Andrew Gordon

Also published as: Andrew S. Gordon


Deep Natural Language Understanding of News Text
Jaya Shree | Emily Liu | Andrew Gordon | Jerry Hobbs
Proceedings of the First Workshop on Narrative Understanding

Early proposals for the deep understanding of natural language text advocated an approach of “interpretation as abduction,” where the meaning of a text was derived as an explanation that logically entailed the input words, given a knowledge base of lexical and commonsense axioms. While most subsequent NLP research has instead pursued statistical and data-driven methods, the approach of interpretation as abduction has seen steady advancements in both theory and software implementations. In this paper, we summarize advances in deriving the logical form of the text, encoding commonsense knowledge, and technologies for scalable abductive reasoning. We then explore the application of these advancements to the deep understanding of a paragraph of news text, where the subtle meaning of words and phrases are resolved by backward chaining on a knowledge base of 80 hand-authored axioms.


pdf bib
Linguistic Features of Helpfulness in Automated Support for Creative Writing
Melissa Roemmele | Andrew Gordon
Proceedings of the First Workshop on Storytelling

We examine an emerging NLP application that supports creative writing by automatically suggesting continuing sentences in a story. The application tracks users’ modifications to generated sentences, which can be used to quantify their “helpfulness” in advancing the story. We explore the task of predicting helpfulness based on automatically detected linguistic features of the suggestions. We illustrate this analysis on a set of user interactions with the application using an initial selection of features relevant to story generation.

An Encoder-decoder Approach to Predicting Causal Relations in Stories
Melissa Roemmele | Andrew Gordon
Proceedings of the First Workshop on Storytelling

We address the task of predicting causally related events in stories according to a standard evaluation framework, the Choice of Plausible Alternatives (COPA). We present a neural encoder-decoder model that learns to predict relations between adjacent sequences in stories as a means of modeling causality. We explore this approach using different methods for extracting and representing sequence pairs as well as different model architectures. We also compare the impact of different training datasets on our model. In particular, we demonstrate the usefulness of a corpus not previously applied to COPA, the ROCStories corpus. While not state-of-the-art, our results establish a new reference point for systems evaluated on COPA, and one that is particularly informative for future neural-based approaches.


An RNN-based Binary Classifier for the Story Cloze Test
Melissa Roemmele | Sosuke Kobayashi | Naoya Inoue | Andrew Gordon
Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics

The Story Cloze Test consists of choosing a sentence that best completes a story given two choices. In this paper we present a system that performs this task using a supervised binary classifier on top of a recurrent neural network to predict the probability that a given story ending is correct. The classifier is trained to distinguish correct story endings given in the training data from incorrect ones that we artificially generate. Our experiments evaluate different methods for generating these negative examples, as well as different embedding-based representations of the stories. Our best result obtains 67.2% accuracy on the test set, outperforming the existing top baseline of 58.5%.

Natural-language Interactive Narratives in Imaginal Exposure Therapy for Obsessive-Compulsive Disorder
Melissa Roemmele | Paola Mardo | Andrew Gordon
Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology — From Linguistic Signal to Clinical Reality

Obsessive-compulsive disorder (OCD) is an anxiety-based disorder that affects around 2.5% of the population. A common treatment for OCD is exposure therapy, where the patient repeatedly confronts a feared experience, which has the long-term effect of decreasing their anxiety. Some exposures consist of reading and writing stories about an imagined anxiety-provoking scenario. In this paper, we present a technology that enables patients to interactively contribute to exposure stories by supplying natural language input (typed or spoken) that advances a scenario. This interactivity could potentially increase the patient’s sense of immersion in an exposure and contribute to its success. We introduce the NLP task behind processing inputs to predict new events in the scenario, and describe our initial approach. We then illustrate the future possibility of this work with an example of an exposure scenario authored with our application.


Improving Fluency in Narrative Text Generation With Grammatical Transformations and Probabilistic Parsing
Emily Ahn | Fabrizio Morbini | Andrew Gordon
Proceedings of the 9th International Natural Language Generation conference


Abduction for Discourse Interpretation: A Probabilistic Framework
Ekaterina Ovchinnikova | Andrew Gordon | Jerry Hobbs
Proceedings of the Joint Symposium on Semantic Processing. Textual Inference and Structures in Corpora


SemEval-2012 Task 7: Choice of Plausible Alternatives: An Evaluation of Commonsense Causal Reasoning
Andrew Gordon | Zornitsa Kozareva | Melissa Roemmele
*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)


Open-domain Commonsense Reasoning Using Discourse Relations from a Corpus of Weblog Stories
Matthew Gerber | Andrew Gordon | Kenji Sagae
Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading


Clustering Words by Syntactic Similarity improves Dependency Parsing of Predicate-argument Structures
Kenji Sagae | Andrew S. Gordon
Proceedings of the 11th International Conference on Parsing Technologies (IWPT’09)


Generalizing semantic role annotations across syntactically similar verbs
Andrew Gordon | Reid Swanson
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics


A Comparison of Alternative Parse Tree Paths for Labeling Semantic Roles
Reid Swanson | Andrew S. Gordon
Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions


Recognizing Expressions of Commonsense Psychology in English Text
Andrew Gordon | Abe Kazemzadeh | Anish Nair | Milena Petrova
Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics