pwc-archive/papers-with-abstracts huggingface dataset:
The Corpus Query Middleware of Tomorrow -- A Proposal for a Hybrid Corpus Query Architecture
Authors: Markus G{\"a}rtner
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Development of dozens of specialized corpus query systems and languages over the past decades has let to a diverse but also fragmented landscape. Today we are faced with a plethora of query tools that each provide unique features, but which are also not interoperable and often rely on very specific database back-ends or formats for storage. This severely hampers usability both for end users that want to query different corpora and also for corpus designers that wish to provide users with an interface for querying and exploration. We propose a hybrid corpus query architecture as a first step to overcoming this issue. It takes the form of a middleware system between user front-ends and optional database or text indexing solutions as back-ends. At its core is a custom query evaluation engine for index-less processing of corpus queries. With a flexible JSON-LD query protocol the approach allows communication with back-end systems to partially solve queries and offset some of the performance penalties imposed by the custom evaluation engine. This paper outlines the details of our first draft of aforementioned architecture.
Using full text indices for querying spoken language data
Authors: Elena Frick, Thomas Schmidt
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

As a part of the ZuMult-project, we are currently modelling a backend architecture that should provide query access to corpora from the Archive of Spoken German (AGD) at the Leibniz-Institute for the German Language (IDS). We are exploring how to reuse existing search engine frameworks providing full text indices and allowing to query corpora by one of the corpus query languages (QLs) established and actively used in the corpus research community. For this purpose, we tested MTAS - an open source Lucene-based search engine for querying on text with multilevel annotations. We applied MTAS on three oral corpora stored in the TEI-based ISO standard for transcriptions of spoken language (ISO 24624:2016). These corpora differ from the corpus data that MTAS was developed for, because they include interactions with two and more speakers and are enriched, inter alia, with timeline-based annotations. In this contribution, we report our test results and address issues that arise when search frameworks originally developed for querying written corpora are being transferred into the field of spoken language.
Challenges for Making Use of a Large Text Corpus such as the `AAC -- Austrian Academy Corpus' for Digital Literary Studies
Authors: Hanno Biber
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The challenges for making use of a large text corpus such as the {`}AAC {--} Austrian Academy Corpus{'} for the purposes of digital literary studies will be addressed in this presentation. The research question of how to use a digital text corpus of considerable size for such a specific research purpose is of interest for corpus research in general as it is of interest for digital literary text studies which rely to a large extent on large digital text corpora. The observations of the usage of lexical entities such as words, word forms, multi word units and many other linguistic units determine the way in which texts are being studied and explored. Larger entities have to be taken into account as well, which is why questions of semantic analysis and larger structures come into play. The texts of the AAC {--} Austrian Academy Corpus which was founded in 2001 are German language texts of historical and cultural significance from the time between 1848 and 1989. The aim of this study is to present possible research questions for corpus-based methodological approaches for the digital study of literary texts and to give examples of early experiments and experiences with making use of a large text corpus for these research purposes.
Czech National Corpus in 2020: Recent Developments and Future Outlook
Authors: Michal Kren
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The paper overviews the state of implementation of the Czech National Corpus (CNC) in all the main areas of its operation: corpus compilation, annotation, application development and user services. As the focus is on the recent development, some of the areas are described in more detail than the others. Close attention is paid to the data collection and, in particular, to the description of web application development. This is not only because CNC has recently seen a significant progress in this area, but also because we believe that end-user web applications shape the way linguists and other scholars think about the language data and about the range of possibilities they offer. This consideration is even more important given the variability of the CNC corpora.
Adding a Syntactic Annotation Level to the Corpus of Contemporary Romanian Language
Authors: Andrei Scutelnicu, Mar, Catalina uc, Dan Cristea
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

In this paper we present an experiment of augmenting the Corpus of Contemporary Romanian Language (CoRoLa) with the syntactic level of annotations, which would allow users to address queries about the syntax of Romanian sentences, in the Universal Dependency model. After a short introduction of CoRoLa, we describe the treebanks used to train the dependency parser, we show the evaluation results and the process of upgrading CoRoLa with the new level of annotations. The parser displaying the best accuracy with respect to recognition of heads and relations, out of three variants trained on manually built treebanks, was chosen. Keywords: Syntactic annotation, treebank, corpus, maltparser
Automatic Term Extraction from Newspaper Corpora: Making the Most of Specificity and Common Features
Authors: Patrick Drouin, Jean-Beno{\^\i}t Morel, Marie-Claude L{'} Homme
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Articles, Specificity, Term Extraction, Word Embeddings

The first step of any terminological work is to setup a reliable, specialized corpus composed of documents written by specialists and then to apply automatic term extraction (ATE) methods to this corpus in order to retrieve a first list of potential terms. In this paper, the experiment we describe differs quite drastically from this usual process since we are applying ATE to unspecialized corpora. The corpus used for this study was built from newspaper articles retrieved from the Web using a short list of keywords. The general intuition on which this research is based is that ATE based corpus comparison techniques can be used to capture both similarities and dissimilarities between corpora. The former are exploited through a termhood measure and the latter through word embeddings. Our initial results were validated manually and show that combining a traditional ATE method that focuses on dissimilarities between corpora to newer methods that exploit similarities (more specifically distributional features of candidates) leads to promising results.
TermPortal: A Workbench for Automatic Term Extraction from Icelandic Texts
Authors: Stein{\th}{\'o}r Steingr{\'\i}msson, {\'A}g{\'u}sta {\TH}orbergsd{\'o}ttir, Hjalti Danielsson, Gunnar Thor Ornolfsson
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Term Extraction

Automatic term extraction (ATE) from texts is critical for effective terminology work in small speech communities. We present TermPortal, a workbench for terminology work in Iceland, featuring the first ATE system for Icelandic. The tool facilitates standardization in terminology work in Iceland, as it exports data in standard formats in order to streamline gathering and distribution of the material. In the project we focus on the domain of finance in order to do be able to fulfill the needs of an important and large field. We present a comprehensive survey amongst the most prominent organizations in that field, the results of which emphasize the need for a good, up-to-date and accessible termbank and the willingness to use terms in Icelandic. Furthermore we present the ATE tool for Icelandic, which uses a variety of methods and shows great potential with a recall rate of up to 95{\%} and a high C-value, indicating that it competently finds term candidates that are important to the input text.
Translating Knowledge Representations with Monolingual Word Embeddings: the Case of a Thesaurus on Corporate Non-Financial Reporting
Authors: Mart{\'\i}n Quesada Zaragoza, Lianet Sep{\'u}lveda Torres, J{\'e}r{\^o}me Basdevant
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Cross-Lingual Word Embeddings, Document Classification, Word Embeddings

A common method of structuring information extracted from textual data is using a knowledge model (e.g. a thesaurus) to organise the information semantically. Creating and managing a knowledge model is already a costly task in terms of human effort, not to mention making it multilingual. Multilingual knowledge modelling is a common problem for both transnational organisations and organisations providing text analytics that want to analyse information in more than one language. Many organisations tend to develop their language resources first in one language (often English). When it comes to analysing data sources in other languages, either a lot of effort has to be invested in recreating the same knowledge base in a different language or the data itself has to be translated into the language of the knowledge model. In this paper, we propose an unsupervised method to automatically induce a given thesaurus into another language using only comparable monolingual corpora. The aim of this proposal is to employ cross-lingual word embeddings to map the set of topics in an already-existing English thesaurus into Spanish. With this in mind, we describe different approaches to generate the Spanish thesaurus terms and offer an extrinsic evaluation by using the obtained thesaurus, which covers non-financial topics in a multi-label document classification task, and we compare the results across these approaches.
Which Dependency Parser to Use for Distributional Semantics in a Specialized Domain?
Authors: Pauline Brunet, Olivier Ferret, Ludovic Tanguy
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

We present a study whose objective is to compare several dependency parsers for English applied to a specialized corpus for building distributional count-based models from syntactic dependencies. One of the particularities of this study is to focus on the concepts of the target domain, which mainly occur in documents as multi-terms and must be aligned with the outputs of the parsers. We compare a set of ten parsers in terms of syntactic triplets but also in terms of distributional neighbors extracted from the models built from these triplets, both with and without an external reference concerning the semantic relations between concepts. We show more particularly that some patterns of proximity between these parsers can be observed across our different evaluations, which could give insights for anticipating the performance of a parser for building distributional models from a given corpus
Terminology in Written Medical Reports: A Proposal of Text Enrichment to Favour its Comprehension by the Patient
Authors: Rosa Estop{\`a}, Alej L{\'o}pez-Fuentes, ra, Jorge M. Porras-Garzon
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The empowerment of the population and the democratisation of information regarding healthcare have revealed that there is a communication gap between health professionals and patients. The latter are constantly receiving more and more written information about their healthcare visits and treatments, but that does not mean they understand it. In this paper we focus on the patient{'}s lack of comprehension of medical reports. After linguistically characterising the medical report, we present the results of a survey that showed that patients have serious comprehension difficulties concerning the medical reports they receive, specifically problems regarding the medical terminology used in these texts, specifically in Spanish and Catalan. To favour the understanding of medical reports, we propose an automatic text enrichment strategy that generates linguistically and cognitively enriched medical reports which are more comprehensible to the patient, and which focus on the parts of the medical report that most interest the patient: the diagnosis and treatment sections.
A study of semantic projection from single word terms to multi-word terms in the environment domain
Authors: Yizhe WANG, Beatrice Daille, Nabil Hathout
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: valid

The semantic projection method is often used in terminology structuring to infer semantic relations between terms. Semantic projection relies upon the assumption of semantic compositionality: the relation that links simple term pairs remains valid in pairs of complex terms built from these simple terms. This paper proposes to investigate whether this assumption commonly adopted in natural language processing is actually valid. First, we describe the process of constructing a list of semantically linked multi-word terms (MWTs) related to the environmental field through the extraction of semantic variants. Second, we present our analysis of the results from the semantic projection. We find that contexts play an essential role in defining the relations between MWTs.
The NetViz terminology visualization tool and the use cases in karstology domain modeling
Authors: Senja Pollak, Vid Podpe{\v{c}}an, Dragana Miljkovic, Uro{\v{s}} Stepi{\v{s}}nik, {\v{S}}pela Vintar
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

We present the NetViz terminology visualization tool and apply it to the domain modeling of karstology, a subfield of geography studying karst phenomena. The developed tool allows for high-performance online network visualization where the user can upload the terminological data in a simple CSV format, define the nodes (terms, categories), edges (relations) and their properties (by assigning different node colors), and then edit and interactively explore domain knowledge in the form of a network. We showcase the usefulness of the tool on examples from the karstology domain, where in the first use case we visualize the domain knowledge as represented in a manually annotated corpus of domain definitions, while in the second use case we show the power of visualization for domain understanding by visualizing automatically extracted knowledge in the form of triplets extracted from the karstology domain corpus. The application is entirely web-based without any need for downloading or special configuration. The source code of the web application is also available under the permissive MIT license, allowing future extensions for developing new terminological applications.
Towards Automatic Thesaurus Construction and Enrichment.
Authors: Amir Hazem, Beatrice Daille, Lanza Claudia
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Semantic Similarity, Semantic Textual Similarity

Thesaurus construction with minimum human efforts often relies on automatic methods to discover terms and their relations. Hence, the quality of a thesaurus heavily depends on the chosen methodologies for: (i) building its content (terminology extraction task) and (ii) designing its structure (semantic similarity task). The performance of the existing methods on automatic thesaurus construction is still less accurate than the handcrafted ones of which is important to highlight the drawbacks to let new strategies build more accurate thesauri models. In this paper, we will provide a systematic analysis of existing methods for both tasks and discuss their feasibility based on an Italian Cybersecurity corpus. In particular, we will provide a detailed analysis on how the semantic relationships network of a thesaurus can be automatically built, and investigate the ways to enrich the terminological scope of a thesaurus by taking into account the information contained in external domain-oriented semantic sets.
Supporting terminology extraction with dependency parses
Authors: Malgorzata Marciniak, Piotr Rychlik, Agnieszka Mykowiecka
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Terminology extraction procedure usually consists of selecting candidates for terms and ordering them according to their importance for the given text or set of texts. Depending on the method used, a list of candidates contains different fractions of grammatically incorrect, semantically odd and irrelevant sequences. The aim of this work was to improve term candidate selection by reducing the number of incorrect sequences using a dependency parser for Polish.
Computational Aspects of Frame-based Meaning Representation in Terminology
Authors: Laura Giacomini, Johannes Sch{\"a}fer
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Term Extraction

Our contribution is part of a wider research project on term variation in German and concentrates on the computational aspects of a frame-based model for term meaning representation in the technical field. We focus on the role of frames (in the sense of Frame-Based Terminology) as the semantic interface between concepts covered by a domain ontology and domain-specific terminology. In particular, we describe methods for performing frame-based corpus annotation and frame-based term extraction. The aim of the contribution is to discuss the capacity of the model to automatically acquire semantic knowledge suitable for terminographic information tools such as specialised dictionaries, and its applicability to further specialised languages.
TermEval 2020: Shared Task on Automatic Term Extraction Using the Annotated Corpora for Term Extraction Research (ACTER) Dataset
Authors: Ayla Rigouts Terryn, Veronique Hoste, Patrick Drouin, Els Lefever
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Term Extraction

The TermEval 2020 shared task provided a platform for researchers to work on automatic term extraction (ATE) with the same dataset: the Annotated Corpora for Term Extraction Research (ACTER). The dataset covers three languages (English, French, and Dutch) and four domains, of which the domain of \textit{heart failure} was kept as a held-out test set on which final f1-scores were calculated. The aim was to provide a large, transparent, qualitatively annotated, and diverse dataset to the ATE research community, with the goal of promoting comparative research and thus identifying strengths and weaknesses of various state-of-the-art methodologies. The results show a lot of variation between different systems and illustrate how some methodologies reach higher precision or recall, how different systems extract different types of terms, how some are exceptionally good at finding rare terms, or are less impacted by term length. The current contribution offers an overview of the shared task with a comparative evaluation, which complements the individual papers by all participants.
TermEval 2020: TALN-LS2N System for Automatic Term Extraction
Authors: Amir Hazem, Bouh, M{\'e}rieme i, Florian Boudin, Beatrice Daille
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Term Extraction

Automatic terminology extraction is a notoriously difficult task aiming to ease effort demanded to manually identify terms in domain-specific corpora by automatically providing a ranked list of candidate terms. The main ways that addressed this task can be ranged in four main categories: (i) rule-based approaches, (ii) feature-based approaches, (iii) context-based approaches, and (iv) hybrid approaches. For this first TermEval shared task, we explore a feature-based approach, and a deep neural network multitask approach -BERT- that we fine-tune for term extraction. We show that BERT models (RoBERTa for English and CamemBERT for French) outperform other systems for French and English languages.
TermEval 2020: RACAI's automatic term extraction system
Authors: Vasile Pais, Radu Ion
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Term Extraction

This paper describes RACAI{'}s automatic term extraction system, which participated in the TermEval 2020 shared task on English monolingual term extraction. We discuss the system architecture, some of the challenges that we faced as well as present our results in the English competition.
TermEval 2020: Using TSR Filtering Method to Improve Automatic Term Extraction
Authors: Antoni Oliver, Merc{\`e} V{\`a}zquez
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: POS, Term Extraction

The identification of terms from domain-specific corpora using computational methods is a highly time-consuming task because terms has to be validated by specialists. In order to improve term candidate selection, we have developed the Token Slot Recognition (TSR) method, a filtering strategy based on terminological tokens which is used to rank extracted term candidates from domain-specific corpora. We have implemented this filtering strategy in TBXTools. In this paper we present the system we have used in the TermEval 2020 shared task on monolingual term extraction. We also present the evaluation results for the system for English, French and Dutch and for two corpora: corruption and heart failure. For English and French we have used a linguistic methodology based on POS patterns, and for Dutch we have used a statistical methodology based on n-grams calculation and filtering with stop-words. For all languages, TSR (Token Slot Recognition) filtering method has been applied. We have obtained competitive results, but there is still room for improvement of the system.
Beyond lexical semantics: notes on pragmatic frames
Authors: Oliver Czulo, Alex Ziem, er, Tiago Timponi Torrent
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: TAG

Framenets as an incarnation of frame semantics have been set up to deal with lexicographic issues (cf. Fillmore and Baker 2010, among others). They are thus concerned with lexical units (LUs) and the conceptual structure which categorizes these together. These lexically-evoked frames, however, do not reflect pragmatic properties of constructions (LUs and other types of constructions), such as expressing illocutions or being considered polite or very informal. From the viewpoint of a multilingual annotation effort, the Global FrameNet Shared Annotation Task, we discuss two phenomena, greetings and tag questions, which highlight the necessity both to investigate the role between construction and frame annotation on the one hand and to develop pragmatic frames describing social interactions which are not explicitly lexicalized.
Finding Corresponding Constructions in English and Japanese in a TED Talk Parallel Corpus using Frames-and-Constructions Analysis
Authors: Kyoko Ohara
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: valid

This paper reports on an effort to search for corresponding constructions in English and Japanese in a TED Talk parallel corpus, using frames-and-constructions analysis (Ohara, 2019; Ohara and Okubo, 2020; cf. Czulo, 2013, 2017). The purpose of the paper is two-fold: (1) to demonstrate the validity of frames-and-constructions analysis to search for corresponding constructions in typologically unrelated languages; and (2) to assess whether the {``}Do schools kill creativity?{''} TED Talk parallel corpus, annotated in various languages for Multilingual FrameNet, is a good starting place for building a multilingual constructicon. The analysis showed that similar to our previous findings involving texts in a Japanese to English bilingual children{'}s book, the TED Talk bilingual transcripts include pairs of constructions that share similar pragmatic functions. While the TED Talk parallel corpus constitutes a good resource for frame semantic annotation in multiple languages, it may not be the ideal place to start aligning constructions among typologically unrelated languages. Finally, this work shows that the proposed method, which focuses on heads of sentences, seems valid for searching for corresponding constructions in transcripts of spoken data, as well as in written data of typologically-unrelated languages.