Marco A. Valenzuela-Escárcega

Also published as: Marco Antonio Valenzuela-Escárcega


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An Unsupervised Method for Learning Representations of Multi-word Expressions for Semantic Classification
Robert Vacareanu | Marco A. Valenzuela-Escárcega | Rebecca Sharp | Mihai Surdeanu
Proceedings of the 28th International Conference on Computational Linguistics

This paper explores an unsupervised approach to learning a compositional representation function for multi-word expressions (MWEs), and evaluates it on the Tratz dataset, which associates two-word expressions with the semantic relation between the compound constituents (e.g. the label employer is associated with the noun compound government agency) (Tratz, 2011). The composition function is based on recurrent neural networks, and is trained using the Skip-Gram objective to predict the words in the context of MWEs. Thus our approach can naturally leverage large unlabeled text sources. Further, our method can make use of provided MWEs when available, but can also function as a completely unsupervised algorithm, using MWE boundaries predicted by a single, domain-agnostic part-of-speech pattern. With pre-defined MWE boundaries, our method outperforms the previous state-of-the-art performance on the coarse-grained evaluation of the Tratz dataset (Tratz, 2011), with an F1 score of 50.4%. The unsupervised version of our method approaches the performance of the supervised one, and even outperforms it in some configurations.

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Odinson: A Fast Rule-based Information Extraction Framework
Marco A. Valenzuela-Escárcega | Gus Hahn-Powell | Dane Bell
Proceedings of the 12th 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.

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MathAlign: Linking Formula Identifiers to their Contextual Natural Language Descriptions
Maria Alexeeva | Rebecca Sharp | Marco A. Valenzuela-Escárcega | Jennifer Kadowaki | Adarsh Pyarelal | Clayton Morrison
Proceedings of the 12th Language Resources and Evaluation Conference

Extending machine reading approaches to extract mathematical concepts and their descriptions is useful for a variety of tasks, ranging from mathematical information retrieval to increasing accessibility of scientific documents for the visually impaired. This entails segmenting mathematical formulae into identifiers and linking them to their natural language descriptions. We propose a rule-based approach for this task, which extracts LaTeX representations of formula identifiers and links them to their in-text descriptions, given only the original PDF and the location of the formula of interest. We also present a novel evaluation dataset for this task, as well as the tool used to create it.

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Parsing as Tagging
Robert Vacareanu | George Caique Gouveia Barbosa | Marco A. Valenzuela-Escárcega | Mihai Surdeanu
Proceedings of the 12th Language Resources and Evaluation Conference

We propose a simple yet accurate method for dependency parsing that treats parsing as tagging (PaT). That is, our approach addresses the parsing of dependency trees with a sequence model implemented with a bidirectional LSTM over BERT embeddings, where the “tag” to be predicted at each token position is the relative position of the corresponding head. For example, for the sentence John eats cake, the tag to be predicted for the token cake is -1 because its head (eats) occurs one token to the left. Despite its simplicity, our approach performs well. For example, our approach outperforms the state-of-the-art method of (Fernández-González and Gómez-Rodríguez, 2019) on Universal Dependencies (UD) by 1.76% unlabeled attachment score (UAS) for English, 1.98% UAS for French, and 1.16% UAS for German. On average, on 12 UD languages, our method with minimal tuning performs comparably with this state-of-the-art approach: better by 0.11% UAS, and worse by 0.58% LAS.


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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.

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Eidos, INDRA, & Delphi: From Free Text to Executable Causal Models
Rebecca Sharp | Adarsh Pyarelal | Benjamin Gyori | Keith Alcock | Egoitz Laparra | Marco A. Valenzuela-Escárcega | Ajay Nagesh | Vikas Yadav | John Bachman | Zheng Tang | Heather Lent | Fan Luo | Mithun Paul | Steven Bethard | Kobus Barnard | Clayton Morrison | Mihai Surdeanu
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)

Building causal models of complicated phenomena such as food insecurity is currently a slow and labor-intensive manual process. In this paper, we introduce an approach that builds executable probabilistic models from raw, free text. The proposed approach is implemented through three systems: Eidos, INDRA, and Delphi. Eidos is an open-domain machine reading system designed to extract causal relations from natural language. It is rule-based, allowing for rapid domain transfer, customizability, and interpretability. INDRA aggregates multiple sources of causal information and performs assembly to create a coherent knowledge base and assess its reliability. This assembled knowledge serves as the starting point for modeling. Delphi is a modeling framework that assembles quantified causal fragments and their contexts into executable probabilistic models that respect the semantics of the original text, and can be used to support decision making.


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Text Annotation Graphs: Annotating Complex Natural Language Phenomena
Angus Forbes | Kristine Lee | Gus Hahn-Powell | Marco A. Valenzuela-Escárcega | Mihai Surdeanu
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Scientific Discovery as Link Prediction in Influence and Citation Graphs
Fan Luo | Marco A. Valenzuela-Escárcega | Gus Hahn-Powell | Mihai Surdeanu
Proceedings of the Twelfth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-12)

We introduce a machine learning approach for the identification of “white spaces” in scientific knowledge. Our approach addresses this task as link prediction over a graph that contains over 2M influence statements such as “CTCF activates FOXA1”, which were automatically extracted using open-domain machine reading. We model this prediction task using graph-based features extracted from the above influence graph, as well as from a citation graph that captures scientific communities. We evaluated the proposed approach through backtesting. Although the data is heavily unbalanced (50 times more negative examples than positives), our approach predicts which influence links will be discovered in the “near future” with a F1 score of 27 points, and a mean average precision of 68%.


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Tell Me Why: Using Question Answering as Distant Supervision for Answer Justification
Rebecca Sharp | Mihai Surdeanu | Peter Jansen | Marco A. Valenzuela-Escárcega | Peter Clark | Michael Hammond
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

For many applications of question answering (QA), being able to explain why a given model chose an answer is critical. However, the lack of labeled data for answer justifications makes learning this difficult and expensive. Here we propose an approach that uses answer ranking as distant supervision for learning how to select informative justifications, where justifications serve as inferential connections between the question and the correct answer while often containing little lexical overlap with either. We propose a neural network architecture for QA that reranks answer justifications as an intermediate (and human-interpretable) step in answer selection. Our approach is informed by a set of features designed to combine both learned representations and explicit features to capture the connection between questions, answers, and answer justifications. We show that with this end-to-end approach we are able to significantly improve upon a strong IR baseline in both justification ranking (+9% rated highly relevant) and answer selection (+6% P@1).

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Swanson linking revisited: Accelerating literature-based discovery across domains using a conceptual influence graph
Gus Hahn-Powell | Marco A. Valenzuela-Escárcega | Mihai Surdeanu
Proceedings of ACL 2017, System Demonstrations

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Learning what to read: Focused machine reading
Enrique Noriega-Atala | Marco A. Valenzuela-Escárcega | Clayton Morrison | Mihai Surdeanu
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Recent efforts in bioinformatics have achieved tremendous progress in the machine reading of biomedical literature, and the assembly of the extracted biochemical interactions into large-scale models such as protein signaling pathways. However, batch machine reading of literature at today’s scale (PubMed alone indexes over 1 million papers per year) is unfeasible due to both cost and processing overhead. In this work, we introduce a focused reading approach to guide the machine reading of biomedical literature towards what literature should be read to answer a biomedical query as efficiently as possible. We introduce a family of algorithms for focused reading, including an intuitive, strong baseline, and a second approach which uses a reinforcement learning (RL) framework that learns when to explore (widen the search) or exploit (narrow it). We demonstrate that the RL approach is capable of answering more queries than the baseline, while being more efficient, i.e., reading fewer documents.


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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.

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Odin’s Runes: A Rule Language for Information Extraction
Marco A. Valenzuela-Escárcega | Gus Hahn-Powell | Mihai Surdeanu
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Odin is an information extraction framework that applies cascades of finite state automata over both surface text and syntactic dependency graphs. Support for syntactic patterns allow us to concisely define relations that are otherwise difficult to express in languages such as Common Pattern Specification Language (CPSL), which are currently limited to shallow linguistic features. The interaction of lexical and syntactic automata provides robustness and flexibility when writing extraction rules. This paper describes Odin’s declarative language for writing these cascaded automata.

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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

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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


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A Domain-independent Rule-based Framework for Event Extraction
Marco A. Valenzuela-Escárcega | Gus Hahn-Powell | Mihai Surdeanu | Thomas Hicks
Proceedings of ACL-IJCNLP 2015 System Demonstrations

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Two Practical Rhetorical Structure Theory Parsers
Mihai Surdeanu | Tom Hicks | Marco Antonio Valenzuela-Escárcega
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations