Семантическая разметка ролей

This paper presents our work on Semantic Role Labeling using a Transformation-Based Error- Driven approach in the style of Eric Brill (Brill, 1995). Our approach achieved an overall F1 score …...

In this paper, a novel semantic role labeler based on dependency trees is developed. This is ac- complished by formulating the semantic role la- beling as a classification problem of …...

In this paper, we propose a semantic role label- ing method using a maximum entropy model, which enables not only to exploit rich features but also to alleviate the data …...

In this study, we try to apply SVMs to the se- mantic role labeling task, which is one of the multiclass problems. As a result, we propose a two-phase semantic …...

We present a semantic role labeling sys- tem submitted to the closed track of the CoNLL-2005 shared task. The system, in- troduced in (Toutanova et al., 2005), im- plements a …...

This paper focuses on semantic role la- beling using automatically-generated syn- tactic information. A simple and robust strategy for system combination is pre- sented, which allows to partially recover from …...

Despite much recent progress on accu- rate semantic role labeling, previous work has largely used independent classifiers, possibly combined with separate label se- quence models via Viterbi decoding. This stands …...

This paper describes a semantic role la- beling system that uses features derived from different syntactic views, and com- bines them within a phrase-based chunk- ing paradigm. For an input …...

A maximum entropy classifier is used in our semantic role labeling system, which takes syntactic constituents as the labeling units. The maximum entropy classifier is trained to identify and classify …...

Semantic role labeling is the process of annotating the predicate-argument struc- ture in text with semantic labels. In this paper we present a state-of-the-art base- line semantic role labeling system …...

Our system for semantic role labeling is multi-stage in nature, being based on tree pruning techniques, statistical methods for lexicalised feature encoding, and a C4.5 decision tree classifier. We use …...

We describe a system for the CoNLL- 2005 shared task of Semantic Role Label- ing. The system implements a two-layer architecture to first identify the arguments and then to label …...

In this paper, we describe our systems for the CoNLL-2005 shared task. The aim of the task is semantic role labeling using a machine-learning algorithm. We apply the Support Vector …...

This paper describes a system for semantic role labeling for the CoNLL2005 Shared task. We divide the task into two sub-tasks: boundary recognition by a general tree- based predicate-argument recognition …...

In this paper we apply conditional random fields (CRFs) to the semantic role labelling task. We define a random field over the structure of each sentence’s syntactic parse tree. For …...

We present an application of Sparse Bayesian Learning to the task of semantic role labeling, and we demonstrate that this method produces smaller classifiers than the popular Support Vector approach. …...

This paper describes a system for the CoNLL-2005 Shared Task on Semantic Role Labeling. We trained two parsers with the training corpus in which the se- mantic argument information is …...

We present an approach for the joint ex- traction of entities and relations in the con- text of opinion recognition and analysis. We identify two types of opinion-related entities — …...

This paper describes our attempt NomBank-based automatic Semantic Roleat Labeling (SRL). NomBank is a project at New York University to annotate the ar- gument structures for common nouns in the …...

Recent work on semantic role labeling (SRL) has focused almost exclusively on the analysis of the predicate-argument structure of verbs, largely due to the lack of human-annotated resources for other …...

This paper demonstrates two methods to improve the performance of instance- based learning (IBL) algorithms for the problem of Semantic Role Labeling (SRL). Two IBL algorithms are utilized: k-Nearest Neighbor …...

Recent work on Semantic Role Labeling (SRL) has shown that to achieve high accuracy a joint inference on the whole predicate argument structure should be ap- plied. In this paper, …...

The end-to-end performance of natural language processing systems for com- pound tasks, such as question answering and textual entailment, is often hampered by use of a greedy 1-best pipeline archi- …...

We propose a new, simple model for the auto- matic induction of selectional preferences, using corpus-based semantic similarity metrics. Fo- cusing on the task of semantic role labeling, we compute …...

PropBank has been widely used as train- ing data for Semantic Role Labeling. However, because this training data is taken from the WSJ, the resulting machine learning models tend to …...

This paper proposes the use of Lexical- ized Tree-Adjoining Grammar (LTAG) for- malism as an important additional source of features for the Semantic Role Labeling (SRL) task. Using a set …...

