pwc-archive/papers-with-abstracts huggingface dataset:
Domain Adapted Distant Supervision for Pedagogically Motivated Relation Extraction
Authors: Oscar Sainz, Oier Lopez de Lacalle, Itziar Aldabe, Montse Maritxalar
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Reading Comprehension, Relation, Relation Extraction, Transfer Learning, valid

In this paper we present a relation extraction system that given a text extracts pedagogically motivated relation types, as a previous step to obtaining a semantic representation of the text which will make possible to automatically generate questions for reading comprehension. The system maps pedagogically motivated relations with relations from ConceptNet and deploys Distant Supervision for relation extraction. We run a study on a subset of those relationships in order to analyse the viability of our approach. For that, we build a domain-specific relation extraction system and explore two relation extraction models: a state-of-the-art model based on transfer learning and a discrete feature based machine learning model. Experiments show that the neural model obtains better results in terms of F-score and we yield promising results on the subset of relations suitable for pedagogical purposes. We thus consider that distant supervision for relation extraction is a valid approach in our target domain, i.e. biology.
Temporal Histories of Epidemic Events (THEE): A Case Study in Temporal Annotation for Public Health
Authors: Jingcheng Niu, Victoria Ng, Gerald Penn, Erin E. Rees
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Articles, Temporal Information Extraction

We present a new temporal annotation standard, THEE-TimeML, and a corpus TheeBank enabling precise temporal information extraction (TIE) for event-based surveillance (EBS) systems in the public health domain. Current EBS must estimate the occurrence time of each event based on coarse document metadata such as document publication time. Because of the complicated language and narration style of news articles, estimated case outbreak times are often inaccurate or even erroneous. Thus, it is necessary to create annotation standards and corpora to facilitate the development of TIE systems in the public health domain to address this problem.We will discuss the adaptations that have proved necessary for this domain as we present THEE-TimeML and TheeBank. Finally, we document the corpus annotation process, and demonstrate the immediate benefit to public health applications brought by the annotations.
Exploiting Citation Knowledge in Personalised Recommendation of Recent Scientific Publications
Authors: Anita Khadka, Iv{\'a}n Cantador, Fern, Miriam ez
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Information Retrieval, Retrieval

In this paper we address the problem of providing personalised recommendations of recent scientific publications to a particular user, and explore the use of citation knowledge to do so. For this purpose, we have generated a novel dataset that captures authors{'} publication history and is enriched with different forms of paper citation knowledge, namely citation graphs, citation positions, citation contexts, and citation types. Through a number of empirical experiments on such dataset, we show that the exploitation of the extracted knowledge, particularly the type of citation, is a promising approach for recommending recently published papers that may not be cited yet. The dataset, which we make publicly available, also represents a valuable resource for further investigation on academic information retrieval and filtering.
A Platform for Event Extraction in Hindi
Authors: Sovan Kumar Sahoo, Saumajit Saha, Asif Ekbal, Pushpak Bhattacharyya
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Articles, Benchmarking, Classification, Deep Learning, Event Extraction, General Classification

Event Extraction is an important task in the widespread field of Natural Language Processing (NLP). Though this task is adequately addressed in English with sufficient resources, we are unaware of any benchmark setup in Indian languages. Hindi is one of the most widely spoken languages in the world. In this paper, we present an Event Extraction framework for Hindi language by creating an annotated resource for benchmarking, and then developing deep learning based models to set as the baselines. We crawl more than seventeen hundred disaster related Hindi news articles from the various news sources. We also develop deep learning based models for Event Trigger Detection and Classification, Argument Detection and Classification and Event-Argument Linking.
Rad-SpatialNet: A Frame-based Resource for Fine-Grained Spatial Relations in Radiology Reports
Authors: Surabhi Datta, Morgan Ulinski, Jordan Godfrey-Stovall, Shekhar Khanpara, Roy F. Riascos-Castaneda, Kirk Roberts
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This paper proposes a representation framework for encoding spatial language in radiology based on frame semantics. The framework is adopted from the existing SpatialNet representation in the general domain with the aim to generate more accurate representations of spatial language used by radiologists. We describe Rad-SpatialNet in detail along with illustrating the importance of incorporating domain knowledge in understanding the varied linguistic expressions involved in different radiological spatial relations. This work also constructs a corpus of 400 radiology reports of three examination types (chest X-rays, brain MRIs, and babygrams) annotated with fine-grained contextual information according to this schema. Spatial trigger expressions and elements corresponding to a spatial frame are annotated. We apply BERT-based models (BERT-Base and BERT- Large) to first extract the trigger terms (lexical units for a spatial frame) and then to identify the related frame elements. The results of BERT- Large are decent, with F1 of 77.89 for spatial trigger extraction and an overall F1 of 81.61 and 66.25 across all frame elements using gold and predicted spatial triggers respectively. This frame-based resource can be used to develop and evaluate more advanced natural language processing (NLP) methods for extracting fine-grained spatial information from radiology text in the future.
NLP Analytics in Finance with DoRe: A French 250M Tokens Corpus of Corporate Annual Reports
Authors: Corentin Masson, Patrick Paroubek
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Stock Market Prediction, Word Embeddings

