Morgan Ulinski


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Rad-SpatialNet: A Frame-based Resource for Fine-Grained Spatial Relations in Radiology Reports
Surabhi Datta | Morgan Ulinski | Jordan Godfrey-Stovall | Shekhar Khanpara | Roy F. Riascos-Castaneda | Kirk Roberts
Proceedings of the Twelfth Language Resources and Evaluation Conference

This paper proposes a representation framework for encoding spatial language in radiology based on frame semantics. The framework is adopted from the existing SpatialNet representation in the general domain with the aim to generate more accurate representations of spatial language used by radiologists. We describe Rad-SpatialNet in detail along with illustrating the importance of incorporating domain knowledge in understanding the varied linguistic expressions involved in different radiological spatial relations. This work also constructs a corpus of 400 radiology reports of three examination types (chest X-rays, brain MRIs, and babygrams) annotated with fine-grained contextual information according to this schema. Spatial trigger expressions and elements corresponding to a spatial frame are annotated. We apply BERT-based models (BERT-Base and BERT- Large) to first extract the trigger terms (lexical units for a spatial frame) and then to identify the related frame elements. The results of BERT- Large are decent, with F1 of 77.89 for spatial trigger extraction and an overall F1 of 81.61 and 66.25 across all frame elements using gold and predicted spatial triggers respectively. This frame-based resource can be used to develop and evaluate more advanced natural language processing (NLP) methods for extracting fine-grained spatial information from radiology text in the future.


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SpatialNet: A Declarative Resource for Spatial Relations
Morgan Ulinski | Bob Coyne | Julia Hirschberg
Proceedings of the Combined Workshop on Spatial Language Understanding (SpLU) and Grounded Communication for Robotics (RoboNLP)

This paper introduces SpatialNet, a novel resource which links linguistic expressions to actual spatial configurations. SpatialNet is based on FrameNet (Ruppenhofer et al., 2016) and VigNet (Coyne et al., 2011), two resources which use frame semantics to encode lexical meaning. SpatialNet uses a deep semantic representation of spatial relations to provide a formal description of how a language expresses spatial information. This formal representation of the lexical semantics of spatial language also provides a consistent way to represent spatial meaning across multiple languages. In this paper, we describe the structure of SpatialNet, with examples from English and German. We also show how SpatialNet can be combined with other existing NLP tools to create a text-to-scene system for a language.

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Crowdsourced Hedge Term Disambiguation
Morgan Ulinski | Julia Hirschberg
Proceedings of the 13th Linguistic Annotation Workshop

We address the issue of acquiring quality annotations of hedging words and phrases, linguistic phenomenona in which words, sounds, or other constructions are used to express ambiguity or uncertainty. Due to the limited availability of existing corpora annotated for hedging, linguists and other language scientists have been constrained as to the extent they can study this phenomenon. In this paper, we introduce a new method of acquiring hedging annotations via crowdsourcing, based on reformulating the task of labeling hedges as a simple word sense disambiguation task. We also introduce a new hedging corpus we have constructed by applying this method, a collection of forum posts annotated using Amazon Mechanical Turk. We found that the crowdsourced judgments we obtained had an inter-annotator agreement of 92.89% (Fleiss’ Kappa=0.751) and, when comparing a subset of these annotations to an expert-annotated gold standard, an accuracy of 96.65%.


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Using Hedge Detection to Improve Committed Belief Tagging
Morgan Ulinski | Seth Benjamin | Julia Hirschberg
Proceedings of the Workshop on Computational Semantics beyond Events and Roles

We describe a novel method for identifying hedge terms using a set of manually constructed rules. We present experiments adding hedge features to a committed belief system to improve classification. We compare performance of this system (a) without hedging features, (b) with dictionary-based features, and (c) with rule-based features. We find that using hedge features improves performance of the committed belief system, particularly in identifying instances of non-committed belief and reported belief.

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Evaluating the WordsEye Text-to-Scene System: Imaginative and Realistic Sentences
Morgan Ulinski | Bob Coyne | Julia Hirschberg
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


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Incrementally Learning a Dependency Parser to Support Language Documentation in Field Linguistics
Morgan Ulinski | Julia Hirschberg | Owen Rambow
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

We present experiments in incrementally learning a dependency parser. The parser will be used in the WordsEye Linguistics Tools (WELT) (Ulinski et al., 2014) which supports field linguists documenting a language’s syntax and semantics. Our goal is to make syntactic annotation faster for field linguists. We have created a new parallel corpus of descriptions of spatial relations and motion events, based on pictures and video clips used by field linguists for elicitation of language from native speaker informants. We collected descriptions for each picture and video from native speakers in English, Spanish, German, and Egyptian Arabic. We compare the performance of MSTParser (McDonald et al., 2006) and MaltParser (Nivre et al., 2006) when trained on small amounts of this data. We find that MaltParser achieves the best performance. We also present the results of experiments using the parser to assist with annotation. We find that even when the parser is trained on a single sentence from the corpus, annotation time significantly decreases.


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Documenting Endangered Languages with the WordsEye Linguistics Tool
Morgan Ulinski | Anusha Balakrishnan | Daniel Bauer | Bob Coyne | Julia Hirschberg | Owen Rambow
Proceedings of the 2014 Workshop on the Use of Computational Methods in the Study of Endangered Languages

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WELT: Using Graphics Generation in Linguistic Fieldwork
Morgan Ulinski | Anusha Balakrishnan | Bob Coyne | Julia Hirschberg | Owen Rambow
Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations