pwc-archive/papers-with-abstracts huggingface dataset:
A Processing Platform Relating Data and Tools for Romanian Language
Authors: Vasile P{\u{a}}i{\textcommabelow{s}}, Radu Ion, Dan Tufi{\textcommabelow{s}}
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This paper presents RELATE (http://relate.racai.ro), a high-performance natural language platform designed for Romanian language. It is meant both for demonstration of available services, from text-span annotations to syntactic dependency trees as well as playing or automatically synthesizing Romanian words, and for the development of new annotated corpora. It also incorporates the search engines for the large COROLA reference corpus of contemporary Romanian and the Romanian wordnet. It integrates multiple text and speech processing modules and exposes their functionality through a web interface designed for the linguist researcher. It makes use of a scheduler-runner architecture, allowing processing to be distributed across multiple computing nodes. A series of input/output converters allows large corpora to be loaded, processed and exported according to user preferences.
LinTO Platform: A Smart Open Voice Assistant for Business Environments
Authors: Ilyes Rebai, Sami Benhamiche, Kate Thompson, Zied Sellami, Damien Laine, Jean-Pierre Lorr{\'e}
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Spoken Language Understanding

In this paper, we present LinTO, an intelligent voice platform and smart room assistant for improving efficiency and productivity in business. Our objective is to build a Spoken Language Understanding system that maintains high performance in both Automatic Speech Recognition (ASR) and Natural Language Processing while being portable and scalable. In this paper we describe the LinTO architecture and our approach to ASR engine training which takes advantage of recent advances in deep learning while guaranteeing high-performance real-time processing. Unlike the existing solutions, the LinTO platform is open source for commercial and non-commercial use
The COMPRISE Cloud Platform
Authors: Raivis Skadi{\c{n}}{\v{s}}, Askars Salimbajevs
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This paper presents the COMPRISE cloud platform that is developed in the H2020 project. We present an overview of the COMPRISE project, its main goals, components, and how the cloud platform fits in the context of the overall project. The COMPRISE cloud platform is presented in more detail {--} main users, use scenarios, functions, implementation details, and how it will be used by both COMPRISE{'}s targeted audience and the broader language-technology community.
From Linguistic Research Projects to Language Technology Platforms: A Case Study in Learner Data
Authors: Ann Sousa, a, Nicolas Ballier, Thomas Gaillat, Bernardo Stearns, Manel Zarrouk, Andrew Simpkin, Manon Bouy{\'e}
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This paper describes the workflow and architecture adopted by a linguistic research project. We report our experience and present the research outputs turned into resources that we wish to share with the community. We discuss the current limitations and the next steps that could be taken for the scaling and development of our research project. Allying NLP and language-centric AI, we discuss similar projects and possible ways to start collaborating towards potential platform interoperability.
Towards an Ontology Based on Hallig-Wartburg's Begriffssystem for Historical Linguistic Linked Data
Authors: Sabine Tittel, Frances Gillis-Webber, Aless Nannini, ro A.
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

To empower end users in searching for historical linguistic content with a performance that far exceeds the research functions offered by websites of, e.g., historical dictionaries, is undoubtedly a major advantage of (Linguistic) Linked Open Data ([L]LOD). An important aim of lexicography is to enable a language-independent, onomasiological approach, and the modelling of linguistic resources following the LOD paradigm facilitates the semantic mapping to ontologies making this approach possible. Hallig-Wartburg{'}s Begriffssystem (HW) is a well-known extra-linguistic conceptual system used as an onomasiological framework by many historical lexicographical and lexicological works. Published in 1952, HW has meanwhile been digitised. With proprietary XML data as the starting point, our goal is the transformation of HW into Linked Open Data in order to facilitate its use by linguistic resources modelled as LOD. In this paper, we describe the particularities of the HW conceptual model and the method of converting HW: We discuss two approaches, (i) the representation of HW in RDF using SKOS, the SKOS thesaurus extension, and XKOS, and (ii) the creation of a lightweight ontology expressed in OWL, based on the RDF/SKOS model. The outcome is illustrated with use cases of medieval Gascon, and Italian.
Transforming the Cologne Digital Sanskrit Dictionaries into OntoLex-Lemon
Authors: Francisco Mondaca, Felix Rau
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The Cologne Digital Sanskrit Dictionaries (CDSD) is a large collection of complex digitized Sanskrit dictionaries, consisting of over thirty-five works, and is the most prominent collection of Sanskrit dictionaries worldwide. In this paper we evaluate two methods for transforming the CDSD into Ontolex-Lemon based on a modelling exercise. The first method that we evaluate consists of applying RDFa to the existent TEI-P5 files. The second method consists of transforming the TEI-encoded dictionaries into new files containing RDF triples modelled in OntoLex-Lemon. As a result of the modelling exercise we choose the second method: to transform TEI-encoded lexical data into Ontolex-Lemon by creating new files containing exclusively RDF triples.
Representing Temporal Information in Lexical Linked Data Resources
Authors: Fahad Khan
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The increasing recognition of the utility of Linked Data as a means of publishing lexical resource has helped to underline the need for RDF based data models which have the flexibility and expressivity to be able to represent the most salient kinds of information contained in such resources as structured data, including, notably, information relating to time and the temporal dimension. In this article we describe a perdurantist approach to modelling diachronic lexical information which builds upon work which we have previously presented and which is based on the ontolex-lemon vocabulary. We present two extended examples, one taken from the Oxford English Dictionary, the other from a work on etymology, to show how our approach can handle different kinds of temporal information often found in lexical resources.
From Linguistic Descriptions to Language Profiles
Authors: Shafqat Mumtaz Virk, Harald Hammarstr{\"o}m, Lars Borin, Markus Forsberg, S{\o}ren Wichmann
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Descriptive

