Ioana Hulpuș


2020

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Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)
Dmitry Ustalov | Swapna Somasundaran | Alexander Panchenko | Fragkiskos D. Malliaros | Ioana Hulpuș | Peter Jansen | Abhik Jana
Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)

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When Shallow is Good Enough: Automatic Assessment of Conceptual Text Complexity using Shallow Semantic Features
Sanja Stajner | Ioana Hulpuș
Proceedings of the 12th Language Resources and Evaluation Conference

According to psycholinguistic studies, the complexity of concepts used in a text and the relations between mentioned concepts play the most important role in text understanding and maintaining reader’s interest. However, the classical approaches to automatic assessment of text complexity, and their commercial applications, take into consideration mainly syntactic and lexical complexity. Recently, we introduced the task of automatic assessment of conceptual text complexity, proposing a set of graph-based deep semantic features using DBpedia as a proxy to human knowledge. Given that such graphs can be noisy, incomplete, and computationally expensive to deal with, in this paper, we propose the use of textual features and shallow semantic features that only require entity linking. We compare the results obtained with new features with those of the state-of-the-art deep semantic features on two tasks: (1) pairwise comparison of two versions of the same text; and (2) five-level classification of texts. We find that the shallow features achieve state-of-the-art results on both tasks, significantly outperforming performances of the deep semantic features on the five-level classification task. Interestingly, the combination of the shallow and deep semantic features lead to a significant improvement of the performances on that task.

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CoCo: A Tool for Automatically Assessing Conceptual Complexity of Texts
Sanja Stajner | Sergiu Nisioi | Ioana Hulpuș
Proceedings of the 12th Language Resources and Evaluation Conference

Traditional text complexity assessment usually takes into account only syntactic and lexical text complexity. The task of automatic assessment of conceptual text complexity, important for maintaining reader’s interest and text adaptation for struggling readers, has only been proposed recently. In this paper, we present CoCo - a tool for automatic assessment of conceptual text complexity, based on using the current state-of-the-art unsupervised approach. We make the code and API freely available for research purposes, and describe the code and the possibility for its personalization and adaptation in details. We compare the current implementation with the state of the art, discussing the influence of the choice of entity linker on the performances of the tool. Finally, we present results obtained on two widely used text simplification corpora, discussing the full potential of the tool.

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Unsupervised stance detection for arguments from consequences
Jonathan Kobbe | Ioana Hulpuș | Heiner Stuckenschmidt
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Social media platforms have become an essential venue for online deliberation where users discuss arguments, debate, and form opinions. In this paper, we propose an unsupervised method to detect the stance of argumentative claims with respect to a topic. Most related work focuses on topic-specific supervised models that need to be trained for every emergent debate topic. To address this limitation, we propose a topic independent approach that focuses on a frequently encountered class of arguments, specifically, on arguments from consequences. We do this by extracting the effects that claims refer to, and proposing a means for inferring if the effect is a good or bad consequence. Our experiments provide promising results that are comparable to, and in particular regards even outperform BERT. Furthermore, we publish a novel dataset of arguments relating to consequences, annotated with Amazon Mechanical Turk.

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Exploring Morality in Argumentation
Jonathan Kobbe | Ines Rehbein | Ioana Hulpuș | Heiner Stuckenschmidt
Proceedings of the 7th Workshop on Argument Mining

Sentiment and stance are two important concepts for the analysis of arguments. We propose to add another perspective to the analysis, namely moral sentiment. We argue that moral values are crucial for ideological debates and can thus add useful information for argument mining. In the paper, we present different models for automatically predicting moral sentiment in debates and evaluate them on a manually annotated testset. We then apply our models to investigate how moral values in arguments relate to argument quality, stance and audience reactions.

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Knowledge Graphs meet Moral Values
Ioana Hulpuș | Jonathan Kobbe | Heiner Stuckenschmidt | Graeme Hirst
Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics

Operationalizing morality is crucial for understanding multiple aspects of society that have moral values at their core – such as riots, mobilizing movements, public debates, etc. Moral Foundations Theory (MFT) has become one of the most adopted theories of morality partly due to its accompanying lexicon, the Moral Foundation Dictionary (MFD), which offers a base for computationally dealing with morality. In this work, we exploit the MFD in a novel direction by investigating how well moral values are captured by KGs. We explore three widely used KGs, and provide concept-level analogues for the MFD. Furthermore, we propose several Personalized PageRank variations in order to score all the concepts and entities in the KGs with respect to their relevance to the different moral values. Our promising results help to progress the operationalization of morality in both NLP and KG communities.

2019

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A Spreading Activation Framework for Tracking Conceptual Complexity of Texts
Ioana Hulpuș | Sanja Štajner | Heiner Stuckenschmidt
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We propose an unsupervised approach for assessing conceptual complexity of texts, based on spreading activation. Using DBpedia knowledge graph as a proxy to long-term memory, mentioned concepts become activated and trigger further activation as the text is sequentially traversed. Drawing inspiration from psycholinguistic theories of reading comprehension, we model memory processes such as semantic priming, sentence wrap-up, and forgetting. We show that our models capture various aspects of conceptual text complexity and significantly outperform current state of the art.