We describe a novel neural network archi- tecture for the problem of semantic role la- beling. Many current solutions are compli- cated, consist of several stages and hand- built features, …...

In this paper we present a semantic role la- beling system submitted to the task Multi- level Semantic Annotation of Catalan and Spanish in the context of SemEval–2007. The core …...

This paper presents a novel application of Alternating Structure Optimization (ASO) to the task of Semantic Role Labeling (SRL) of noun predicates in NomBank. ASO is a recently proposed linear …...

We present a simple, two-steps supervised strategy for the identification and classifica- tion of thematic roles in natural language texts. We employ no external source of in- formation but automatic …...

This paper describes our experience in preparing the data and evaluating the results for three subtasks of SemEval-2007 Task-17 – Lexical Sample, Semantic Role Labeling (SRL) and All-Words respectively. We …...

Most research on semantic role labeling (SRL) has been focused on training and evaluating on the same corpus in order to develop the technology. This strategy, while appropriate for initiating …...

We present a sequential Semantic Role La- beling system that describes the tagging problem as a Maximum Entropy Markov Model. The system uses full syntactic in- formation to select BIO-tokens …...

This paper describes our system for syn- tactic and semantic dependency parsing to participate the shared task of CoNLL- 2008. We use a pipeline approach, in which syntactic dependency parsing, …...

Pictorial communication systems convert natural language text into pictures to as- sist people with limited literacy. We define a novel and challenging problem: picture layout optimization. Given an input sen- …...

In this paper we present our syntactic and semantic dependency parsing system par- ticipated in both closed and open compe- titions of the CoNLL 2008 Shared Task. By combining the …...

In recent years, with the development of Chi- nese semantically annotated corpus, such as Chinese Proposition Bank and Normalization Bank, the Chinese semantic role labeling (SRL) task has been boosted. …...

In Semantic Role Labeling (SRL), argu- ments are usually limited in a syntax sub- tree. It is reasonable to label arguments lo- cally in such a sub-tree rather than a …...

In this paper, we report experiments that explore learning of syntactic and seman- tic representations. First, we extend a state-of-the-art statistical parser to pro- duce a richly annotated tree that …...

There is a widely held belief in the natural lan- guage and computational linguistics commu- nities that Semantic Role Labeling (SRL) is a significant step toward improving important applications, e.g. …...

Parse-tree paths are commonly used to incor- porate information from syntactic parses into NLP systems. These systems typically treat the paths as atomic (or nearly atomic) features; these features are …...

Almost all automatic semantic role label- ing (SRL) systems rely on a preliminary parsing step that derives a syntactic struc- ture from the sentence being analyzed. This makes the choice …...

This paper describes a system to solve the joint learning of syntactic and seman- tic dependencies. An directed graphical model is put forward to integrate depen- dency relation classification and …...

Most existing systems for Chinese Seman- tic Role Labeling (SRL) make use of full syntactic parses. In this paper, we evalu- ate SRL methods that take partial parses as inputs. …...

We present a comparison between two systems for establishing syntactic and semantic depen- dencies: one that performs dependency parsing and semantic role labeling as a single task, and another that …...

The combination of Support Vector Machines with very high dimensional kernels, such as string or tree kernels, suffers from two ma- jor drawbacks: first, the implicit representa- tion of feature …...

The sense of a preposition is related to the semantics of its dominating prepositional phrase. Knowing the sense of a prepo- sition could help to correctly classify the semantic role …...

We present a framework to extract the most important features (tree fragments) from a Tree Kernel (TK) space according to their importance in the target kernel- based machine, e.g. Support …...

We present an integrated dependency- based semantic role labeling system for English from both NomBank and Prop- Bank. By introducing assistant argument labels and considering much more fea- ture templates, …...

Large scale annotated corpora are pre- requisite to developing high-performance semantic role labeling systems. Unfor- tunately, such corpora are expensive to produce, limited in size, and may not be representative. …...

. Extracting semantic roles is one of the major steps in representing text meaning. It refers to finding the semantic relations between a predicate and syntactic constituents in a sentence. …...

