Dane Bell


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PatternRank: Jointly Ranking Patterns and Extractions for Relation Extraction Using Graph-Based Algorithms
Robert Vacareanu | Dane Bell | Mihai Surdeanu
Proceedings of the First Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning

In this paper we revisit the direction of using lexico-syntactic patterns for relation extraction instead of today’s ubiquitous neural classifiers. We propose a semi-supervised graph-based algorithm for pattern acquisition that scores patterns and the relations they extract jointly, using a variant of PageRank. We insert light supervision in the form of seed patterns or relations, and model it with several custom teleportation probabilities that bias random-walk scores of patterns/relations based on their proximity to correct information. We evaluate our approach on Few-Shot TACRED, and show that our method outperforms (or performs competitively with) more expensive and opaque deep neural networks. Lastly, we thoroughly compare our proposed approach with the seminal RlogF pattern acquisition algorithm of, showing that it outperforms it for all the hyper parameters tested, in all settings.


Odinson: A Fast Rule-based Information Extraction Framework
Marco A. Valenzuela-Escárcega | Gus Hahn-Powell | Dane Bell
Proceedings of the Twelfth Language Resources and Evaluation Conference

We present Odinson, a rule-based information extraction framework, which couples a simple yet powerful pattern language that can operate over multiple representations of text, with a runtime system that operates in near real time. In the Odinson query language, a single pattern may combine regular expressions over surface tokens with regular expressions over graphs such as syntactic dependencies. To guarantee the rapid matching of these patterns, our framework indexes most of the necessary information for matching patterns, including directed graphs such as syntactic dependencies, into a custom Lucene index. Indexing minimizes the amount of expensive pattern matching that must take place at runtime. As a result, the runtime system matches a syntax-based graph traversal in 2.8 seconds in a corpus of over 134 million sentences, nearly 150,000 times faster than its predecessor.


Enabling Search and Collaborative Assembly of Causal Interactions Extracted from Multilingual and Multi-domain Free Text
George C. G. Barbosa | Zechy Wong | Gus Hahn-Powell | Dane Bell | Rebecca Sharp | Marco A. Valenzuela-Escárcega | Mihai Surdeanu
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)

Many of the most pressing current research problems (e.g., public health, food security, or climate change) require multi-disciplinary collaborations. In order to facilitate this process, we propose a system that incorporates multi-domain extractions of causal interactions into a single searchable knowledge graph. Our system enables users to search iteratively over direct and indirect connections in this knowledge graph, and collaboratively build causal models in real time. To enable the aggregation of causal information from multiple languages, we extend an open-domain machine reader to Portuguese. The new Portuguese reader extracts over 600 thousand causal statements from 120 thousand Portuguese publications with a precision of 62%, which demonstrates the value of mining multilingual scientific information.


Grounding Gradable Adjectives through Crowdsourcing
Rebecca Sharp | Mithun Paul | Ajay Nagesh | Dane Bell | Mihai Surdeanu
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Detecting Diabetes Risk from Social Media Activity
Dane Bell | Egoitz Laparra | Aditya Kousik | Terron Ishihara | Mihai Surdeanu | Stephen Kobourov
Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis

This work explores the detection of individuals’ risk of type 2 diabetes mellitus (T2DM) directly from their social media (Twitter) activity. Our approach extends a deep learning architecture with several contributions: following previous observations that language use differs by gender, it captures and uses gender information through domain adaptation; it captures recency of posts under the hypothesis that more recent posts are more representative of an individual’s current risk status; and, lastly, it demonstrates that in this scenario where activity factors are sparsely represented in the data, a bag-of-word neural network model using custom dictionaries of food and activity words performs better than other neural sequence models. Our best model, which incorporates all these contributions, achieves a risk-detection F1 of 41.9, considerably higher than the baseline rate (36.9).


SnapToGrid: From Statistical to Interpretable Models for Biomedical Information Extraction
Marco A. Valenzuela-Escárcega | Gus Hahn-Powell | Dane Bell | Mihai Surdeanu
Proceedings of the 15th Workshop on Biomedical Natural Language Processing

This before That: Causal Precedence in the Biomedical Domain
Gus Hahn-Powell | Dane Bell | Marco A. Valenzuela-Escárcega | Mihai Surdeanu
Proceedings of the 15th Workshop on Biomedical Natural Language Processing

Sieve-based Coreference Resolution in the Biomedical Domain
Dane Bell | Gus Hahn-Powell | Marco A. Valenzuela-Escárcega | Mihai Surdeanu
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We describe challenges and advantages unique to coreference resolution in the biomedical domain, and a sieve-based architecture that leverages domain knowledge for both entity and event coreference resolution. Domain-general coreference resolution algorithms perform poorly on biomedical documents, because the cues they rely on such as gender are largely absent in this domain, and because they do not encode domain-specific knowledge such as the number and type of participants required in chemical reactions. Moreover, it is difficult to directly encode this knowledge into most coreference resolution algorithms because they are not rule-based. Our rule-based architecture uses sequentially applied hand-designed “sieves”, with the output of each sieve informing and constraining subsequent sieves. This architecture provides a 3.2% increase in throughput to our Reach event extraction system with precision parallel to that of the stricter system that relies solely on syntactic patterns for extraction.

Towards Using Social Media to Identify Individuals at Risk for Preventable Chronic Illness
Dane Bell | Daniel Fried | Luwen Huangfu | Mihai Surdeanu | Stephen Kobourov
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We describe a strategy for the acquisition of training data necessary to build a social-media-driven early detection system for individuals at risk for (preventable) type 2 diabetes mellitus (T2DM). The strategy uses a game-like quiz with data and questions acquired semi-automatically from Twitter. The questions are designed to inspire participant engagement and collect relevant data to train a public-health model applied to individuals. Prior systems designed to use social media such as Twitter to predict obesity (a risk factor for T2DM) operate on entire communities such as states, counties, or cities, based on statistics gathered by government agencies. Because there is considerable variation among individuals within these groups, training data on the individual level would be more effective, but this data is difficult to acquire. The approach proposed here aims to address this issue. Our strategy has two steps. First, we trained a random forest classifier on data gathered from (public) Twitter statuses and state-level statistics with state-of-the-art accuracy. We then converted this classifier into a 20-questions-style quiz and made it available online. In doing so, we achieved high engagement with individuals that took the quiz, while also building a training set of voluntarily supplied individual-level data for future classification.