Абстрактное представление смысла

Meaning Representation Jeffrey Flanigan Sam Thomson Jaime Carbonell Chris Dyer Noah A. Smith Language Technologies Institute Carnegie Mellon University Pittsburgh, PA 15213, USA {jflanigan,sthomson,jgc,cdyer,nasmith}@cs.cmu.edu Abstract Abstract Meaning Representation (AMR) is …...

AbstractAbstract Meaning Representations (AMRs) are rooted, directional and labeled graphs that abstract away from morpho-syntactic idiosyncrasies such as word category (verbs and nouns), word order, and function words (determiners, some …...

AbstractThis research focuses on expanding PropBank, a corpus annotated with predicate argument structures, with new predicate types; namely, noun, adjective and complex predicates, such as Light Verb Constructions. This effort …...

We present a two-stage framework to parse a sentence into its Abstract Meaning Repre- sentation (AMR). We first use a dependency parser to generate a dependency tree for the sentence. …...

Meaning Representation Using Syntax-Based Machine Translation Michael Pust, Ulf Hermjakob, Kevin Knight, Daniel Marcu, Jonathan May Information Sciences Institute Computer Science Department University of Southern California {pust, ulf, knight, marcu, …...

Meaning Representation Xiaoman Pan1, Taylor Cassidy2, Ulf Hermjakob3, Heng Ji1, Kevin Knight3 1Computer Science Department, Rensselaer Polytechnic Institute {panx2,jih}@rpi.edu 2IBM Research & Army Research Laboratory taylor.cassidy.civ@mail.mil 3Information Sciences Institute, University …...

To alleviate the error propagation in the tra- ditional pipelined models for Abstract Mean- ing Representation (AMR) parsing, we for- mulate AMR parsing as a joint task that per- forms …...

Meaning Representation using Learning to Search Sudha Rao1,3∗, Yogarshi Vyas1,3∗, Hal Daum´e III1,3, Philip Resnik2,3 1Computer Science, 2Linguistics, 3UMIACS University of Maryland {raosudha,yogarshi,hal}@cs.umd.edu, resnik@umd.edu Abstract In this paper we describe …...

Meaning Representation using Tree Transducers Jeffrey Flanigan♠ Chris Dyer♠ Noah A. Smith♥ Jaime Carbonell♠ ♠School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA ♥Computer Science & Engineering, University of …...

We describe our submission system to the SemEval-2016 Task 8 on Abstract Meaning Representation (AMR) Parsing. We attempt to improve AMR parsing by exploiting prepo- sition semantic role labeling information …...

We propose a brand new “Liberal” Event Extraction paradigm to extract events and discover event schemas from any input corpus simultaneously. We incorporate symbolic (e.g., Abstract Meaning Repre- sentation) and …...

Meaning Representation Sho Takase† Jun Suzuki‡ Naoaki Okazaki† Tsutomu Hirao‡ Masaaki Nagata‡ Graduate School of Information Sciences, Tohoku University† NTT Communication Science Laboratories, NTT Corporation‡ {takase, okazaki}@ecei.tohoku.ac.jp {suzuki.jun, hirao.tsutomu, nagata.masaaki}@lab.ntt.co.jp …...

Meaning Representation parsing James Goodman∗ Andreas Vlachos† Jason Naradowsky∗ ∗ Computer Science Department, University College London james@janigo.co.uk, jason.narad@gmail.com † Department of Computer Science, University of Sheffield a.vlachos@sheffield.ac.uk Abstract Semantic parsers …...

AbstractWe present a system which parses sentences into Abstract Meaning Representations, improving state-of-the-art results for this task by more than 5%. AMR graphs represent semantic content using linguistic properties such …...

AbstractThis paper describes our approach to the SemEval-2017 “Semantic Textual Similarity” and “Multilingual Word Similarity” tasks. In the former, we test our approach in both English and Spanish, and use …...

AbstractSequence-to-sequence models have shown strong performance across a broad range of applications. However, their application to parsing and generating text using Abstract Meaning Representation (AMR) has been limited, due to …...

AbstractParsing sentences to linguistically-expressive semantic representations is a key goal of Natural Language Processing. Yet statistical parsing has focussed almost exclusively on bilexical dependencies or domain-specific logical forms. We propose …...

AbstractAbstract Meaning Representation (AMR) parsing aims at abstracting away from the syntactic realization of a sentence, and denote only its meaning in a canonical form. As such, it is ideal …...

Meaning Representation of Constructions: The More We Include, the Better the Representation Claire Bonial,1 Bianca Badarau,2 Kira Griffitt,3 Ulf Hermjakob,4 Kevin Knight,4 Tim O’Gorman,5 Martha Palmer,5 Nathan Schneider6 1U.S. Army …...

AbstractThere are few corpora that endeavor to represent the semantic content of entire documents. We present a corpus that accomplishes one way of capturing document level semantics, by annotating coreference …...

Meaning Representations for Spanish Noelia Migueles-Abraira, Rodrigo Agerri, Arantza Diaz de Ilarraza IXA NLP Group, University of the Basque Country UPV/EHU noeliamigueles@gmail.com,{rodrigo.agerri,a.diazdeilarraza}@ehu.eus Abstract Until recently, semantic annotations for different semantic …...

AbstractAbstract Meaning Representation (AMR) research has mostly focused on English. We show that it is possible to use AMR annotations for English as a semantic representation for sentences written in …...

AbstractAbstract Meaning Representation (AMR) represents sentences as directed, acyclic and rooted graphs, aiming at capturing their meaning in a machine readable format. AMR parsing converts natural language sentences into such …...

AbstractGenerating from Abstract Meaning Representation (AMR) is an underspecified problem, as many syntactic decisions are not specified by the semantic graph. To explicitly account for this variation, we break down …...

AbstractWe propose neural models to generate high-quality text from structured representations based on Minimal Recursion Semantics (MRS). MRS is a rich semantic representation that encodes more precise semantic detail than …...

AbstractMost current state-of-the art systems for generating English text from Abstract Meaning Representation (AMR) have been evaluated only using automated metrics, such as BLEU, which are known to be problematic …...

AbstractAbstract meaning representation (AMR)-to-text generation is the challenging task of generating natural language texts from AMR graphs, where nodes represent concepts and edges denote relations. The current state-of-the-art methods use …...

AbstractThis paper describes a schema that enriches Abstract Meaning Representation (AMR) in order to provide a semantic representation for facilitating Natural Language Understanding (NLU) in dialogue systems. AMR offers a …...

AbstractSpatial Reasoning from language is essential for natural language understanding. Supporting it requires a representation scheme that can capture spatial phenomena encountered in language as well as in images and …...

AbstractCommonsenseQA is a task in which a correct answer is predicted through commonsense reasoning with pre-defined knowledge. Most previous works have aimed to improve the performance with distributed representation without …...

AbstractGenerating text from structured data is challenging because it requires bridging the gap between (i) structure and natural language (NL) and (ii) semantically underspecified input and fully specified NL output. …...

AbstractAbstract Meaning Representation (AMR) is a graph-based semantic formalism where the nodes are concepts and edges are relations among them. Most of AMR parsing methods require alignment between the nodes …...

AbstractThis paper presents an expansion to the Abstract Meaning Representation (AMR) annotation schema that captures fine-grained semantically and pragmatically derived spatial information in grounded corpora. We describe a new lexical …...

AbstractAbstract Meaning Representation (AMR) parsing aims at converting sentences into AMR representations. These are graphs and not trees because AMR supports reentrancies (nodes with more than one parent). Following previous …...

AbstractModeling the parser state is key to good performance in transition-based parsing. Recurrent Neural Networks considerably improved the performance of transition-based systems by modelling the global state, e.g. stack-LSTM parsers, …...

AbstractAbstract Meaning Representation (AMR) is a popular formalism of natural language that represents the meaning of a sentence as a semantic graph. It is agnostic about how to derive meanings …...

AbstractWe develop high performance multilingual Abstract Meaning Representation (AMR) systems by projecting English AMR annotations to other languages with weak supervision. We achieve this goal by bootstrapping transformer-based multilingual word …...

AbstractAbstract Meaning Representation (AMR) is a sentence-level meaning representation based on predicate argument structure. One of the challenges we find in AMR parsing is to capture the structure of complex …...

AbstractAlthough parsing to Abstract Meaning Representation (AMR) has become very popular and AMR has been shown effective on the many sentence-level downstream tasks, little work has studied how to generate …...

AbstractBiomedical Information Extraction from scientific literature presents two unique and non-trivial challenges. First, compared with general natural language texts, sentences from scientific papers usually possess wider contexts between knowledge elements. …...