AbstractIn this paper we present a new FrameNet-based Shallow Semantic Parser. Shallow Semantic Parsing has been a popular Natural Language Processing task since the 2004 and 2005 CoNLL Shared Task …...

Many Semantic Role Labeling (SRL) combination strategies have been pro- posed and tested on English SRL task. But little is known about how much Chi- nese SRL can benefit from …...

This paper proposes a convolution forest ker- nel to effectively explore rich structured fea- tures embedded in a packed parse forest. As opposed to the convolution tree kernel, the proposed …...

AbstractWe present the methodology that underlies mew metrics for semantic machine translation evaluation we are developing. Unlike widely-used lexical and n-gram based MT evaluation metrics, the aim of semantic MT …...

AbstractSemantic Role Labeling cannot be performed without an associated linguistic resource. A key resource for such a task is the FrameNet resource based on Fillmore’s theory of frame semantics. Like …...

The core-adjunct argument distinction is a basic one in the theory of argument struc- ture. The task of distinguishing between the two has strong relations to various ba- sic NLP …...

We show that jointly performing semantic role labeling (SRL) on bitext can improve SRL results on both sides. In our approach, we use monolingual SRL systems to produce ar- gument …...

This paper explores joint syntactic and seman- tic parsing of Chinese to further improve the performance of both syntactic and semantic parsing, in particular the performance of se- mantic parsing …...

Semantic role labeling (SRL) and word sense disambiguation (WSD) are two fun- damental tasks in natural language pro- cessing to find a sentence-level seman- tic representation. To date, they have …...

In this paper, we provide a theoretical framework for feature selection in tree ker- nel spaces based on gradient-vector com- ponents of kernel-based machines. We show that a huge number …...

Most supervised language processing sys- tems show a significant drop-off in per- formance when they are tested on text that comes from a domain significantly different from the domain of …...

A fundamental step in sentence compre- hension involves assigning semantic roles to sentence constituents. To accomplish this, the listener must parse the sentence, find constituents that are candidate argu- ments, …...

Current Semantic Role Labeling technolo- gies are based on inductive algorithms trained over large scale repositories of annotated examples. Frame-based sys- tems currently make use of the FrameNet database but …...

This paper presents a model that extends se- mantic role labeling. Existing approaches in- dependently analyze relations expressed by verb predicates or those expressed as nominal- izations. However, sentences express …...

This paper describes the statistical machine translation system submitted to the WMT11 Featured Translation Task, which involves translating Haitian Creole SMS messages into English. In our experiments we try to …...

We present an approach for Seman- tic Role Labeling (SRL) using Condi- tional Random Fields in a joint identifi- cation/classification step. The approach is based on shallow syntactic information (chunks) …...

We present a novel method for FrameNet- based semantic role labeling (SRL), fo- cusing on limitations posed by the limited coverage of available annotated data. Our SRL model is based …...

We introduce two Bayesian models for un- supervised semantic role labeling (SRL) task. The models treat SRL as clustering of syntactic signatures of arguments with clusters corresponding to semantic roles. …...

Many NLP tasks make predictions that are in- herently coupled to syntactic relations, but for many languages the resources required to pro- vide such syntactic annotations are unavail- able. For …...

Machine translation aims to generate a target sentence that is semantically equivalent to the source sentence. However, most of current statistical machine translation models do not model the semantics of …...

This paper deals with the problem of predict- ing structures in the context of NLP. Typically, in structured prediction, an inference proce- dure is applied to each example independently of …...

In this paper, we propose a novel semantic cohesion model. Our model utilizes the predicate- argument structures as soft constraints and plays the role as a reordering model in the …...

AbstractWe present the first results on semantic role labeling using the Swedish FrameNet, which is a lexical resource currently in development. Several aspects of the task are investigated, including the …...

Reducing the reliance of semantic role labeling (SRL) methods on human-annotated data has become an active area of research. However, the prior work has largely focused on either (1) looking …...

AbstractWhen training semantic role labeling systems, the syntax of example sentences is of particular importance. Unfortunately, for the FrameNet annotated sentences, there is no standard parsed version. The integration of …...

We present a method for learning syntax- semantics mappings for verbs from unanno- tated corpora. We learn linkings, i.e., map- pings from the syntactic arguments and ad- juncts of a …...

We present an approach which uses the sim- ilarity in semantic structure of bilingual par- allel sentences to bootstrap a pair of seman- tic role labeling (SRL) models. The setting …...

This short paper presents a pilot study in- vestigating the training of a standard Seman- tic Role Labeling (SRL) system on product reviews for the new task of detecting com- …...

Coreference resolution plays a critical role in discourse analysis. This paper focuses on exploiting zero pronouns to improve Chi- nese coreference resolution. In particular, a simplified semantic role labeling framework …...

We present a method for automatically generating input parsers from English specifications of input file formats. We use a Bayesian generative model to cap- ture relevant natural language phenomena and …...

We introduce a Semantic Role Labeling (SRL) parser that finds semantic roles for a predicate together with the syntactic paths linking predicates and arguments. Our main contribution is to formulate …...

State-of-the-art semantic role labelling systems require large annotated corpora to achieve full performance. Unfortunately, such corpora are expensive to produce and often do not generalize well across do- mains. Even …...

AbstractIn this paper, we present the first Semantic Role Labeling system developed for Basque. The system is implemented using machine learning techniques and trained with the Reference Corpus for the …...

We explore the extent to which high- resource manual annotations such as tree- banks are necessary for the task of se- mantic role labeling (SRL). We examine how performance changes …...

The current approaches to Semantic Role Labeling (SRL) usually perform role clas- sification for each predicate separately and the interaction among individual predi- cate’s role labeling is ignored if there …...

AbstractWe present analyses showing that HMEANT is a reliable, accurate and fine-grained semantic frame based human MT evaluation metric with high inter-annotator agreement (IAA) and correlation with human adequacy judgments, …...

This paper presents predicate-argument structure analysis (PASA) for dialogue systems in Japanese. Conventional PASA and semantic role labeling have been applied to newspaper arti- cles. Because pronominalization and ellipses frequently …...

Semantic role labeling has become a key mod- ule for many language processing applica- tions such as question answering, information extraction, sentiment analysis, and machine translation. To build an unrestricted …...

We describe a system for semantic role label- ing adapted to a dependency parsing frame- work. Verb arguments are predicted over nodes in a dependency parse tree instead of nodes …...

AbstractFrame semantic representations have been useful in several applications ranging from text-to-scene generation, to question answering and social network analysis. Predicting such representations from raw text is, however, a challenging …...

We present a new approach for unsuper- vised semantic role labeling that lever- ages distributed representations. We in- duce embeddings to represent a predi- cate, its arguments and their complex …...

AbstractWe present a dynamic programming algorithm for efficient constrained inference in semantic role labeling. The algorithm tractably captures a majority of the structural constraints examined by prior work in this …...

This paper introduces a tensor-based approach to semantic role labeling (SRL). The motiva- tion behind the approach is to automatically induce a compact feature representation for words and their relations, …...

We show that applying semantic role label constraints to bracketing ITG alignment to train MT systems improves the quality of MT output in comparison to the conventional BITG and GIZA …...

We present a new method for semantic role labeling in which arguments and seman- tic roles are jointly embedded in a shared vector space for a given predicate. These embeddings …...

We introduce a new approach to unsupervised estimation of feature-rich semantic role la- beling models. Our model consists of two components: (1) an encoding component: a semantic role labeling model …...

AbstractThis paper describes a unified neural architecture for identifying and classifying multi-typed semantic relations between words in a sentence. We investigate two typical and well-studied tasks: semantic role labeling (SRL) …...

An important aspect of natural language un- derstanding involves recognizing and catego- rizing events and the relations among them. However, these tasks are quite subtle and an- notating training data …...

AbstractWe present a successful collaboration of word embeddings and co-training to tackle in the most difficult test case of semantic role labeling: predicting out-of-domain and unseen semantic frames. Despite the …...

Semantic Role Labeling (SRL) captures se- mantic roles (or participants) such as agent, patient, and theme associated with verbs from the text. While it provides important in- termediate semantic representations …...

AbstractSemantic role labeling (SRL) is the task of identifying and labeling predicate-argument structures in sentences with semantic frame and role labels. A known challenge in SRL is the large number …...