Recent advances in neural computing and word embeddings for semantic processing open many new applications areas which had been left unaddressed so far because of inadequate language understanding capacity. But this new kind of approaches rely even more on training data to be operational. Corpora for financial applications exists, but most of them concern stock market prediction and are in English. To address this need for the French language and regulation oriented applications which require a deeper understanding of the text content, we hereby present {``}DoRe{''}, a French and dialectal French Corpus for NLP analytics in Finance, Regulation and Investment. This corpus is composed of: (a) 1769 Annual Reports from 336 companies among the most capitalized companies in: France (Euronext Paris) {\&} Belgium (Euronext Brussels), covering a time frame from 2009 to 2019, and (b) related MetaData containing information for each company about its ISIN code, capitalization and sector. This corpus is designed to be as modular as possible in order to allow for maximum reuse in different tasks pertaining to Economics, Finance and Regulation. After presenting existing resources, we relate the construction of the DoRe corpus and the rationale behind our choices, concluding on the spectrum of possible uses of this new resource for NLP applications.
The Language of Brain Signals: Natural Language Processing of Electroencephalography Reports
Authors: Ramon Maldonado, S Harabagiu, a
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: EEG, Electroencephalogram (EEG)

Brain signals are captured by clinical electroencephalography (EEG) which is an excellent tool for probing neural function. When EEG tests are performed, a textual EEG report is generated by the neurologist to document the findings, thus using language that describes the brain signals and its clinical correlations. Even with the impetus provided by the BRAIN initiative (brainitititive.nih.gov), there are no annotations available in texts that capture language describing the brain activities and their correlations with various pathologies. In this paper we describe an annotation effort carried out on a large corpus of EEG reports, providing examples of EEG-specific and clinically relevant concepts. In addition, we detail our annotation schema for brain signal attributes. We also discuss the resulting annotation of long-distance relations between concepts in EEG reports. By exemplifying a self-attention joint-learning to predict similar annotations in the EEG report corpus, we discuss the promising results, hoping that our effort will inform the design of novel knowledge capture techniques that will include the language of brain signals.
Humans Keep It One Hundred: an Overview of AI Journey
Authors: Tatiana Shavrina, Anton Emelyanov, Alena Fenogenova, Vadim Fomin, Vladislav Mikhailov, Andrey Evlampiev, Valentin Malykh, Vladimir Larin, Alex Natekin, Aleks Vatulin, R, Peter Romov, Daniil Anastasiev, Nikolai Zinov, Andrey Chertok
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Text Generation

Artificial General Intelligence (AGI) is showing growing performance in numerous applications - beating human performance in Chess and Go, using knowledge bases and text sources to answer questions (SQuAD) and even pass human examination (Aristo project). In this paper, we describe the results of AI Journey, a competition of AI-systems aimed to improve AI performance on knowledge bases, reasoning and text generation. Competing systems pass the final native language exam (in Russian), including versatile grammar tasks (test and open questions) and an essay, achieving a high score of 69{\%}, with 68{\%} being an average human result. During the competition, a baseline for the task and essay parts was proposed, and 80+ systems were submitted, showing different approaches to task understanding and reasoning. All the data and solutions can be found on github https://github.com/sberbank-ai/combined{\_}solution{\_}aij2019
Towards Data-driven Ontologies: a Filtering Approach using Keywords and Natural Language Constructs
Authors: Maaike de Boer, Jack P. C. Verhoosel
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Creating ontologies is an expensive task. Our vision is that we can automatically generate ontologies based on a set of relevant documents to create a kick-start in ontology creating sessions. In this paper, we focus on enhancing two often used methods, OpenIE and co-occurrences. We evaluate the methods on two document sets, one about pizza and one about the agriculture domain. The methods are evaluated using two types of F1-score (objective, quantitative) and through a human assessment (subjective, qualitative). The results show that 1) Cooc performs both objectively and subjectively better than OpenIE; 2) the filtering methods based on keywords and on Word2vec perform similarly; 3) the filtering methods both perform better compared to OpenIE and similar to Cooc; 4) Cooc-NVP performs best, especially considering the subjective evaluation. Although, the investigated methods provide a good start for extracting an ontology out of a set of domain documents, various improvements are still possible, especially in the natural language based methods.
A French Corpus and Annotation Schema for Named Entity Recognition and Relation Extraction of Financial News
Authors: Ali Jabbari, Olivier Sauvage, Hamada Zeine, Hamza Chergui
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Articles, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Relation, Relation Extraction

In financial services industry, compliance involves a series of practices and controls in order to meet key regulatory standards which aim to reduce financial risk and crime, e.g. money laundering and financing of terrorism. Faced with the growing risks, it is imperative for financial institutions to seek automated information extraction techniques for monitoring financial activities of their customers. This work describes an ontology of compliance-related concepts and relationships along with a corpus annotated according to it. The presented corpus consists of financial news articles in French and allows for training and evaluating domain-specific named entity recognition and relation extraction algorithms. We present some of our experimental results on named entity recognition and relation extraction using our annotated corpus. We aim to furthermore use the the proposed ontology towards construction of a knowledge base of financial relations.
Inferences for Lexical Semantic Resource Building with Less Supervision
Authors: Nadia Bebeshina, Mathieu Lafourcade
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Lexical semantic resources may be built using various approaches such as extraction from corpora, integration of the relevant pieces of knowledge from the pre-existing knowledge resources, and endogenous inference. Each of these techniques needs human supervision in order to deal with the potential errors, mapping difficulties or inferred candidate validation. We detail how various inference processes can be employed for the less supervised lexical semantic resource building. Our experience is based on the combination of different inference techniques for multilingual resource building and evaluation.
Acquiring Social Knowledge about Personality and Driving-related Behavior
Authors: Ritsuko Iwai, Daisuke Kawahara, Takatsune Kumada, Sadao Kurohashi
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

In this paper, we introduce our psychological approach to collect human-specific social knowledge from a text corpus, using NLP techniques. It is often not explicitly described but shared among people, which we call social knowledge. We focus on the social knowledge, especially personality and driving. We used the language resources that were developed based on psychological research methods; a Japanese personality dictionary (317 words) and a driving experience corpus (8,080 sentences) annotated with behavior and subjectivity. Using them, we automatically extracted collocations between personality descriptors and driving-related behavior from a driving behavior and subjectivity corpus (1,803,328 sentences after filtering) and obtained unique 5,334 collocations. To evaluate the collocations as social knowledge, we designed four step-by-step crowdsourcing tasks. They resulted in 266 pieces of social knowledge. They include the knowledge that might be difficult to recall by themselves but easy to agree with. We discuss the acquired social knowledge and the contribution to implementations into systems.
Multiple Knowledge GraphDB (MKGDB)
Authors: Stefano Faralli, Paola Velardi, Farid Yusifli
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Clustering, Knowledge Graphs

We present MKGDB, a large-scale graph database created as a combination of multiple taxonomy backbones extracted from 5 existing knowledge graphs, namely: ConceptNet, DBpedia, WebIsAGraph, WordNet and the Wikipedia category hierarchy. MKGDB, thanks the versatility of the Neo4j graph database manager technology, is intended to favour and help the development of open-domain natural language processing applications relying on knowledge bases, such as information extraction, hypernymy discovery, topic clustering, and others. Our resource consists of a large hypernymy graph which counts more than 37 million nodes and more than 81 million hypernymy relations.
Evaluation Dataset and Methodology for Extracting Application-Specific Taxonomies from the Wikipedia Knowledge Graph
Authors: Georgeta Bordea, Stefano Faralli, Fleur Mougin, Paul Buitelaar, Gayo Diallo
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Knowledge Graphs

