Thirty Musts for Meaning Banking

Abzianidze, Lasha; Bos, Johan

doi:10.18653/v1/w19-3302

Cited by 5 publications

(3 citation statements)

References 49 publications

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“…In special, sentence-level semantics, which is the focus of this dissertation, has gained the attention of the NLP community, since it may be used in many NLP applications, such as text generation, automatic summarization, machine translation, and others. These tasks need a deeper understanding of the text to produce results more similar way to how humans do (ABEND; RAPPOPORT, 2017;ABZIANIDZE;BOS, 2019).…”

Section: Introductionmentioning

confidence: 99%

Abstract Meaning Representation Parsing for the Brazilian Portuguese Language

Anchiêta

Pardo

2022

Lecture Notes in Computer Science

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Semântica computacional é a área encarregada de estudar possíveis representações semânticas, ou seja, formalismos semânticos que são computacionalmente viáveis para representar expressões da língua humana. Esses formalismos desempenham um papel importante para o entendimento de uma língua natural, capturando o significado de expressões linguísticas. Além disso, eles são o principal ingrediente para desenvolver analisadores semânticos, que são responsáveis por mapear sentenças de uma língua natural em uma representação semântica computacionalmente tratável. Com o objetivo de representar e entender características semânticas de uma língua natural e, com isso, desenvolver ferramentas computacionais que produzam resultados mais próximos aos dos humanos, diversos formalismos semânticos foram propostos, como: Universal Networking Language (UNL), Universal Conceptual Cognitive Annotation, (UCCA), Abstract Meaning Representation (AMR), entre outros. Em especial, Abstract Meaning Representation (AMR) é um formalismo semântico baseado em grafo direcionado que possui única raiz com nós e arestas rotulados. Os nós representam conceitos (que podem ser as palavras de uma sentença), as arestas representam relações semânticas entre os conceitos e os nós não possuem alinhamento explícito com as palavras da sentença. AMR compreende algumas caractetísticas semânticas como: entidades nomeadas, correferência, papéis semânticos, desambiguação lexical, entre outras. Neste trabalho, focou-se na representação AMR para a língua portuguesa, pois ela possui uma estrutura mais fácil de produzir do que outras representações semânticas. Dessa forma, anotou-se o livro do Pequeno Príncipe, que é primeiro corpus anotado nesse formalismo para a língua portuguesa e desenvolveu-se o primeiro analisador semântico para essa representação. Além disso, adaptou-se alguns métodos de análise semântica da língua inglesa para a língua portuguesa. Mais do que isso, desenvolveu-se um novo método de alinhamento entre as palavras da sentença e os nós do grafo que melhora os resultados dos analisadores semânticos adaptados e um novo método de avaliação entre grafos AMRs que é mais robusto, rápido e justo do que a métrica tradicional de avaliação. Por fim, utilizou-se esses métodos em uma tarefa de detecção de paráfrase, combinando tanto características semânticas implícitas quanto explícitas para classificar se uma sentença é paráfrase de outra.

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Section: Introductionmentioning

confidence: 99%

Abstract Meaning Representation Parsing for the Brazilian Portuguese Language

Anchiêta

Pardo

2022

Lecture Notes in Computer Science

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“…The adoption of PD methods by NLP will help to alleviate issues concerning the development of more democratic, fairer, less-biased technologies to process natural language data. This short paper is the outcome of an ongoing dialogue between designers and NLP experts and adopts a non-standard format following previous work by Traum (2000); Bender (2013); Abzianidze and Bos (2019). Every section is a guiding principle.…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the 1st Workshop on NLP for Positive Impact

2021

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Amidst rising mental health needs in society, virtual agents are increasingly deployed in counselling. In order to give pertinent advice, counsellors must first gain an understanding of the issues at hand by eliciting sharing from the counsellee. It is thus important for the counsellor chatbot to encourage the user to open up and talk. One way to sustain the conversation flow is to acknowledge the counsellee's key points by restating them, or probing them further with questions. This paper applies models from two closely related NLP tasks -summarization and question generation -to restatement and question generation in the counselling context. We conducted experiments on a manually annotated dataset of Cantonese post-reply pairs on topics related to loneliness, academic anxiety and test anxiety. We obtained the best performance in both restatement and question generation by finetuning BertSum, a state-of-the-art summarization model, with the in-domain manual dataset augmented with a large-scale, automatically mined open-domain dataset.

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“…An important application for meaning representations are inference tasks that play a crucial role in many NLP tasks such as information extraction and question answering (Abzianidze and Bos, 2019). Such inference tasks are where AMR often falls short due to its lack of precise scope representation.…”

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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Thirty Musts for Meaning Banking

Cited by 5 publications

References 49 publications

Abstract Meaning Representation Parsing for the Brazilian Portuguese Language

Abstract Meaning Representation Parsing for the Brazilian Portuguese Language

Proceedings of the 1st Workshop on NLP for Positive Impact

Representing Implicit Positive Meaning of Negated Statements in AMR

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