Understanding Negation in Positive Terms Using Syntactic Dependencies

Sarabi, Zahra; Blanco, Eduardo

doi:10.18653/v1/d16-1119

Cited by 7 publications

(10 citation statements)

References 28 publications

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“…Instead of choosing as focus the semantic role that is most prominently negated, they consider all roles and rank the likelihood of the underlying positive interpretations with a score ranging from 0 to 5. Sarabi and Blanco (2016) move away from semantic roles and work with syntactic dependencies. By selecting subtrees in dependency trees, they target foci of verbal negations—and the underlying positive interpretations—in a continuum of granularity.…”

Section: Annotated Corpora and Shared Tasksmentioning

confidence: 99%

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Recent advances in processing negation

Morante

Blanco

2020

Nat. Lang. Eng.

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Negation is a complex linguistic phenomenon present in all human languages. It can be seen as an operator that transforms an expression into another expression whose meaning is in some way opposed to the original expression. In this article, we survey previous work on negation with an emphasis on computational approaches. We start defining negation and two important concepts: scope and focus of negation. Then, we survey work in natural language processing that considers negation primarily as a means to improve the results in some task. We also provide information about corpora containing negation annotations in English and other languages, which usually include a combination of annotations of negation cues, scopes, foci, and negated events. We continue the survey with a description of automated approaches to process negation, ranging from early rule-based systems to systems built with traditional machine learning and neural networks. Finally, we conclude with some reflections on current progress and future directions.

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Section: Annotated Corpora and Shared Tasksmentioning

confidence: 99%

“…There are less efforts experimenting with the other corpora annotating focus of negation. Most use an SVM trained with lexical, syntactic, and semantic features (Blanco and Moldovan 2012; Blanco and Sarabi 2016; Sarabi and Blanco 2016; Sarabi and Blanco 2017). Matsuyoshi et al .…”

Section: Processing Negationmentioning

confidence: 99%

Recent advances in processing negation

Morante

Blanco

2020

Nat. Lang. Eng.

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“…Anand and Martell (2012) refine PB-FOC and argue that 27.4% of negations with a focus annotated in PB-FOC do not actually have a focus. Sarabi and Blanco (2016) present a complementary approach grounded on syntactic dependencies. All of these efforts identify the tokens that are the focus of negation.…”

Section: Previous Workmentioning

confidence: 99%

Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)

2019

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Recognizing affective states is essential for narrative text understanding and for applications such as conversational dialogue, summarization, and sarcasm recognition. Many tools have been developed to recognize explicit expressions of sentiment, but affective states can also be inferred from events. This talk will focus on "affective events", which are generally desirable or undesirable experiences that implicitly suggest an affective state for the experiencer. For example, buying a home is usually desirable and associated with a positive affective state, but being laid off is undesirable and associated with a negative state. First, we will describe a weakly supervised learning method to induce affective events from a text corpus by optimizing for semantic consistency. Second, we aim to characterize affective events based on Human Needs Categories, which often explain people's motivations, goals, and desires. We will present a co-training model for Human Needs categorization that uses an event expression classifier and an event context classifier to learn from both labeled and unlabeled texts.Bio: Ellen Riloff is a Professor in the School of Computing at the University of Utah. Her primary research area is natural language processing, with an emphasis on information extraction, affective text analysis, semantic class induction, and bootstrapping methods that learn from unannotated texts. Prof. Riloff has served as the General Chair for the EMNLP 2018 conference,

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“…The goal of this paper is therefore (i) to investigate how the PPIs of negative statements can be represented in AMRs and (ii) to investigate whether these structures can be generated from the AMR of the negated sentence in a systematic way based on the work by Blanco and Sarabi (2016) and Sarabi and Blanco (2016). To this end, we propose a novel, logically-motivated AMR structure that makes both scope and focus of negation explict.…”

Section: Introductionmentioning

confidence: 99%

Representing Implicit Positive Meaning of Negated Statements in AMR

Stein¹,

Donatelli²

2021

Proceedings of the Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop

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Meaning Representation (AMR) has become popular for representing the meaning of natural language in graph structures. However, AMR does not represent scope information, posing a problem for its overall expressivity and specifically for drawing inferences from negated statements. This is the case with so-called "positive interpretations" of negated statements, in which implicit positive meaning is identified by inferring the opposite of the negation's focus. In this work, we investigate how potential positive interpretations (PPIs) can be represented in AMR. We propose a logically motivated AMR structure for PPIs that makes the focus of negation explicit and sketch an initial proposal for a systematic methodology to generate this more expressive structure.

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Understanding Negation in Positive Terms Using Syntactic Dependencies

Cited by 7 publications

References 28 publications

Recent advances in processing negation

Recent advances in processing negation

Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)

Representing Implicit Positive Meaning of Negated Statements in AMR

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