In this work, we address the task of extracting application-specific taxonomies from the category hierarchy of Wikipedia. Previous work on pruning the Wikipedia knowledge graph relied on silver standard taxonomies which can only be automatically extracted for a small subset of domains rooted in relatively focused nodes, placed at an intermediate level in the knowledge graphs. In this work, we propose an iterative methodology to extract an application-specific gold standard dataset from a knowledge graph and an evaluation framework to comparatively assess the quality of noisy automatically extracted taxonomies. We employ an existing state of the art algorithm in an iterative manner and we propose several sampling strategies to reduce the amount of manual work needed for evaluation. A first gold standard dataset is released to the research community for this task along with a companion evaluation framework. This dataset addresses a real-world application from the medical domain, namely the extraction of food-drug and herb-drug interactions.
Subjective Evaluation of Comprehensibility in Movie Interactions
Authors: R, Estelle ria, Lionel Fontan, Maxime Le Coz, Isabelle Ferran{\'e}, Julien Pinquier
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Various research works have dealt with the comprehensibility of textual, audio, or audiovisual documents, and showed that factors related to text (e.g. linguistic complexity), sound (e.g. speech intelligibility), image (e.g. presence of visual context), or even to cognition and emotion can play a major role in the ability of humans to understand the semantic and pragmatic contents of a given document. However, to date, no reference human data is available that could help investigating the role of the linguistic and extralinguistic information present at these different levels (i.e., linguistic, audio/phonetic, and visual) in multimodal documents (e.g., movies). The present work aimed at building a corpus of human annotations that would help to study further how much and in which way the human perception of comprehensibility (i.e., of the difficulty of comprehension, referred in this paper as overall difficulty) of audiovisual documents is affected (1) by lexical complexity, grammatical complexity, and speech intelligibility, and (2) by the modality/ies (text, audio, video) available to the human recipient.
Representing Multiword Term Variation in a Terminological Knowledge Base: a Corpus-Based Study
Authors: Pilar Le{\'o}n-Ara{\'u}z, Arianne Reimerink, Melania Cabezas-Garc{\'\i}a
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Translation

In scientific and technical communication, multiword terms are the most frequent type of lexical units. Rendering them in another language is not an easy task due to their cognitive complexity, the proliferation of different forms, and their unsystematic representation in terminographic resources. This often results in a broad spectrum of translations for multiword terms, which also foment term variation since they consist of two or more constituents. In this study we carried out a quantitative and qualitative analysis of Spanish translation variants of a set of environment-related concepts by evaluating equivalents in three parallel corpora, two comparable corpora and two terminological resources. Our results showed that MWTs exhibit a significant degree of term variation of different characteristics, which were used to establish a set of criteria according to which term variants should be selected, organized and described in terminological knowledge bases.
A Topic-Aligned Multilingual Corpus of Wikipedia Articles for Studying Information Asymmetry in Low Resource Languages
Authors: Dwaipayan Roy, Sumit Bhatia, Prateek Jain
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Articles

Wikipedia is the largest web-based open encyclopedia covering more than three hundred languages. However, different language editions of Wikipedia differ significantly in terms of their information coverage. We present a systematic comparison of information coverage in English Wikipedia (most exhaustive) and Wikipedias in eight other widely spoken languages (Arabic, German, Hindi, Korean, Portuguese, Russian, Spanish and Turkish). We analyze the content present in the respective Wikipedias in terms of the coverage of topics as well as the depth of coverage of topics included in these Wikipedias. Our analysis quantifies and provides useful insights about the information gap that exists between different language editions of Wikipedia and offers a roadmap for the IR community to bridge this gap.
P\'art\'elet: A Hungarian Corpus of Propaganda Texts from the Hungarian Socialist Era
Authors: Zolt{\'a}n Kmetty, Veronika Vincze, Dorottya Demszky, Orsolya Ring, Bal{\'a}zs Nagy, Martina Katalin Szab{\'o}
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

In this paper, we present P{\'a}rt{\'e}let, a digitized Hungarian corpus of Communist propaganda texts. P{\'a}rt{\'e}let was the official journal of the governing party during the Hungarian socialism from 1956 to 1989, hence it represents the direct political agitation and propaganda of the dictatorial system in question. The paper has a dual purpose: first, to present a general review of the corpus compilation process and the basic statistical data of the corpus, and second, to demonstrate through two case studies what the dataset can be used for. We show that our corpus provides a unique opportunity for conducting research on Hungarian propaganda discourse, as well as analyzing changes of this discourse over a 35-year period of time with computer-assisted methods.
KORE 50\^DYWC: An Evaluation Data Set for Entity Linking Based on DBpedia, YAGO, Wikidata, and Crunchbase
Authors: Kristian Noullet, Rico Mix, Michael F{\"a}rber
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Entity Linking, Knowledge Graphs, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

A major domain of research in natural language processing is named entity recognition and disambiguation (NERD). One of the main ways of attempting to achieve this goal is through use of Semantic Web technologies and its structured data formats. Due to the nature of structured data, information can be extracted more easily, therewith allowing for the creation of knowledge graphs. In order to properly evaluate a NERD system, gold standard data sets are required. A plethora of different evaluation data sets exists, mostly relying on either Wikipedia or DBpedia. Therefore, we have extended a widely-used gold standard data set, KORE 50, to not only accommodate NERD tasks for DBpedia, but also for YAGO, Wikidata and Crunchbase. As such, our data set, KORE 50{\^{}}DYWC, allows for a broader spectrum of evaluation. Among others, the knowledge graph agnosticity of NERD systems may be evaluated which, to the best of our knowledge, was not possible until now for this number of knowledge graphs.