Constructing a cross-document event coreference corpus for Dutch

The task of event coreference resolution plays a critical role in many natural language processing applications such as information extraction, question answering, and topic detection and tracking. In this article, we describe a new class of unsupervised, nonparametric Bayesian models with the purpose of probabilistically inferring coreference clusters of event mentions from a collection of unlabeled documents. In order to infer these clusters, we automatically extract various lexical, syntactic, and semantic features for each event mention from the document collection. Extracting a rich set of features for each event mention allows us to cast event coreference resolution as the task of grouping together the mentions that share the same features (they have the same participating entities, share the same location, happen at the same time, etc.). Some of the most important challenges posed by the resolution of event coreference in an unsupervised way stem from (a) the choice of representing event mentions through a rich set of features and (b) the ability of modeling events described both within the same document and across multiple documents. Our first unsupervised model that addresses these challenges is a generalization of the hierarchical Dirichlet process. This new extension presents the hierarchical Dirichlet process's ability to capture the uncertainty regarding the number of clustering components and, additionally, takes into account any finite number of features associated with each event mention. Furthermore, to overcome some of the limitations of this extension, we devised a new hybrid model, which combines an infinite latent class model with a discrete time series model. The main advantage of this hybrid model stands in its capability to automatically infer the number of features associated with each event mention from data and, at the same time, to perform an automatic selection of the most informative features for the task of event coreference. The evaluation performed for solving both within- and cross-document event coreference shows significant improvements of these models when compared against two baselines for this task.

A tool for the manual annotation of cross-document entity and event coreferences that helps annotators to label mention coreference relations in text is essential for the annotation of coreference corpora. To the best of our knowledge, CROss-document Main Events and entities Recognition (CROMER) is the only open-source manual annotation tool available for cross-document entity and event coreferences. However, CROMER lacks multi-language support and extensibility. Moreover, to label cross-document mention coreference relations, CROMER requires the support of another intra-document coreference annotation tool known as Content Annotation Tool, which is now unavailable. To address these problems, we introduce Cross-Document Coreference Annotation Tool (CDCAT), a new multi-language open-source manual annotation tool for cross-document entity and event coreference, which can handle different input/output formats, preprocessing functions, languages, and annotation systems. Using this new tool, annotators can label a reference relation with only two mouse clicks. Best practice analyses reveal that annotators can reach an annotation speed of 0.025 coreference relations per second on a corpus with a coreference density of 0.076 coreference relations per word. As the first multi-language open-source cross-document entity and event coreference annotation tool, CDCAT can theoretically achieve higher annotation efficiency than CROMER.

Most previous models on event coreference resolution largely depend on hand-crafted features and annotated corpora. To address above issues, this paper introduces a neural model to resolve document-level event coreference in raw texts by both employing various neural components to better represent event semantics and integrating data augmentation with reinforcement learning to largely expand the dataset and effectively improve its quality. Experimentation on three KBP datasets shows that our proposed neural model significantly outperforms several strong state-of-the-art baselines.

Events play an important role in natural language processing and information retrieval due to numerous event-oriented texts and information needs. Many natural language processing and information retrieval applications could benefit from a structured event-oriented document representation. In this paper, we proposeevent graphsas a novel way of structuring event-based information from text. Nodes in event graphs represent the individual mentions of events, whereas edges represent the temporal and coreference relations between mentions. Contrary to previous natural language processing research, which has mainly focused on individual event extraction tasks, we describe a complete end-to-end system for event graph extraction from text. Our system is a three-stage pipeline that performs anchor extraction, argument extraction, and relation extraction (temporal relation extraction and event coreference resolution), each at a performance level comparable with the state of the art. We presentEvExtra, a large newspaper corpus annotated with event mentions and event graphs, on which we train and evaluate our models. To measure the overall quality of the constructed event graphs, we propose two metrics based on the tensor product between automatically and manually constructed graphs. Finally, we evaluate the overall quality of event graphs with the proposed evaluation metrics and perform a headroom analysis of the system.

Event coreference resolution is an important step for business intelligence.In this paper, we present a method for event coreference resolution based on heterogeneous information network.We adopt holistic, hierarchical clustering algorithm to solve this problem.In terms of similarity measure, the proposed method utilizes the relationship between entities, events, documents and data source, calculates event comprehensive similarity through the event properties similarity.Experiment on business event sets, characters event sets and product event sets, the experiment results show that the algorithm of this paper can effectively complete event coreference resolution, and has good recall and precision.

We study a new problem of cross-lingual transfer learning for event coreference resolution (ECR) where models trained on data from a source language are adapted for evaluations in different target languages. We introduce the first baseline model for this task based on XLM-RoBERTa, a state-of-the-art multilingual pre-trained language model. We also explore language adversarial neural networks (LANN) that present language discriminators to distinguish texts from the source and target languages to improve the language generalization for ECR. In addition, we introduce two novel mechanisms to further enhance the general representation learning of LANN, featuring: (i) multi-view alignment to penalize cross coreference-label alignment of examples in the source and target languages, and (ii) optimal transport to select close examples in the source and target languages to provide better training signals for the language discriminators. Finally, we perform extensive experiments for cross-lingual ECR from English to Spanish and Chinese to demonstrate the effectiveness of the proposed methods.

Cross-document coreference resolution plays an import part in the filed of natural language processing (NLP). It captures the ability of gathering documents for information about a certain entity. Most previous algorithms identify the underlying entity of a given document depending on the original text, which is unreliable if the original text contains multiple parts of different themes. In this paper, we propose a cross-document coreference resolution algorithm based on automatic text summary instead of the original text. In our approach, we extract query-specific and informative-indicative summary from the original text by using Hobbs algorithm and measure the similarity between two summaries. This automatic text summary-based cross-document coreference resolution (ATSCDCR) system is effective in disambiguating different entities of the same mention name and identifying the same entity of different mention names. The results from our experiments show that the macro average of ATSCDCR system is up to 73.16% and the micro average of ATSCDCR system is 67.34 %.

Event coreference models cluster event mentions pertaining to the same real-world event. Recent models rely on contextualized representations to recognize coreference among lexically or contextually similar mentions. However, models typically fail to leverage commonsense inferences, which is particularly limiting for resolving lexically-divergent mentions. We propose a model that extends event mentions with temporal commonsense inferences. Given a complex sentence with multiple events, e.g., "The man killed his wife and got arrested", with the target event "arrested", our model generates plausible events that happen before the target event -such as "the police arrived", and after it, such as "he was sentenced". We show that incorporating such inferences into an existing event coreference model improves its performance, and we analyze the coreferences in which such temporal knowledge is required.

Cross-document co-reference resolution (CCR) computes equivalence classes over textual mentions denoting the same entity in a document corpus.Named-entity linking (NEL) disambiguates mentions onto entities present in a knowledge base (KB) or maps them to null if not present in the KB.Traditionally, CCR and NEL have been addressed separately.However, such approaches miss out on the mutual synergies if CCR and NEL were performed jointly.This paper proposes C3EL, an unsupervised framework combining CCR and NEL for jointly tackling both problems.C3EL incorporates results from the CCR stage into NEL, and vice versa: additional global context obtained from CCR improves the feature space and performance of NEL, while NEL in turn provides distant KB features for already disambiguated mentions to improve CCR.The CCR and NEL steps are interleaved in an iterative algorithm that focuses on the highest-confidence still unresolved mentions in each iteration.Experimental results on two different corpora, news-centric and web-centric, demonstrate significant gains over state-of-the-art baselines for both CCR and NEL.

e19062 Background: The annual incidence of venous thromboembolism (VTE) is roughly 50-fold increased post allogeneic hematopoietic cell transplant (HSCT). Timely detection of VTE events has a significant impact on morbidity and mortality from these events. Manual chart review is commonly used for detection of VTE events; however, this is labor intensive and often not feasible. The International Classification of Diseases, 10th revision (ICD-10) has attempted to codify VTE more easily but has high false positive and false negative rates. Natural language processing (NLP) algorithms have been designed to improve detection of VTE events in electronic medical record systems over the past several years. This study aims to compare the accuracy of our institution’s NLP algorithm to the ICD-10 in detecting extremity VTE/pulmonary embolism (PE) events in hematopoietic stem cell transplant (HSCT) patients. Methods: This study was a retrospective analysis of adult patients who had allogeneic HSCT at MD Anderson between 2016-2020. Patient charts were assessed by three different methods for acute VTE events within 100 days of HSCT (index date). ICD-10 codes and our institution’s NLP algorithm were used separately to identify these VTE events. Afterwards, manual chart review was performed for confirmation. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for the ICD-10, NLP, and the NLP+ICD combination. Results: A total of 1,391 patient charts were analyzed in our study. There were 129 (9.3%) VTE events detected via manual chart review. Three methodologies were used for comparison: the NLP algorithm alone, the ICD code alone, and detection by NLP or ICD. The NLP algorithm alone had the best specificity (98.9%) and PPV (89.4%) of the three methods. The ICD-10 code had a statistically significant decrease in ability to detect acute VTE events compared to the NLP (p<0.001) (Table). Of the 19 VTE events not identified by NLP, all were found in radiology or vascular laboratory reports. Conclusions: Our study shows the overall superior performance of the NLP algorithm, as it had higher sensitivity, specificity, PPV, and NPV compared to the ICD-10, as well as a better specificity and PPV compared to the combination of both methods. Future refinement of the NLP algorithm and combination with other detection methodologies will allow for more accurate detection of VTE in large cohorts. [Table: see text]

