pwc-archive/papers-with-abstracts huggingface dataset:
Development of a Corpus Annotated with Medications and their Attributes in Psychiatric Health Records
Authors: Jaya Chaturvedi, Natalia Viani, Jyoti Sanyal, Chloe Tytherleigh, Idil Hasan, Kate Baird, Sumithra Velupillai, Robert Stewart, Angus Roberts
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Free text fields within electronic health records (EHRs) contain valuable clinical information which is often missed when conducting research using EHR databases. One such type of information is medications which are not always available in structured fields, especially in mental health records. Most use cases that require medication information also generally require the associated temporal information (e.g. current or past) and attributes (e.g. dose, route, frequency). The purpose of this study is to develop a corpus of medication annotations in mental health records. The aim is to provide a more complete picture behind the mention of medications in the health records, by including additional contextual information around them, and to create a resource for use when developing and evaluating applications for the extraction of medications from EHR text. Thus far, an analysis of temporal information related to medications mentioned in a sample of mental health records has been conducted. The purpose of this analysis was to understand the complexity of medication mentions and their associated temporal information in the free text of EHRs, with a specific focus on the mental health domain.
Do not let the history haunt you: Mitigating Compounding Errors in Conversational Question Answering
Authors: M, Angrosh ya, James O{'} Neill, Danushka Bollegala, Frans Coenen
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Conversational Question Answering, Question Answering

The Conversational Question Answering (CoQA) task involves answering a sequence of inter-related conversational questions about a contextual paragraph. Although existing approaches employ human-written ground-truth answers for answering conversational questions at test time, in a realistic scenario, the CoQA model will not have any access to ground-truth answers for the previous questions, compelling the model to rely upon its own previously predicted answers for answering the subsequent questions. In this paper, we find that compounding errors occur when using previously predicted answers at test time, significantly lowering the performance of CoQA systems. To solve this problem, we propose a sampling strategy that dynamically selects between target answers and model predictions during training, thereby closely simulating the situation at test time. Further, we analyse the severity of this phenomena as a function of the question type, conversation length and domain type.
CLEEK: A Chinese Long-text Corpus for Entity Linking
Authors: Weixin Zeng, Xiang Zhao, Jiuyang Tang, Zhen Tan, Xuqian Huang
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Entity Linking, Knowledge Base Construction

Entity linking, as one of the fundamental tasks in natural language processing, is crucial to knowledge fusion, knowledge base construction and update. Nevertheless, in contrast to the research on entity linking for English text, which undergoes continuous development, the Chinese counterpart is still in its infancy. One prominent issue lies in publicly available annotated datasets and evaluation benchmarks, which are lacking and deficient. In specific, existing Chinese corpora for entity linking were mainly constructed from noisy short texts, such as microblogs and news headings, where long texts were largely overlooked, which yet constitute a wider spectrum of real-life scenarios. To address the issue, in this work, we build CLEEK, a Chinese corpus of multi-domain long text for entity linking, in order to encourage advancement of entity linking in languages besides English. The corpus consists of 100 documents from diverse domains, and is publicly accessible. Moreover, we devise a measure to evaluate the difficulty of documents with respect to entity linking, which is then used to characterize the corpus. Additionally, the results of two baselines and seven state-of-the-art solutions on CLEEK are reported and compared. The empirical results validate the usefulness of CLEEK and the effectiveness of proposed difficulty measure.
A Contract Corpus for Recognizing Rights and Obligations
Authors: Ruka Funaki, Yusuke Nagata, Kohei Suenaga, Shinsuke Mori
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

A contract is a legal document executed by two or more parties. It is important for these parties to precisely understand their rights and obligations that are described in the contract. However, understanding the content of a contract is sometimes difficult and costly, particularly if the contract is long and complicated. Therefore, a language-processing system that can present information concerning rights and obligations found within a given contract document would help a contracting party to make better decisions. As a step toward the development of such a language-processing system, in this paper, we describe the annotated corpus of contract documents that we built. Our corpus is annotated so that a language-processing system can recognize a party{'}s rights and obligations. The annotated information includes the parties involved in the contract, the rights and obligations of the parties, the conditions and the exceptions under which these rights and obligations to take effect. The corpus was built based on 46 English contracts and 25 Japanese contracts drafted by lawyers. We explain how we annotated the corpus and the statistics of the corpus. We also report the results of the experiments for recognizing rights and obligations.
Extraction of the Argument Structure of Tokyo Metropolitan Assembly Minutes: Segmentation of Question-and-Answer Sets
Authors: Keiichi Takamaru, Yasutomo Kimura, Hideyuki Shibuki, Hokuto Ototake, Yuzu Uchida, Kotaro Sakamoto, Madoka Ishioroshi, Teruko Mitamura, K, Noriko o
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Segmentation

In this study, we construct a corpus of Japanese local assembly minutes. All speeches in an assembly were transcribed into a local assembly minutes based on the local autonomy law. Therefore, the local assembly minutes form an extremely large amount of text data. Our ultimate objectives were to summarize and present the arguments in the assemblies, and to use the minutes as primary information for arguments in local politics. To achieve this, we structured all statements in assembly minutes. We focused on the structure of the discussion, i.e., the extraction of question and answer pairs. We organized the shared task {``}QA Lab-PoliInfo{''} in NTCIR 14. We conducted a {``}segmentation task{''} to identify the scope of one question and answer in the minutes as a sub task of the shared task. For the segmentation task, 24 runs from five teams were submitted. Based on the obtained results, the best recall was 1.000, best precision was 0.940, and best F-measure was 0.895.
A Term Extraction Approach to Survey Analysis in Health Care
Authors: C{\'e}cile Robin, Mona Isazad Mashinchi, Fatemeh Ahmadi Zeleti, Adegboyega Ojo, Paul Buitelaar
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: ARC, Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis, Survey, Term Extraction

The voice of the customer has for a long time been a key focus of businesses in all domains. It has received a lot of attention from the research community in Natural Language Processing (NLP) resulting in many approaches to analyzing customers feedback ((aspect-based) sentiment analysis, topic modeling, etc.). In the health domain, public and private bodies are increasingly prioritizing patient engagement for assessing the quality of the service given at each stage of the care. Patient and customer satisfaction analysis relate in many ways. In the domain of health particularly, a more precise and insightful analysis is needed to help practitioners locate potential issues and plan actions accordingly. We introduce here an approach to patient experience with the analysis of free text questions from the 2017 Irish National Inpatient Survey campaign using term extraction as a means to highlight important and insightful subject matters raised by patients. We evaluate the results by mapping them to a manually constructed framework following the Activity, Resource, Context (ARC) methodology (Ordenes, 2014) and specific to the health care environment, and compare our results against manual annotations done on the full 2017 dataset based on those categories.
Automated Discovery of Mathematical Definitions in Text
Authors: Natalia Vanetik, Marina Litvak, Sergey Shevchuk, Lior Reznik
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Articles, Binary Classification, Definition Extraction, Question Answering, Sentence, Word Embeddings

Automatic definition extraction from texts is an important task that has numerous applications in several natural language processing fields such as summarization, analysis of scientific texts, automatic taxonomy generation, ontology generation, concept identification, and question answering. For definitions that are contained within a single sentence, this problem can be viewed as a binary classification of sentences into definitions and non-definitions. Definitions in scientific literature can be generic (Wikipedia) or more formal (mathematical articles). In this paper, we focus on automatic detection of one-sentence definitions in mathematical texts, which are difficult to separate from surrounding text. We experiment with several data representations, which include sentence syntactic structure and word embeddings, and apply deep learning methods such as convolutional neural network (CNN) and recurrent neural network (RNN), in order to identify mathematical definitions. Our experiments demonstrate the superiority of CNN and its combination with RNN, applied on the syntactically-enriched input representation. We also present a new dataset for definition extraction from mathematical texts. We demonstrate that the use of this dataset for training learning models improves the quality of definition extraction when these models are then used for other definition datasets. Our experiments with different domains approve that mathematical definitions require special treatment, and that using cross-domain learning is inefficient.
WN-Salience: A Corpus of News Articles with Entity Salience Annotations
Authors: Chuan Wu, Evangelos Kanoulas, Maarten de Rijke, Wei Lu
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Articles, Entity Linking

Entities can be found in various text genres, ranging from tweets and web pages to user queries submitted to web search engines. Existing research either considers all entities in the text equally important, or heuristics are used to measure their salience. We believe that a key reason for the relatively limited work on entity salience is the lack of appropriate datasets. To support research on entity salience, we present a new dataset, the WikiNews Salience dataset (WN-Salience), which can be used to benchmark tasks such as entity salience detection and salient entity linking. WN-Salience is built on top of Wikinews, a Wikimedia project whose mission is to present reliable news articles. Entities in Wikinews articles are identified by the authors of the articles and are linked to Wikinews categories when they are salient or to Wikipedia pages otherwise. The dataset is built automatically, and consists of approximately 7,000 news articles, and 90,000 in-text entity annotations. We compare the WN-Salience dataset against existing datasets on the task and analyze their differences. Furthermore, we conduct experiments on entity salience detection; the results demonstrate that WN-Salience is a challenging testbed that is complementary to existing ones.
Event Extraction from Unstructured Amharic Text
Authors: ephrem tadesse, Rosa Tsegaye, Kuulaa Qaqqabaa
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: BIG-bench Machine Learning, Event Extraction

In information extraction, event extraction is one of the types that extract the specific knowledge of certain incidents from texts. Event extraction has been done on different languages text but not on one of the Semitic language, Amharic. In this study, we present a system that extracts an event from unstructured Amharic text. The system has designed by the integration of supervised machine learning and rule-based approaches. We call this system a hybrid system. The system uses the supervised machine learning to detect events from the text and the handcrafted and the rule-based rules to extract the event from the text. For the event extraction, we have been using event arguments. Event arguments identify event triggering words or phrases that clearly express the occurrence of the event. The event argument attributes can be verbs, nouns, sometimes adjectives (such as Ìƒrg/wedding) and time as well. The hybrid system has compared with the standalone rule-based method that is well known for event extraction. The study has shown that the hybrid system has outperformed the standalone rule-based method.
Comparing Machine Learning and Deep Learning Approaches on NLP Tasks for the Italian Language
Authors: Bernardo Magnini, Alberto Lavelli, Simone Magnolini
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: BIG-bench Machine Learning, Classification, Deep Learning, Feature Engineering, General Classification, Sentiment Analysis, text-classification, Text Classification

We present a comparison between deep learning and traditional machine learning methods for various NLP tasks in Italian. We carried on experiments using available datasets (e.g., from the Evalita shared tasks) on two sequence tagging tasks (i.e., named entities recognition and nominal entities recognition) and four classification tasks (i.e., lexical relations among words, semantic relations among sentences, sentiment analysis and text classification). We show that deep learning approaches outperform traditional machine learning algorithms in sequence tagging, while for classification tasks that heavily rely on semantics approaches based on feature engineering are still competitive. We think that a similar analysis could be carried out for other languages to provide an assessment of machine learning / deep learning models across different languages.
MyFixit: An Annotated Dataset, Annotation Tool, and Baseline Methods for Information Extraction from Repair Manuals
Authors: Nima Nabizadeh, Dorothea Kolossa, Martin Heckmann
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Text instructions are among the most widely used media for learning and teaching. Hence, to create assistance systems that are capable of supporting humans autonomously in new tasks, it would be immensely productive, if machines were enabled to extract task knowledge from such text instructions. In this paper, we, therefore, focus on information extraction (IE) from the instructional text in repair manuals. This brings with it the multiple challenges of information extraction from the situated and technical language in relatively long and often complex instructions. To tackle these challenges, we introduce a semi-structured dataset of repair manuals. The dataset is annotated in a large category of devices, with information that we consider most valuable for an automated repair assistant, including the required tools and the disassembled parts at each step of the repair progress. We then propose methods that can serve as baselines for this IE task: an unsupervised method based on a bags-of-n-grams similarity for extracting the needed tools in each repair step, and a deep-learning-based sequence labeling model for extracting the identity of disassembled parts. These baseline methods are integrated into a semi-automatic web-based annotator application that is also available along with the dataset.
Towards Entity Spaces
Authors: Marieke van Erp, Paul Groth
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Entity Linking, Word Embeddings

Entities are a central element of knowledge bases and are important input to many knowledge-centric tasks including text analysis. For example, they allow us to find documents relevant to a specific entity irrespective of the underlying syntactic expression within a document. However, the entities that are commonly represented in knowledge bases are often a simplification of what is truly being referred to in text. For example, in a knowledge base, we may have an entity for Germany as a country but not for the more fuzzy concept of Germany that covers notions of German Population, German Drivers, and the German Government. Inspired by recent advances in contextual word embeddings, we introduce the concept of entity spaces - specific representations of a set of associated entities with near-identity. Thus, these entity spaces provide a handle to an amorphous grouping of entities. We developed a proof-of-concept for English showing how, through the introduction of entity spaces in the form of disambiguation pages, the recall of entity linking can be improved.
Evaluating Information Loss in Temporal Dependency Trees
Authors: Mustafa Ocal, Mark Finlayson
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Temporal Dependency Trees (TDTs) have emerged as an alternative to full temporal graphs for representing the temporal structure of texts, with a key advantage being that TDTs can be straightforwardly computed using adapted dependency parsers. Relative to temporal graphs, the tree form of TDTs naturally omits some fraction of temporal relationships, which intuitively should decrease the amount of temporal information available, potentially increasing temporal indeterminacy of the global ordering. We demonstrate a new method for quantifying this indeterminacy that relies on solving temporal constraint problems to extract timelines, and show that TDTs result in up to a 109{\%} increase in temporal indeterminacy over their corresponding temporal graphs for the three corpora we examine. On average, the increase in indeterminacy is 32{\%}, and we show that this increase is a result of the TDT representation eliminating on average only 2.4{\%} of total temporal relations. This result suggests that small differences can have big effects in temporal graphs, and the use of TDTs must be balanced against their deficiencies, with tasks requiring an accurate global temporal ordering potentially calling for use of the full temporal graph
Populating Legal Ontologies using Semantic Role Labeling
Authors: Llio Humphreys, Guido Boella, Luigi di Caro, Livio Robaldo, Leon van der Torre, Sepideh Ghanavati, Robert Muthuri
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Management, Semantic Role Labeling

This 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 could significantly reduce this bottleneck. The research question of this paper is: How to address the resource bottleneck problem of creating specialist knowledge management systems? In particular, how to semi-automate the extraction of norms and their elements to populate legal ontologies? This paper shows that the acquisition paradox can be addressed by combining state-of-the-art general-purpose NLP modules with pre- and post-processing using rules based on domain knowledge. It describes a Semantic Role Labeling based information extraction system to extract norms from legislation and represent them as structured norms in legal ontologies. The output is intended to help make laws more accessible, understandable, and searchable in legal document management systems such as Eunomos (Boella et al., 2016).
PST 2.0 -- Corpus of Polish Spatial Texts
Authors: Micha{\l} Marci{\'n}czuk, Marcin Oleksy, Jan Wieczorek
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

In the paper, we focus on modeling spatial expressions in texts. We present the guidelines used to annotate the PST 2.0 (Corpus of Polish Spatial Texts) {---} a corpus designed for training and testing the tools for spatial expression recognition. The corpus contains a set of texts gathered from texts collected from travel blogs available under Creative Commons license. We have defined our guidelines based on three existing specifications for English (SpatialML, SpatialRole Labelling from SemEval-2013 Task 3 and ISO-Space1.4 from SpaceEval 2014). We briefly present the existing specifications and discuss what modifications have been made to adapt the guidelines to the characteristics of the Polish language. We also describe the process of data collection and manual annotation, including inter-annotator agreement calculation and corpus statistics. In the end, we present detailed statistics of the PST 2.0 corpus, which include the number of components, relations, expressions, and the most common values of spatial indicators, motion indicators, path indicators, distances, directions, and regions.
Odinson: A Fast Rule-based Information Extraction Framework
Authors: Marco A. Valenzuela-Esc{\'a}rcega, Gus Hahn-Powell, Dane Bell
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

We present Odinson, a rule-based information extraction framework, which couples a simple yet powerful pattern language that can operate over multiple representations of text, with a runtime system that operates in near real time. In the Odinson query language, a single pattern may combine regular expressions over surface tokens with regular expressions over graphs such as syntactic dependencies. To guarantee the rapid matching of these patterns, our framework indexes most of the necessary information for matching patterns, including directed graphs such as syntactic dependencies, into a custom Lucene index. Indexing minimizes the amount of expensive pattern matching that must take place at runtime. As a result, the runtime system matches a syntax-based graph traversal in 2.8 seconds in a corpus of over 134 million sentences, nearly 150,000 times faster than its predecessor.
MathAlign: Linking Formula Identifiers to their Contextual Natural Language Descriptions
Authors: Maria Alexeeva, Rebecca Sharp, Marco A. Valenzuela-Esc{\'a}rcega, Jennifer Kadowaki, Adarsh Pyarelal, Clayton Morrison
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Information Retrieval, Reading Comprehension, Retrieval

Extending machine reading approaches to extract mathematical concepts and their descriptions is useful for a variety of tasks, ranging from mathematical information retrieval to increasing accessibility of scientific documents for the visually impaired. This entails segmenting mathematical formulae into identifiers and linking them to their natural language descriptions. We propose a rule-based approach for this task, which extracts LaTeX representations of formula identifiers and links them to their in-text descriptions, given only the original PDF and the location of the formula of interest. We also present a novel evaluation dataset for this task, as well as the tool used to create it.