with Character-Level Information

no code implementations1 Aug 2021 Yuval Pinter, Amanda Stent, Mark Dredze, Jacob Eisenstein

Commonly-used transformer language models depend on a tokenization schema which sets an unchangeable subword vocabulary prior to pre-training, destined to be applied to all downstream tasks regardless of domain shift, novel word formations, or other sources of vocabulary mismatch.

Tokenization

Faithful and Plausible Explanations of Medical Code Predictions

1 code implementation16 Apr 2021 Zach Wood-Doughty, Isabel Cachola, Mark Dredze

Machine learning models that offer excellent predictive performance often lack the interpretability necessary to support integrated human machine decision-making.

Decision Making

Improving Zero-Shot Multi-Lingual Entity Linking

no code implementations16 Apr 2021 Elliot Schumacher, James Mayfield, Mark Dredze

Entity linking -- the task of identifying references in free text to relevant knowledge base representations -- often focuses on single languages.

Entity Linking

Fine-tuning Encoders for Improved Monolingual and Zero-shot Polylingual Neural Topic Modeling

1 code implementation NAACL 2021 Aaron Mueller, Mark Dredze

Neural topic models can augment or replace bag-of-words inputs with the learned representations of deep pre-trained transformer-based word prediction models.

Classification Cross-Lingual Transfer +3

Gender and Racial Fairness in Depression Research using Social Media

no code implementations EACL 2021 Carlos Aguirre, Keith Harrigian, Mark Dredze

While previous research has raised concerns about possible biases in models produced from this data, no study has quantified how these biases actually manifest themselves with respect to different demographic groups, such as gender and racial/ethnic groups.

Fairness

User Factor Adaptation for User Embedding via Multitask Learning

1 code implementation22 Feb 2021 Xiaolei Huang, Michael J. Paul, Robin Burke, Franck Dernoncourt, Mark Dredze

In this study, we treat the user interest as domains and empirically examine how the user language can vary across the user factor in three English social media datasets.

Text Classification

Generating Synthetic Text Data to Evaluate Causal Inference Methods

no code implementations10 Feb 2021 Zach Wood-Doughty, Ilya Shpitser, Mark Dredze

High-dimensional and unstructured data such as natural language complicates the evaluation of causal inference methods; such evaluations rely on synthetic datasets with known causal effects.

Causal Inference Text Generation

On the State of Social Media Data for Mental Health Research

1 code implementation10 Nov 2020 Keith Harrigian, Carlos Aguirre, Mark Dredze

Data-driven methods for mental health treatment and surveillance have become a major focus in computational science research in the last decade.

Do Models of Mental Health Based on Social Media Data Generalize?

no code implementations Findings of the Association for Computational Linguistics 2020 Keith Harrigian, Carlos Aguirre, Mark Dredze

Proxy-based methods for annotating mental health status in social media have grown popular in computational research due to their ability to gather large training samples.

Cross-Lingual Transfer in Zero-Shot Cross-Language Entity Linking

1 code implementation19 Oct 2020 Elliot Schumacher, James Mayfield, Mark Dredze

We find that the multilingual ability of BERT leads to robust performance in monolingual and multilingual settings.

Cross-Lingual Transfer Entity Linking

Demographic Representation and Collective Storytelling in the Me Too Twitter Hashtag Activism Movement

no code implementations13 Oct 2020 Aaron Mueller, Zach Wood-Doughty, Silvio Amir, Mark Dredze, Alicia L. Nobles

The #MeToo movement on Twitter has drawn attention to the pervasive nature of sexual harassment and violence.

Do Explicit Alignments Robustly Improve Multilingual Encoders?

1 code implementation EMNLP 2020 Shijie Wu, Mark Dredze

Multilingual BERT (mBERT), XLM-RoBERTa (XLMR) and other unsupervised multilingual encoders can effectively learn cross-lingual representation.

Clinical Concept Linking with Contextualized Neural Representations

no code implementations ACL 2020 Elliot Schumacher, Andriy Mulyar, Mark Dredze

We propose an approach to concept linking that leverages recent work in contextualized neural models, such as ELMo (Peters et al. 2018), which create a token representation that integrates the surrounding context of the mention and concept name.

Entity Linking

Are All Languages Created Equal in Multilingual BERT?

1 code implementation WS 2020 Shijie Wu, Mark Dredze

Multilingual BERT (mBERT) trained on 104 languages has shown surprisingly good cross-lingual performance on several NLP tasks, even without explicit cross-lingual signals.

Cross-Lingual Transfer Dependency Parsing +2

Sources of Transfer in Multilingual Named Entity Recognition

1 code implementation ACL 2020 David Mueller, Nicholas Andrews, Mark Dredze

However, a straightforward implementation of this simple idea does not always work in practice: naive training of NER models using annotated data drawn from multiple languages consistently underperforms models trained on monolingual data alone, despite having access to more training data.

Named Entity Recognition NER

Using Noisy Self-Reports to Predict Twitter User Demographics

1 code implementation1 May 2020 Zach Wood-Doughty, Paiheng Xu, Xiao Liu, Mark Dredze

We present a method to identify self-reports of race and ethnicity from Twitter profile descriptions.

Mental Health Surveillance over Social Media with Digital Cohorts

no code implementations WS 2019 Silvio Amir, Mark Dredze, John W. Ayers

The ability to track mental health conditions via social media opened the doors for large-scale, automated, mental health surveillance.

Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT

1 code implementation IJCNLP 2019 Shijie Wu, Mark Dredze

Pretrained contextual representation models (Peters et al., 2018; Devlin et al., 2018) have pushed forward the state-of-the-art on many NLP tasks.

Cross-Lingual NER Dependency Parsing +5

Using Author Embeddings to Improve Tweet Stance Classification

no code implementations WS 2018 Adrian Benton, Mark Dredze

Many social media classification tasks analyze the content of a message, but do not consider the context of the message.

Classification General Classification +1

Convolutions Are All You Need (For Classifying Character Sequences)

no code implementations WS 2018 Zach Wood-Doughty, Nicholas Andrews, Mark Dredze

While recurrent neural networks (RNNs) are widely used for text classification, they demonstrate poor performance and slow convergence when trained on long sequences.

Classification Document Classification +3

Challenges of Using Text Classifiers for Causal Inference

1 code implementation EMNLP 2018 Zach Wood-Doughty, Ilya Shpitser, Mark Dredze

Causal understanding is essential for many kinds of decision-making, but causal inference from observational data has typically only been applied to structured, low-dimensional datasets.

Causal Inference Decision Making

Predicting Twitter User Demographics from Names Alone

1 code implementation WS 2018 Zach Wood-Doughty, Nicholas Andrews, Rebecca Marvin, Mark Dredze

Social media analysis frequently requires tools that can automatically infer demographics to contextualize trends.

Deep Dirichlet Multinomial Regression

1 code implementation NAACL 2018 Adrian Benton, Mark Dredze

We present deep Dirichlet Multinomial Regression (dDMR), a generative topic model that simultaneously learns document feature representations and topics.

Document-level Topic Models

Johns Hopkins or johnny-hopkins: Classifying Individuals versus Organizations on Twitter

1 code implementation WS 2018 Zach Wood-Doughty, Praateek Mahajan, Mark Dredze

Previous work (McCorriston et al., 2015) presented a method for determining if an account was an individual or organization based on account profile and a collection of tweets.

General Classification

Constructing an Alias List for Named Entities during an Event

no code implementations WS 2017 Anietie Andy, Mark Dredze, Mugizi Rwebangira, Chris Callison-Burch

EntitySpike uses a temporal heuristic to identify named entities with similar context that occur in the same time period (within minutes) during an event.

Community Question Answering

How Does Twitter User Behavior Vary Across Demographic Groups?

no code implementations WS 2017 Zach Wood-Doughty, Michael Smith, David Broniatowski, Mark Dredze

Demographically-tagged social media messages are a common source of data for computational social science.

Pocket Knowledge Base Population

no code implementations ACL 2017 Travis Wolfe, Mark Dredze, Benjamin Van Durme

Existing Knowledge Base Population methods extract relations from a closed relational schema with limited coverage leading to sparse KBs.

Knowledge Base Population Open Information Extraction +1

Ethical Research Protocols for Social Media Health Research

no code implementations WS 2017 Adrian Benton, Glen Coppersmith, Mark Dredze

Social media have transformed data-driven research in political science, the social sciences, health, and medicine.

Decision Making

Feature Generation for Robust Semantic Role Labeling

no code implementations22 Feb 2017 Travis Wolfe, Mark Dredze, Benjamin Van Durme

Hand-engineered feature sets are a well understood method for creating robust NLP models, but they require a lot of expertise and effort to create.

Semantic Role Labeling

Harmonic Grammar, Optimality Theory, and Syntax Learnability: An Empirical Exploration of Czech Word Order

no code implementations19 Feb 2017 Ann Irvine, Mark Dredze

This work presents a systematic theoretical and empirical comparison of the major algorithms that have been proposed for learning Harmonic and Optimality Theory grammars (HG and OT, respectively).

Multi-task Domain Adaptation for Sequence Tagging

no code implementations WS 2017 Nanyun Peng, Mark Dredze

Many domain adaptation approaches rely on learning cross domain shared representations to transfer the knowledge learned in one domain to other domains.

Chinese Word Segmentation Domain Adaptation +2

Twitter as a Source of Global Mobility Patterns for Social Good

no code implementations20 Jun 2016 Mark Dredze, Manuel García-Herranz, Alex Rutherford, Gideon Mann

Data on human spatial distribution and movement is essential for understanding and analyzing social systems.

Embedding Lexical Features via Low-Rank Tensors

1 code implementation NAACL 2016 Mo Yu, Mark Dredze, Raman Arora, Matthew Gormley

Modern NLP models rely heavily on engineered features, which often combine word and contextual information into complex lexical features.

Relation Extraction

Improving Named Entity Recognition for Chinese Social Media with Word Segmentation Representation Learning

no code implementations ACL 2016 Nanyun Peng, Mark Dredze

Named entity recognition, and other information extraction tasks, frequently use linguistic features such as part of speech tags or chunkings.

Named Entity Recognition NER +1

Approximation-Aware Dependency Parsing by Belief Propagation

no code implementations TACL 2015 Matthew R. Gormley, Mark Dredze, Jason Eisner

We show how to adjust the model parameters to compensate for the errors introduced by this approximation, by following the gradient of the actual loss on training data.

Dependency Parsing

Interactive Knowledge Base Population

no code implementations31 May 2015 Travis Wolfe, Mark Dredze, James Mayfield, Paul McNamee, Craig Harman, Tim Finin, Benjamin Van Durme

Most work on building knowledge bases has focused on collecting entities and facts from as large a collection of documents as possible.

Knowledge Base Population

Improved Relation Extraction with Feature-Rich Compositional Embedding Models

1 code implementation EMNLP 2015 Matthew R. Gormley, Mo Yu, Mark Dredze

We propose a Feature-rich Compositional Embedding Model (FCM) for relation extraction that is expressive, generalizes to new domains, and is easy-to-implement.

 Ranked #1 on Relation Extraction on ACE 2005 (Cross Sentence metric)

Relation Classification Word Embeddings

Sprite: Generalizing Topic Models with Structured Priors

no code implementations TACL 2015 Michael J. Paul, Mark Dredze

We introduce Sprite, a family of topic models that incorporates structure into model priors as a function of underlying components.

Topic Models

Learning Composition Models for Phrase Embeddings

1 code implementation TACL 2015 Mo Yu, Mark Dredze

We propose efficient unsupervised and task-specific learning objectives that scale our model to large datasets.

Language Modelling Semantic Similarity +2

Factorial LDA: Sparse Multi-Dimensional Text Models

no code implementations NeurIPS 2012 Michael Paul, Mark Dredze

Multi-dimensional latent variable models can capture the many latent factors in a text corpus, such as topic, author perspective and sentiment.

Latent Variable Models

Adaptive Regularization of Weight Vectors

no code implementations NeurIPS 2009 Koby Crammer, Alex Kulesza, Mark Dredze

We present AROW, a new online learning algorithm that combines several properties of successful : large margin training, confidence weighting, and the capacity to handle non-separable data.

Exact Convex Confidence-Weighted Learning

no code implementations NeurIPS 2008 Koby Crammer, Mark Dredze, Fernando Pereira

Confidence-weighted (CW) learning [6], an online learning method for linear classifiers, maintains a Gaussian distributions over weight vectors, with a covariance matrix that represents uncertainty about weights and correlations.

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