With the massive success of stochastic lan- guage models, some claim that the symbolic approach is not necessary for natural language processing. However, recent studies made use of linguistically motivated …...

AbstractAMR (Abstract Meaning Representation) and EDS (Elementary Dependency Structures) are two popular meaning representations in NLP/NLU. AMR is more abstract and conceptual, while EDS is more low level, closer to …...

Meaning Representation for Knowledge Base Question Answering Pavan Kapanipathi∗, Ibrahim Abdelaziz∗, Srinivas Ravishankar∗, Salim Roukos, Alexander Gray, Ramon Astudillo, Maria Chang, Cristina Cornelio, Saswati Dana, Achille Fokoue, Dinesh Garg, Alfio …...

AbstractWe present algorithms for aligning components of Abstract Meaning Representation (AMR) graphs to spans in English sentences. We leverage unsupervised learning in combination with heuristics, taking the best of both …...

AbstractAlthough neural models have achieved competitive results in dialogue systems, they have shown limited ability in representing core semantics, such as ignoring important entities. To this end, we exploit Abstract …...

AbstractPredicting linearized Abstract Meaning Representation (AMR) graphs using pre-trained sequence-to-sequence Transformer models has recently led to large improvements on AMR parsing benchmarks. These parsers are simple and avoid explicit modeling …...

AbstractSystems that generate natural language text from abstract meaning representations such as AMR are typically evaluated using automatic surface matching metrics that compare the generated texts to reference texts from …...

AbstractThis paper presents Gesture AMR, an extension to Abstract Meaning Representation (AMR), that captures the meaning of gesture. In developing Gesture AMR, we consider how gesture form and meaning relate; …...

AbstractAbstract Meaning Representation (AMR) is a semantic representation for NLP/NLU. In this paper, we propose to use it for data augmentation in NLP. Our proposed data augmentation technique, called AMR-DA, …...

AbstractAutomatic evaluation metrics are essential for the rapid development of open-domain dialogue systems as they facilitate hyper-parameter tuning and comparison between models. Although recently proposed trainable conversation-level metrics have shown …...

AbstractDespite extensive research on parsing of English sentences into Abstract Meaning Representation (AMR) graphs, which are compared to gold graphs via the Smatch metric, full-document parsing into a unified graph …...

AbstractTransition-based parsers for Abstract Meaning Representation (AMR) rely on node-to-word alignments. These alignments are learned separately from parser training and require a complex pipeline of rule-based components, pre-processing, and post-processing …...

AbstractThe recent transition to the online educational domain has increased the need for Automatic Short Answer Grading (ASAG). ASAG automatically evaluates a student’s response against a (given) correct response and …...

AbstractWe introduce a new method to improve existing multilingual sentence embeddings with Abstract Meaning Representation (AMR). Compared with the original textual input, AMR is a structured semantic representation that presents …...

AbstractModels based on large-pretrained language models, such as S(entence)BERT, provide effective and efficient sentence embeddings that show high correlation to human similarity ratings, but lack interpretability. On the other hand, …...

AbstractPre-trained language models have made great progress on dialogue tasks. However, these models are typically trained on surface dialogue text, thus are proven to be weak in understanding the main …...

AbstractUnavailability of parallel corpora for training text style transfer (TST) models is a very challenging yet common scenario. Also, TST models implicitly need to preserve the content while transforming a …...

AbstractCommonly found in academic and formal texts, a nominalization uses a deverbal noun to describe an event associated with its corresponding verb. Nominalizations can be difficult to interpret because of …...

AbstractThe success of scene graphs for visual scene understanding has brought attention to the benefits of abstracting a visual input (e.g., image) into a structured representation, where entities (people and …...

AbstractEvent argument extraction (EAE) identifies event arguments and their specific roles for a given event. Recent advancement in generation-based EAE models has shown great performance and generalizability over classification-based models. …...

AbstractAspect-based sentiment analysis (ABSA) is a fine-grained sentiment classification task. Many recent works have used dependency trees to extract the relation between aspects and contexts and have achieved significant improvements. …...

AbstractRecent works have introduced Abstract Meaning Representation (AMR) for Document-level Event Argument Extraction (Doc-level EAE), since AMR provides a useful interpretation of complex semantic structures and helps to capture long-distance …...

AbstractText generation from Abstract Meaning Representation (AMR) has substantially benefited from the popularized Pretrained Language Models (PLMs). Myriad approaches have linearized the input graph as a sequence of tokens to …...