H. Andrew Schwartz

Also published as: H Andrew Schwartz, Hansen A. Schwartz, Hansen Andrew Schwartz


2021

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Empirical Evaluation of Pre-trained Transformers for Human-Level NLP: The Role of Sample Size and Dimensionality
Adithya V Ganesan | Matthew Matero | Aravind Reddy Ravula | Huy Vu | H. Andrew Schwartz
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

In human-level NLP tasks, such as predicting mental health, personality, or demographics, the number of observations is often smaller than the standard 768+ hidden state sizes of each layer within modern transformer-based language models, limiting the ability to effectively leverage transformers. Here, we provide a systematic study on the role of dimension reduction methods (principal components analysis, factorization techniques, or multi-layer auto-encoders) as well as the dimensionality of embedding vectors and sample sizes as a function of predictive performance. We first find that fine-tuning large models with a limited amount of data pose a significant difficulty which can be overcome with a pre-trained dimension reduction regime. RoBERTa consistently achieves top performance in human-level tasks, with PCA giving benefit over other reduction methods in better handling users that write longer texts. Finally, we observe that a majority of the tasks achieve results comparable to the best performance with just 1/12 of the embedding dimensions.

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On the Distribution, Sparsity, and Inference-time Quantization of Attention Values in Transformers
Tianchu Ji | Shraddhan Jain | Michael Ferdman | Peter Milder | H. Andrew Schwartz | Niranjan Balasubramanian
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Characterizing Social Spambots by their Human Traits
Salvatore Giorgi | Lyle Ungar | H. Andrew Schwartz
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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Autoregressive Affective Language Forecasting: A Self-Supervised Task
Matthew Matero | H. Andrew Schwartz
Proceedings of the 28th International Conference on Computational Linguistics

Human natural language is mentioned at a specific point in time while human emotions change over time. While much work has established a strong link between language use and emotional states, few have attempted to model emotional language in time. Here, we introduce the task of affective language forecasting – predicting future change in language based on past changes of language, a task with real-world applications such as treating mental health or forecasting trends in consumer confidence. We establish some of the fundamental autoregressive characteristics of the task (necessary history size, static versus dynamic length, varying time-step resolutions) and then build on popular sequence models for words to instead model sequences of language-based emotion in time. Over a novel Twitter dataset of 1,900 users and weekly + daily scores for 6 emotions and 2 additional linguistic attributes, we find a novel dual-sequence GRU model with decayed hidden states achieves best results (r = .66) significantly out-predicting, e.g., a moving averaging based on the past time-steps (r = .49). We make our anonymized dataset as well as task setup and evaluation code available for others to build on.

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Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview
Deven Santosh Shah | H. Andrew Schwartz | Dirk Hovy
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

An increasing number of natural language processing papers address the effect of bias on predictions, introducing mitigation techniques at different parts of the standard NLP pipeline (data and models). However, these works have been conducted individually, without a unifying framework to organize efforts within the field. This situation leads to repetitive approaches, and focuses overly on bias symptoms/effects, rather than on their origins, which could limit the development of effective countermeasures. In this paper, we propose a unifying predictive bias framework for NLP. We summarize the NLP literature and suggest general mathematical definitions of predictive bias. We differentiate two consequences of bias: outcome disparities and error disparities, as well as four potential origins of biases: label bias, selection bias, model overamplification, and semantic bias. Our framework serves as an overview of predictive bias in NLP, integrating existing work into a single structure, and providing a conceptual baseline for improved frameworks.

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Hierarchical Modeling for User Personality Prediction: The Role of Message-Level Attention
Veronica Lynn | Niranjan Balasubramanian | H. Andrew Schwartz
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Not all documents are equally important. Language processing is increasingly finding use as a supplement for questionnaires to assess psychological attributes of consenting individuals, but most approaches neglect to consider whether all documents of an individual are equally informative. In this paper, we present a novel model that uses message-level attention to learn the relative weight of users’ social media posts for assessing their five factor personality traits. We demonstrate that models with message-level attention outperform those with word-level attention, and ultimately yield state-of-the-art accuracies for all five traits by using both word and message attention in combination with past approaches (an average increase in Pearson r of 2.5%). In addition, examination of the high-signal posts identified by our model provides insight into the relationship between language and personality, helping to inform future work.

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Learning Emotion from 100 Observations: Unexpected Robustness of Deep Learning under Strong Data Limitations
Sven Buechel | João Sedoc | H. Andrew Schwartz | Lyle Ungar
Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media

One of the major downsides of Deep Learning is its supposed need for vast amounts of training data. As such, these techniques appear ill-suited for NLP areas where annotated data is limited, such as less-resourced languages or emotion analysis, with its many nuanced and hard-to-acquire annotation formats. We conduct a questionnaire study indicating that indeed the vast majority of researchers in emotion analysis deems neural models inferior to traditional machine learning when training data is limited. In stark contrast to those survey results, we provide empirical evidence for English, Polish, and Portuguese that commonly used neural architectures can be trained on surprisingly few observations, outperforming n-gram based ridge regression on only 100 data points. Our analysis suggests that high-quality, pre-trained word embeddings are a main factor for achieving those results.

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Understanding Weekly COVID-19 Concerns through Dynamic Content-Specific LDA Topic Modeling
Mohammadzaman Zamani | H. Andrew Schwartz | Johannes Eichstaedt | Sharath Chandra Guntuku | Adithya Virinchipuram Ganesan | Sean Clouston | Salvatore Giorgi
Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science

The novelty and global scale of the COVID-19 pandemic has lead to rapid societal changes in a short span of time. As government policy and health measures shift, public perceptions and concerns also change, an evolution documented within discourse on social media.We propose a dynamic content-specific LDA topic modeling technique that can help to identify different domains of COVID-specific discourse that can be used to track societal shifts in concerns or views. Our experiments show that these model-derived topics are more coherent than standard LDA topics, and also provide new features that are more helpful in prediction of COVID-19 related outcomes including social mobility and unemployment rate.

2019

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Tweet Classification without the Tweet: An Empirical Examination of User versus Document Attributes
Veronica Lynn | Salvatore Giorgi | Niranjan Balasubramanian | H. Andrew Schwartz
Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science

NLP naturally puts a primary focus on leveraging document language, occasionally considering user attributes as supplemental. However, as we tackle more social scientific tasks, it is possible user attributes might be of primary importance and the document supplemental. Here, we systematically investigate the predictive power of user-level features alone versus document-level features for document-level tasks. We first show user attributes can sometimes carry more task-related information than the document itself. For example, a tweet-level stance detection model using only 13 user-level attributes (i.e. features that did not depend on the specific tweet) was able to obtain a higher F1 than the top-performing SemEval participant. We then consider multiple tasks and a wider range of user attributes, showing the performance of strong document-only models can often be improved (as in stance, sentiment, and sarcasm) with user attributes, particularly benefiting tasks with stable “trait-like” outcomes (e.g. stance) most relative to frequently changing “state-like” outcomes (e.g. sentiment). These results not only support the growing work on integrating user factors into predictive systems, but that some of our NLP tasks might be better cast primarily as user-level (or human) tasks.

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Suicide Risk Assessment with Multi-level Dual-Context Language and BERT
Matthew Matero | Akash Idnani | Youngseo Son | Salvatore Giorgi | Huy Vu | Mohammad Zamani | Parth Limbachiya | Sharath Chandra Guntuku | H. Andrew Schwartz
Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology

Mental health predictive systems typically model language as if from a single context (e.g. Twitter posts, status updates, or forum posts) and often limited to a single level of analysis (e.g. either the message-level or user-level). Here, we bring these pieces together to explore the use of open-vocabulary (BERT embeddings, topics) and theoretical features (emotional expression lexica, personality) for the task of suicide risk assessment on support forums (the CLPsych-2019 Shared Task). We used dual context based approaches (modeling content from suicide forums separate from other content), built over both traditional ML models as well as a novel dual RNN architecture with user-factor adaptation. We find that while affect from the suicide context distinguishes with no-risk from those with “any-risk”, personality factors from the non-suicide contexts provide distinction of the levels of risk: low, medium, and high risk. Within the shared task, our dual-context approach (listed as SBU-HLAB in the official results) achieved state-of-the-art performance predicting suicide risk using a combination of suicide-context and non-suicide posts (Task B), achieving an F1 score of 0.50 over hidden test set labels.

2018

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CLPsych 2018 Shared Task: Predicting Current and Future Psychological Health from Childhood Essays
Veronica Lynn | Alissa Goodman | Kate Niederhoffer | Kate Loveys | Philip Resnik | H. Andrew Schwartz
Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic

We describe the shared task for the CLPsych 2018 workshop, which focused on predicting current and future psychological health from an essay authored in childhood. Language-based predictions of a person’s current health have the potential to supplement traditional psychological assessment such as questionnaires, improving intake risk measurement and monitoring. Predictions of future psychological health can aid with both early detection and the development of preventative care. Research into the mental health trajectory of people, beginning from their childhood, has thus far been an area of little work within the NLP community. This shared task represents one of the first attempts to evaluate the use of early language to predict future health; this has the potential to support a wide variety of clinical health care tasks, from early assessment of lifetime risk for mental health problems, to optimal timing for targeted interventions aimed at both prevention and treatment.

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Predicting Human Trustfulness from Facebook Language
Mohammadzaman Zamani | Anneke Buffone | H. Andrew Schwartz
Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic

Trustfulness — one’s general tendency to have confidence in unknown people or situations — predicts many important real-world outcomes such as mental health and likelihood to cooperate with others such as clinicians. While data-driven measures of interpersonal trust have previously been introduced, here, we develop the first language-based assessment of the personality trait of trustfulness by fitting one’s language to an accepted questionnaire-based trust score. Further, using trustfulness as a type of case study, we explore the role of questionnaire size as well as word count in developing language-based predictive models of users’ psychological traits. We find that leveraging a longer questionnaire can yield greater test set accuracy, while, for training, we find it beneficial to include users who took smaller questionnaires which offers more observations for training. Similarly, after noting a decrease in individual prediction error as word count increased, we found a word count-weighted training scheme was helpful when there were very few users in the first place.

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Identifying Locus of Control in Social Media Language
Masoud Rouhizadeh | Kokil Jaidka | Laura Smith | H. Andrew Schwartz | Anneke Buffone | Lyle Ungar
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Individuals express their locus of control, or “control”, in their language when they identify whether or not they are in control of their circumstances. Although control is a core concept underlying rhetorical style, it is not clear whether control is expressed by how or by what authors write. We explore the roles of syntax and semantics in expressing users’ sense of control –i.e. being “controlled by” or “in control of” their circumstances– in a corpus of annotated Facebook posts. We present rich insights into these linguistic aspects and find that while the language signaling control is easy to identify, it is more challenging to label it is internally or externally controlled, with lexical features outperforming syntactic features at the task. Our findings could have important implications for studying self-expression in social media.

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The Remarkable Benefit of User-Level Aggregation for Lexical-based Population-Level Predictions
Salvatore Giorgi | Daniel Preoţiuc-Pietro | Anneke Buffone | Daniel Rieman | Lyle Ungar | H. Andrew Schwartz
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Nowcasting based on social media text promises to provide unobtrusive and near real-time predictions of community-level outcomes. These outcomes are typically regarding people, but the data is often aggregated without regard to users in the Twitter populations of each community. This paper describes a simple yet effective method for building community-level models using Twitter language aggregated by user. Results on four different U.S. county-level tasks, spanning demographic, health, and psychological outcomes show large and consistent improvements in prediction accuracies (e.g. from Pearson r=.73 to .82 for median income prediction or r=.37 to .47 for life satisfaction prediction) over the standard approach of aggregating all tweets. We make our aggregated and anonymized community-level data, derived from 37 billion tweets – over 1 billion of which were mapped to counties, available for research.

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Causal Explanation Analysis on Social Media
Youngseo Son | Nipun Bayas | H. Andrew Schwartz
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Understanding causal explanations - reasons given for happenings in one’s life - has been found to be an important psychological factor linked to physical and mental health. Causal explanations are often studied through manual identification of phrases over limited samples of personal writing. Automatic identification of causal explanations in social media, while challenging in relying on contextual and sequential cues, offers a larger-scale alternative to expensive manual ratings and opens the door for new applications (e.g. studying prevailing beliefs about causes, such as climate change). Here, we explore automating causal explanation analysis, building on discourse parsing, and presenting two novel subtasks: causality detection (determining whether a causal explanation exists at all) and causal explanation identification (identifying the specific phrase that is the explanation). We achieve strong accuracies for both tasks but find different approaches best: an SVM for causality prediction (F1 = 0.791) and a hierarchy of Bidirectional LSTMs for causal explanation identification (F1 = 0.853). Finally, we explore applications of our complete pipeline (F1 = 0.868), showing demographic differences in mentions of causal explanation and that the association between a word and sentiment can change when it is used within a causal explanation.

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Residualized Factor Adaptation for Community Social Media Prediction Tasks
Mohammadzaman Zamani | H. Andrew Schwartz | Veronica Lynn | Salvatore Giorgi | Niranjan Balasubramanian
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Predictive models over social media language have shown promise in capturing community outcomes, but approaches thus far largely neglect the socio-demographic context (e.g. age, education rates, race) of the community from which the language originates. For example, it may be inaccurate to assume people in Mobile, Alabama, where the population is relatively older, will use words the same way as those from San Francisco, where the median age is younger with a higher rate of college education. In this paper, we present residualized factor adaptation, a novel approach to community prediction tasks which both (a) effectively integrates community attributes, as well as (b) adapts linguistic features to community attributes (factors). We use eleven demographic and socioeconomic attributes, and evaluate our approach over five different community-level predictive tasks, spanning health (heart disease mortality, percent fair/poor health), psychology (life satisfaction), and economics (percent housing price increase, foreclosure rate). Our evaluation shows that residualized factor adaptation significantly improves 4 out of 5 community-level outcome predictions over prior state-of-the-art for incorporating socio-demographic contexts.

2017

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On the Distribution of Lexical Features at Multiple Levels of Analysis
Fatemeh Almodaresi | Lyle Ungar | Vivek Kulkarni | Mohsen Zakeri | Salvatore Giorgi | H. Andrew Schwartz
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Natural language processing has increasingly moved from modeling documents and words toward studying the people behind the language. This move to working with data at the user or community level has presented the field with different characteristics of linguistic data. In this paper, we empirically characterize various lexical distributions at different levels of analysis, showing that, while most features are decidedly sparse and non-normal at the message-level (as with traditional NLP), they follow the central limit theorem to become much more Log-normal or even Normal at the user- and county-levels. Finally, we demonstrate that modeling lexical features for the correct level of analysis leads to marked improvements in common social scientific prediction tasks.

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Recognizing Counterfactual Thinking in Social Media Texts
Youngseo Son | Anneke Buffone | Joe Raso | Allegra Larche | Anthony Janocko | Kevin Zembroski | H Andrew Schwartz | Lyle Ungar
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Counterfactual statements, describing events that did not occur and their consequents, have been studied in areas including problem-solving, affect management, and behavior regulation. People with more counterfactual thinking tend to perceive life events as more personally meaningful. Nevertheless, counterfactuals have not been studied in computational linguistics. We create a counterfactual tweet dataset and explore approaches for detecting counterfactuals using rule-based and supervised statistical approaches. A combined rule-based and statistical approach yielded the best results (F1 = 0.77) outperforming either approach used alone.

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Human Centered NLP with User-Factor Adaptation
Veronica Lynn | Youngseo Son | Vivek Kulkarni | Niranjan Balasubramanian | H. Andrew Schwartz
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We pose the general task of user-factor adaptation – adapting supervised learning models to real-valued user factors inferred from a background of their language, reflecting the idea that a piece of text should be understood within the context of the user that wrote it. We introduce a continuous adaptation technique, suited for real-valued user factors that are common in social science and bringing us closer to personalized NLP, adapting to each user uniquely. We apply this technique with known user factors including age, gender, and personality traits, as well as latent factors, evaluating over five tasks: POS tagging, PP-attachment, sentiment analysis, sarcasm detection, and stance detection. Adaptation provides statistically significant benefits for 3 of the 5 tasks: up to +1.2 points for PP-attachment, +3.4 points for sarcasm, and +3.0 points for stance.