AbstractText analysis methods widely used in digital humanities often involve word co-occurrence, e.g. concept co-occurrence networks. These methods provide a useful corpus overview, but cannot determine the predicates that relate …...

AbstractSemantic Role Labeling (SRL) is the task of identifying the predicate-argument structure in sentences with semantic frame and role labels. For the English language, the Proposition Bank provides both a …...

AbstractThis paper describes the procedure of semantic role labeling and the development of the first manually annotated Persian Proposition Bank (PerPB) which added a layer of predicate-argument information to the …...

AbstractSpeech data currently receives a growing attention and is an important source of information. We still lack suitable corpora of transcribed speech annotated with semantic roles that can be used …...

. VerbNet is a lexical resource for English verbs that has proven useful for NLP thanks to its high lexical and syntactic coverage and its systematic coding of thematic roles. …...

AbstractCrowdsourcing has proven to be an effective method for generating labeled data for a range of NLP tasks. However, multiple recent attempts of using crowdsourcing to generate gold-labeled training data …...

AbstractWe introduce a new deep learning model for semantic role labeling (SRL) that significantly improves the state of the art, along with detailed analyses to reveal its strengths and limitations. …...

AbstractSemantic role labeling (SRL) is the task of identifying the predicate-argument structure of a sentence. It is typically regarded as an important step in the standard NLP pipeline. As the …...

AbstractThis paper presents a method for applying automatically acquired knowledge to semantic role labeling (SRL). We use a large amount of automatically extracted knowledge to improve the performance of SRL. …...

AbstractFrame-semantic parsing and semantic role labelling, that aim to automatically assign semantic roles to arguments of verbs in a sentence, have become an active strand of research in NLP. However, …...

AbstractSemantic role labeling (SRL) is to recognize the predicate-argument structure of a sentence, including subtasks of predicate disambiguation and argument labeling. Previous studies usually formulate the entire SRL problem into …...

AbstractAnimacy is a necessary property for a referent to be an agent, and thus animacy detection is useful for a variety of natural language processing tasks, including word sense disambiguation, …...

AbstractCommonsense, real-world knowledge about the events that entities or “things in the world” are typically involved in, as well as part-whole relationships, is valuable for allowing computational systems to draw …...

AbstractThis paper describes the augmentation of an existing corpus of child-directed speech. The resulting corpus is a gold-standard labeled corpus for supervised learning of semantic role labels in adult-child dialogues. …...

In this paper we proposed the use of effective features and transfer leaning to improve the accuracies of neural-network-based models for accurate semantic role labeling (SRL) of Japanese, which is …...

Semantic Role Labeling (SRL) has been ex- tensively studied, mostly for understanding English formal language. However, only a few reports exist for informal conversational text, especially for language being used …...

This paper describes a project for constructing FrameNet annotations in Korean over the KAIST treebank corpus to scale up the Korean FrameNet resource. Annotating FrameNet over raw sentences is an …...

AbstractPrevious attempts at injecting semantic frame biases into SMT training for low resource languages failed because either (a) no semantic parser is available for the low resource input language; or …...

AbstractFor over a decade, machine learning has been used to extract opinion-holder-target structures from text to answer the question “Who expressed what kind of sentiment towards what?”. Recent neural approaches …...

AbstractWe present data and methods that enable a supervised learning approach to Open Information Extraction (Open IE). Central to the approach is a novel formulation of Open IE as a …...

In this paper, we describe the Circumstantial Event Ontology (CEO), a newly developed ontology for calamity events that models semantic circumstantial relations between event classes, where we define circumstantial as …...

AbstractSemantic role labeling (SRL) aims to identify the predicate-argument structure of a sentence. Inspired by the strong correlation between syntax and semantics, previous works pay much attention to improve SRL …...

AbstractSemantic role labeling (SRL) is an important task for understanding natural languages, where the objective is to analyse propositions expressed by the verb and to identify each word that bears …...

AbstractSemantic role labeling (SRL) involves extracting propositions (i.e. predicates and their typed arguments) from natural language sentences. State-of-the-art SRL models rely on powerful encoders (e.g., LSTMs) and do not model …...

AbstractThere are many different ways in which external information might be used in a NLP task. This paper investigates how external syntactic information can be used most effectively in the …...

AbstractWe introduce Rosita, a method to produce multilingual contextual word representations by training a single language model on text from multiple languages. Our method combines the advantages of contextual word …...

AbstractSemantic role labeling (SRL) is a task to recognize all the predicate-argument pairs of a sentence, which has been in a performance improvement bottleneck after a series of latest works …...

AbstractWe build the first full pipeline for semantic role labelling of Russian texts. The pipeline implements predicate identification, argument extraction, argument classification (labeling), and global scoring via integer linear programming. …...

AbstractRecently, semantic role labeling (SRL) has earned a series of success with even higher performance improvements, which can be mainly attributed to syntactic integration and enhanced word representation. However, most …...

AbstractIn this paper we focus on learning dependency aware representations for semantic role labeling without recourse to an external parser. The backbone of our model is an LSTM-based semantic role …...

AbstractResources for Semantic Role Labeling (SRL) are typically annotated by experts at great expense. Prior attempts to develop crowdsourcing methods have either had low accuracy or required substantial expert annotation. …...

AbstractAs users engage in public discourse, the rate of voluntarily disclosed personal information has seen a steep increase. So-called self-disclosure can result in a number of privacy concerns. Users are …...

AbstractSemantic role labeling (SRL) identifies predicate-argument structure(s) in a given sentence. Although different languages have different argument annotations, polyglot training, the idea of training one model on multiple languages, has …...

AbstractNegation is a universal linguistic phenomenon with a great qualitative impact on natural language processing applications. The availability of corpora annotated with negation is essential to training negation processing systems. …...

AbstractMany efforts of research are devoted to semantic role labeling (SRL) which is crucial for natural language understanding. Supervised approaches have achieved impressing performances when large-scale corpora are available for …...

AbstractNatural language processing covers a wide variety of tasks predicting syntax, semantics, and information content, and usually each type of output is generated with specially designed architectures. In this paper, …...

AbstractSemantic role labeling (SRL) is the task of identifying predicates and labeling argument spans with semantic roles. Even though most semantic-role formalisms are built upon constituent syntax, and only syntactic …...

AbstractCurrent end-to-end semantic role labeling is mostly accomplished via graph-based neural models. However, these all are first-order models, where each decision for detecting any predicate-argument pair is made in isolation …...

AbstractSemantic role labeling is primarily used to identify predicates, arguments, and their semantic relationships. Due to the limitations of modeling methods and the conditions of pre-identified predicates, previous work has …...

AbstractThis paper is concerned with the goal of maintaining legal information and compliance systems: the ‘resource consumption bottleneck’ of creating semantic technologies manually. The use of automated information extraction techniques …...

AbstractFact checking is a challenging task because verifying the truthfulness of a claim requires reasoning about multiple retrievable evidence. In this work, we present a method suitable for reasoning about …...

AbstractWe reduce the task of (span-based) PropBank-style semantic role labeling (SRL) to syntactic dependency parsing. Our approach is motivated by our empirical analysis that shows three common syntactic patterns account …...

AbstractThis work deals with the challenge of learning and reasoning over multi-hop question answering (QA). We propose a graph reasoning network based on the semantic structure of the sentences to …...

AbstractFrame semantic parsing is a semantic analysis task based on FrameNet which has received great attention recently. The task usually involves three subtasks sequentially: (1) target identification, (2) frame classification …...

Structural syntax knowledge has been proven effective for semantic role labeling (SRL), while existing works mostly use only one sin- gleton syntax, such as either syntactic depen- dency or constituency …...

AbstractMuch recent work suggests that incorporating syntax information from dependency trees can improve task-specific transformer models. However, the effect of incorporating dependency tree information into pre-trained transformer models (e.g., BERT) …...

AbstractAlthough recent developments in neural architectures and pre-trained representations have greatly increased state-of-the-art model performance on fully-supervised semantic role labeling (SRL), the task remains challenging for languages where supervised SRL …...

AbstractWhile cross-lingual techniques are finding increasing success in a wide range of Natural Language Processing tasks, their application to Semantic Role Labeling (SRL) has been strongly limited by the fact …...

AbstractSemantic Role Labeling (SRL) aims at recognizing the predicate-argument structure of a sentence and can be decomposed into two subtasks: predicate disambiguation and argument labeling. Prior work deals with these …...

AbstractSemantic Role Labeling (SRL) is the task of labeling semantic arguments for marked semantic predicates. Semantic arguments and their predicates are related in various distinct manners, of which certain semantic …...

AbstractArgument classification is at the core of Semantic Role Labeling. Given a sentence and the predicate, a semantic role label is assigned to each argument of the predicate. While semantic …...

AbstractIn this paper, we propose a mixture model-based end-to-end method to model the syntactic-semantic dependency correlation in Semantic Role Labeling (SRL). Semantic dependencies in SRL are modeled as a distribution …...

AbstractDuring the past decade, neural network models have made tremendous progress on in-domain semantic role labeling (SRL). However, performance drops dramatically under the out-of-domain setting. In order to facilitate research …...

AbstractThanks to the effectiveness and wide availability of modern pretrained language models (PLMs), recently proposed approaches have achieved remarkable results in dependency- and span-based, multilingual and cross-lingual Semantic Role Labeling …...

AbstractSemantic role labeling (SRL) is a fundamental yet challenging task in the NLP community. Recent works of SRL mainly fall into two lines: 1) BIO-based; 2) span-based. Despite ubiquity, they …...

AbstractAs an important task to analyze the semantic structure of a sentence, semantic role labeling (SRL) aims to locate the semantic role (e.g., agent) of noun phrases with respect to …...

AbstractRobot-Assisted minimally invasive robotic surgery is the gold standard for the surgical treatment of many pathological conditions, and several manuals and academic papers describe how to perform these interventions. These …...

AbstractData-hungry deep neural networks have established themselves as the de facto standard for many NLP tasks, including the traditional sequence tagging ones. Despite their state-of-the-art performance on high-resource languages, they …...

AbstractIn this paper, we present the Universal Semantic Annotator (USeA), which offers the first unified API for high-quality automatic annotations of texts in 100 languages through state-of-the-art systems for Word …...

AbstractDespite impressive progress in machine translation in recent years, it has occasionally been argued that current systems are still mainly based on pattern recognition and that further progress may be …...

AbstractMemes can sway people’s opinions over social media as they combine visual and textual information in an easy-to-consume manner. Since memes instantly turn viral, it becomes crucial to infer their …...

AbstractAutomatic image comprehension is an important yet challenging task that includes identifying actions in an image and corresponding action participants. Most current approaches to this task, now termed Grounded Situation …...

AbstractRelationship extraction from unstructured data remains one of the most challenging tasks in the field of Natural Language Processing (NLP). The complexity of relationship extraction arises from the need to …...

AbstractDecision-makers in the humanitarian sector rely on timely and exact information during crisis events. Knowing how many civilians were injured during an earthquake is vital to allocate aids properly. Information …...

AbstractAutomatic fact verification has received significant attention recently. Contemporary automatic fact-checking systems focus on estimating truthfulness using numerical scores which are not human-interpretable. A human fact-checker generally follows several logical …...

AbstractThis paper presents a series of approaches aimed at enhancing the performance of Aspect-Based Sentiment Analysis (ABSA) by utilizing extracted semantic information from a Semantic Role Labeling (SRL) model. We …...

AbstractIn this paper we investigate the application of active learning to semantic role labeling (SRL) using Bayesian Active Learning by Disagreement (BALD). Our new predicate-focused selection method quickly improves efficiency …...

AbstractSemantic role labeling (SRL) identifies the predicate-argument structure in a sentence. This task is usually accomplished in four steps: predicate identification, predicate sense disambiguation, argument identification, and argument classification. Errors …...

AbstractAutomatic generation of questions from text has gained increasing attention due to its useful applications. We propose a novel question generation method that combines the benefits of rule-based and neural …...

AbstractIn this paper, we investigate whether symbolic semantic representations, extracted from deep semantic parsers, can help reasoning over the states of involved entities in a procedural text. We consider a …...