A survey on textual entailment based question answering

Datasets and methods for cross-document coreference resolution (CDCR) focus on events or entities with strict coreference relations. They lack, however, annotating and resolving coreference mentions with more abstract or loose relations that may occur when news articles report about controversial and polarized events. Bridging and loose coreference relations trigger associations that may expose news readers to bias by word choice and labeling. For example, coreferential mentions of “direct talks between U.S. President Donald Trump and Kim” such as “an extraordinary meeting following months of heated rhetoric” or “great chance to solve a world problem” form a more positive perception of this event. A step towards bringing awareness of bias by word choice and labeling is the reliable resolution of coreferences with high lexical diversity. We propose an unsupervised method named XCoref, which is a CDCR method that capably resolves not only previously prevalent entities, such as persons, e.g., “Donald Trump,” but also abstractly defined concepts, such as groups of persons, “caravan of immigrants,” events and actions, e.g., “marching to the U.S. border.” In an extensive evaluation, we compare the proposed XCoref to a state-of-the-art CDCR method and a previous method TCA that resolves such complex coreference relations and find that XCoref outperforms these methods. Outperforming an established CDCR model shows that the new CDCR models need to be evaluated on semantically complex mentions with more loose coreference relations to indicate their applicability of models to resolve mentions in the “wild” of political news articles.KeywordsCross-document coreference resolutionNews analysisMedia bias

Previous research especially based on the concept of intertextuality has denoted an important shift in perspective considering the relationship between text and narrator. The distinction between narrator and voice or, as Roland Barthes formulated in the title of his famous essay ‘The Death of the Author’ (1967), likewise Foucault, dispensed with the idea of authorship. This approach may also provide a deeper insight in the construction of fragments, that – as has been rightly remarked – are made rather than born (Dionisotti, above). However, although prior work has already been focused on the possibilities of methods from the field of Information Retrieval especially concerning the digital editing of quotations and fragments, this study argues the importance of combining textmining methods with approaches from narratology. Within the eAQUA project we adapt and use tools from the field of NLP (Natural Language Processing) to support our scientific work. In this paper we describe the extent to which the complex relationship between quotations as fragmentary texts and their surrounding context benefits from algorithm-based methods like word level n-gams as features in classification and referencing of fragments. The examples described in this paper using eAQUA’s Citatongraph demonstrate that the Citationgraph gives an excellent estimate of quotations and text reuse. Concerning the question of fragments this tool remarkably broadens our possibilities helping to ‘unearth’ fragments lost in the process of editorial decisions. Representing a kind of ‘unediting’ whereby the various layers of editorial decisions can be decoded this new approach yields a promising new avenue of research.

Recognizing textual entailment comprises the task of determining semantic entailment relations between text fragments. A text fragment entails another text fragment if, from the meaning of the former, one can infer the meaning of the latter. If such relation is bidirectional, then we are in the presence of a paraphrase. Automatically recognizing textual entailment relations captures major semantic inference needs in several natural language processing (NLP) applications. As in many NLP tasks, textual entailment corpora for English abound, while the same is not true for more resource-scarce languages such as Portuguese. Exploiting what seems to be the only Portuguese corpus for textual entailment and paraphrases (the ASSIN corpus), in this paper, we address the task of automatically recognizing textual entailment (RTE) and paraphrases from text written in the Portuguese language, by employing supervised machine learning techniques. We employ lexical, syntactic and semantic features, and analyze the impact of using semantic-based approaches in the performance of the system. We then try to take advantage of the bi-dialect nature of ASSIN to compensate its limited size. With the same aim, we explore modeling the task of recognizing textual entailment and paraphrases as a binary classification problem by considering the bidirectional nature of paraphrases as entailment relationships. Addressing the task as a multi-class classification problem, we achieve results in line with the winner of the ASSIN Challenge. In addition, we conclude that semantic-based approaches are promising in this task, and that combining data from European and Brazilian Portuguese is less straightforward than it may initially seem. The binary classification modeling of the problem does not seem to bring advantages to the original multi-class model, despite the outstanding results obtained by the binary classifier for recognizing textual entailments.

The rapid proliferation of misinformation across online platforms poses a challenge that calls for advanced real-time detection and verification mechanisms. Most existing methods depend on some form of user inspection or centralized system which tends to be slow, biased, and subjective. In this paper, we address these gaps by integrating Natural Language Processing (NLP), Machine Learning (ML), and blockchain technology in a single novel framework. The system functions through three primary processes: event detection, trust evaluation, and validation through blockchain. In the primary step, NLP models examine data streams like news articles, tweets, and forum posts to capture major events across diverse areas including politics, finance, healthcare, and natural disasters. The second step applies ML algorithms to evaluate the identified events based on source credibility, narrative reliability, and factual consistency analysis. In the last stage, the verification process is facilitated through the use of blockchain technology which assures transparency, immutability, decentralization, and security by recording verified events alongside their trust scores on a blockchain ledger. This multi-disciplinary approach not only enhances accuracy and boosts accountability but also streamlines real-time processing while tackling the challenges of trusted event detection and verification. By applying NLP, ML, and blockchain, this paper proposes a new paradigm of trust enhancement for digital information.