Eduardo Blanco


2020

pdf bib
An Analysis of Natural Language Inference Benchmarks through the Lens of Negation
Md Mosharaf Hossain | Venelin Kovatchev | Pranoy Dutta | Tiffany Kao | Elizabeth Wei | Eduardo Blanco
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Negation is underrepresented in existing natural language inference benchmarks. Additionally, one can often ignore the few negations in existing benchmarks and still make the right inference judgments. In this paper, we present a new benchmark for natural language inference in which negation plays a critical role. We also show that state-of-the-art transformers struggle making inference judgments with the new pairs.

pdf bib
Beyond Possession Existence : Duration and Co-Possession
Dhivya Chinnappa | Srikala Murugan | Eduardo Blanco
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

This paper introduces two tasks : determining (a) the duration of possession relations and (b) co-possessions, i.e., whether multiple possessors possess a possessee at the same time. We present new annotations on top of corpora annotating possession existence and experimental results. Regarding possession duration, we derive the time spans we work with empirically from annotations indicating lower and upper bounds. Regarding co-possessions, we use a binary label. Cohen’s kappa coefficients indicate substantial agreement, and experimental results show that text is more useful than the image for solving these tasks.

pdf bib
Predicting the Focus of Negation : Model and Error Analysis
Md Mosharaf Hossain | Kathleen Hamilton | Alexis Palmer | Eduardo Blanco
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

The focus of a negation is the set of tokens intended to be negated, and a key component for revealing affirmative alternatives to negated utterances. In this paper, we experiment with neural networks to predict the focus of negation. Our main novelty is leveraging a scope detector to introduce the scope of negation as an additional input to the network. Experimental results show that doing so obtains the best results to date. Additionally, we perform a detailed error analysis providing insights into the main error categories, and analyze errors depending on whether the model takes into account scope and context information.

2019

pdf bib
Extracting Possessions from Social Media : Images Complement Language
Dhivya Chinnappa | Srikala Murugan | Eduardo Blanco
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

This paper describes a new dataset and experiments to determine whether authors of tweets possess the objects they tweet about. We work with 5,000 tweets and show that both humans and neural networks benefit from images in addition to text. We also introduce a simple yet effective strategy to incorporate visual information into any neural network beyond weights from pretrained networks. Specifically, we consider the tags identified in an image as an additional textual input, and leverage pretrained word embeddings as usually done with regular text. Experimental results show this novel strategy is beneficial.

2018

pdf bib
Possessors Change Over Time : A Case Study with Artworks
Dhivya Chinnappa | Eduardo Blanco
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

This paper presents a corpus and experimental results to extract possession relations over time. We work with Wikipedia articles about artworks, and extract possession relations along with temporal information indicating when these relations are true. The annotation scheme yields many possessors over time for a given artwork, and experimental results show that an LSTM ensemble can automate the task.

pdf bib
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Eduardo Blanco | Wei Lu
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

pdf bib
Proceedings of the Workshop on Computational Semantics beyond Events and Roles
Eduardo Blanco | Roser Morante
Proceedings of the Workshop on Computational Semantics beyond Events and Roles

pdf bib
Mining Possessions : Existence, Type and Temporal Anchors
Dhivya Chinnappa | Eduardo Blanco
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

This paper presents a corpus and experiments to mine possession relations from text. Specifically, we target alienable and control possessions, and assign temporal anchors indicating when the possession holds between possessor and possessee. We present new annotations for this task, and experimental results using both traditional classifiers and neural networks. Results show that the three subtasks (predicting possession existence, possession type and temporal anchors) can be automated.

2017

pdf bib
Proceedings of the Workshop Computational Semantics Beyond Events and Roles
Eduardo Blanco | Roser Morante | Roser Saurí
Proceedings of the Workshop Computational Semantics Beyond Events and Roles

pdf bib
Dimensions of Interpersonal Relationships : Corpus and Experiments
Farzana Rashid | Eduardo Blanco
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

This paper presents a corpus and experiments to determine dimensions of interpersonal relationships. We define a set of dimensions heavily inspired by work in social science. We create a corpus by retrieving pairs of people, and then annotating dimensions for their relationships. A corpus analysis shows that dimensions can be annotated reliably. Experimental results show that given a pair of people, values to dimensions can be assigned automatically.

pdf bib
If No Media Were Allowed inside the Venue, Was Anybody Allowed?
Zahra Sarabi | Eduardo Blanco
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

This paper presents a framework to understand negation in positive terms. Specifically, we extract positive meaning from negation when the negation cue syntactically modifies a noun or adjective. Our approach is grounded on generating potential positive interpretations automatically, and then scoring them. Experimental results show that interpretations scored high can be reliably identified.