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Assessing Objective Recommendation Quality through Political Forecasting
H. Andrew Schwartz | Masoud Rouhizadeh | Michael Bishop | Philip Tetlock | Barbara Mellers | Lyle Ungar
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Recommendations are often rated for their subjective quality, but few researchers have studied comment quality in terms of objective utility. We explore recommendation quality assessment with respect to both subjective (i.e. users’ ratings) and objective (i.e., did it influence? did it improve decisions?) metrics in a massive online geopolitical forecasting system, ultimately comparing linguistic characteristics of each quality metric. Using a variety of features, we predict all types of quality with better accuracy than the simple yet strong baseline of comment length. Looking at the most predictive content illustrates rater biases; for example, forecasters are subjectively biased in favor of comments mentioning business transactions or dealings as well as material things, even though such comments do not indeed prove any more useful objectively. Additionally, more complex sentence constructions, as evidenced by subordinate conjunctions, are characteristic of comments leading to objective improvements in forecasting.

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DLATK: Differential Language Analysis ToolKit
H. Andrew Schwartz | Salvatore Giorgi | Maarten Sap | Patrick Crutchley | Lyle Ungar | Johannes Eichstaedt
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We present Differential Language Analysis Toolkit (DLATK), an open-source python package and command-line tool developed for conducting social-scientific language analyses. While DLATK provides standard NLP pipeline steps such as tokenization or SVM-classification, its novel strengths lie in analyses useful for psychological, health, and social science: (1) incorporation of extra-linguistic structured information, (2) specified levels and units of analysis (e.g. document, user, community), (3) statistical metrics for continuous outcomes, and (4) robust, proven, and accurate pipelines for social-scientific prediction problems. DLATK integrates multiple popular packages (SKLearn, Mallet), enables interactive usage (Jupyter Notebooks), and generally follows object oriented principles to make it easy to tie in additional libraries or storage technologies.

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Domain Adaptation from User-level Facebook Models to County-level Twitter Predictions
Daniel Rieman | Kokil Jaidka | H. Andrew Schwartz | Lyle Ungar
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Several studies have demonstrated how language models of user attributes, such as personality, can be built by using the Facebook language of social media users in conjunction with their responses to psychology questionnaires. It is challenging to apply these models to make general predictions about attributes of communities, such as personality distributions across US counties, because it requires 1. the potentially inavailability of the original training data because of privacy and ethical regulations, 2. adapting Facebook language models to Twitter language without retraining the model, and 3. adapting from users to county-level collections of tweets. We propose a two-step algorithm, Target Side Domain Adaptation (TSDA) for such domain adaptation when no labeled Twitter/county data is available. TSDA corrects for the different word distributions between Facebook and Twitter and for the varying word distributions across counties by adjusting target side word frequencies; no changes to the trained model are made. In the case of predicting the Big Five county-level personality traits, TSDA outperforms a state-of-the-art domain adaptation method, gives county-level predictions that have fewer extreme outliers, higher year-to-year stability, and higher correlation with county-level outcomes.

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Using Twitter Language to Predict the Real Estate Market
Mohammadzaman Zamani | H. Andrew Schwartz
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

We explore whether social media can provide a window into community real estate -foreclosure rates and price changes- beyond that of traditional economic and demographic variables. We find language use in Twitter not only predicts real estate outcomes as well as traditional variables across counties, but that including Twitter language in traditional models leads to a significant improvement (e.g. from Pearson r = :50 to r = :59 for price changes). We overcome the challenge of the relative sparsity and noise in Twitter language variables by showing that training on the residual error of the traditional models leads to more accurate overall assessments. Finally, we discover that it is Twitter language related to business (e.g. ‘company’, ‘marketing’) and technology (e.g. ‘technology’, ‘internet’), among others, that yield predictive power over economics.

2016

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Does ‘well-being’ translate on Twitter?
Laura Smith | Salvatore Giorgi | Rishi Solanki | Johannes Eichstaedt | H. Andrew Schwartz | Muhammad Abdul-Mageed | Anneke Buffone | Lyle Ungar
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Using Syntactic and Semantic Context to Explore Psychodemographic Differences in Self-reference
Masoud Rouhizadeh | Lyle Ungar | Anneke Buffone | H Andrew Schwartz
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Modelling Valence and Arousal in Facebook posts
Daniel Preoţiuc-Pietro | H. Andrew Schwartz | Gregory Park | Johannes Eichstaedt | Margaret Kern | Lyle Ungar | Elisabeth Shulman
Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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The Clinical Panel: Leveraging Psychological Expertise During NLP Research
Glen Coppersmith | Kristy Hollingshead | H. Andrew Schwartz | Molly Ireland | Rebecca Resnik | Kate Loveys | April Foreman | Loring Ingraham
Proceedings of the First Workshop on NLP and Computational Social Science

2015

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The role of personality, age, and gender in tweeting about mental illness
Daniel Preoţiuc-Pietro | Johannes Eichstaedt | Gregory Park | Maarten Sap | Laura Smith | Victoria Tobolsky | H. Andrew Schwartz | Lyle Ungar
Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality

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Mental Illness Detection at the World Well-Being Project for the CLPsych 2015 Shared Task
Daniel Preoţiuc-Pietro | Maarten Sap | H. Andrew Schwartz | Lyle Ungar
Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality

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Extracting Human Temporal Orientation from Facebook Language
H. Andrew Schwartz | Gregory Park | Maarten Sap | Evan Weingarten | Johannes Eichstaedt | Margaret Kern | David Stillwell | Michal Kosinski | Jonah Berger | Martin Seligman | Lyle Ungar
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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Towards Assessing Changes in Degree of Depression through Facebook
H. Andrew Schwartz | Johannes Eichstaedt | Margaret L. Kern | Gregory Park | Maarten Sap | David Stillwell | Michal Kosinski | Lyle Ungar
Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality

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Developing Age and Gender Predictive Lexica over Social Media
Maarten Sap | Gregory Park | Johannes Eichstaedt | Margaret Kern | David Stillwell | Michal Kosinski | Lyle Ungar | Hansen Andrew Schwartz
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2013

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Choosing the Right Words: Characterizing and Reducing Error of the Word Count Approach
Hansen Andrew Schwartz | Johannes Eichstaedt | Eduardo Blanco | Lukasz Dziurzynski | Margaret L. Kern | Stephanie Ramones | Martin Seligman | Lyle Ungar
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity

2012

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Penn: Using Word Similarities to better Estimate Sentence Similarity
Sneha Jha | Hansen A. Schwartz | Lyle Ungar
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)

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Improving Supervised Sense Disambiguation with Web-Scale Selectors
H. Andrew Schwartz | Fernando Gomez | Lyle Ungar
Proceedings of COLING 2012

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New Insights from Coarse Word Sense Disambiguation in the Crowd
Adam Kapelner | Krishna Kaliannan | H. Andrew Schwartz | Lyle Ungar | Dean Foster
Proceedings of COLING 2012: Posters

2010

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UCF-WS: Domain Word Sense Disambiguation Using Web Selectors
Hansen A. Schwartz | Fernando Gomez
Proceedings of the 5th International Workshop on Semantic Evaluation

2009

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Acquiring Applicable Common Sense Knowledge from the Web
Hansen A. Schwartz | Fernando Gomez
Proceedings of the Workshop on Unsupervised and Minimally Supervised Learning of Lexical Semantics

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Using Web Selectors for the Disambiguation of All Words
Hansen A. Schwartz | Fernando Gomez
Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions (SEW-2009)

2008

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Acquiring Knowledge from the Web to be used as Selectors for Noun Sense Disambiguation
Hansen A. Schwartz | Fernando Gomez
CoNLL 2008: Proceedings of the Twelfth Conference on Computational Natural Language Learning