Language catalogues and typological databases are two important types of resources containing different types of knowledge about the world{'}s natural languages. The former provide metadata such as number of speakers, location (in prose descriptions and/or GPS coordinates), language code, literacy, etc., while the latter contain information about a set of structural and functional attributes of languages. Given that both types of resources are developed and later maintained manually, there are practical limits as to the number of languages and the number of features that can be surveyed. We introduce the concept of a language profile, which is intended to be a structured representation of various types of knowledge about a natural language extracted semi-automatically from descriptive documents and stored at a central location. It has three major parts: (1) an introductory; (2) an attributive; and (3) a reference part, each containing different types of knowledge about a given natural language. As a case study, we develop and present a language profile of an example language. At this stage, a language profile is an independent entity, but in the future it is envisioned to become part of a network of language profiles connected to each other via various types of relations. Such a representation is expected to be suitable both for humans and machines to read and process for further deeper linguistic analyses and/or comparisons.
Terme-\`a-LLOD: Simplifying the Conversion and Hosting of Terminological Resources as Linked Data
Authors: Maria Pia di Buono, Philipp Cimiano, Mohammad Fazleh Elahi, Frank Grimm
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

In recent years, there has been increasing interest in publishing lexicographic and terminological resources as linked data. The benefit of using linked data technologies to publish terminologies is that terminologies can be linked to each other, thus creating a cloud of linked terminologies that cross domains, languages and that support advanced applications that do not work with single terminologies but can exploit multiple terminologies seamlessly. We present Terme-{`}a-LLOD (TAL), a new paradigm for transforming and publishing terminologies as linked data which relies on a virtualization approach. The approach rests on a preconfigured virtual image of a server that can be downloaded and installed. We describe our approach to simplifying the transformation and hosting of terminological resources in the remainder of this paper. We provide a proof-of-concept for this paradigm showing how to apply it to the conversion of the well-known IATE terminology as well as to various smaller terminologies. Further, we discuss how the implementation of our paradigm can be integrated into existing NLP service infrastructures that rely on virtualization technology. While we apply this paradigm to the transformation and hosting of terminologies as linked data, the paradigm can be applied to any other resource format as well.
Annohub -- Annotation Metadata for Linked Data Applications
Authors: Frank Abromeit, Christian F{\"a}th, Luis Glaser
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

We introduce a new dataset for the Linguistic Linked Open Data (LLOD) cloud that will provide metadata about annotation and language information harvested from annotated language resources like corpora freely available on the internet. To our knowledge annotation metadata is not provided by any metadata provider, e.g. linghub, datahub or CLARIN so far. On the other hand, language metadata that is found on such portals is rarely provided in machine-readable form, especially as Linked Data. In this paper, we describe the harvesting process, content and structure of the new dataset and its application in the Lin|gu|is|tik portal, a research platform for linguists. Aside from that, we introduce tools for the conversion of XML encoded language resources to the CoNLL format. The generated RDF data as well as the XML-converter application are made public under an open license.
Challenges of Word Sense Alignment: Portuguese Language Resources
Authors: Ana Salgado, Sina Ahmadi, Alberto Sim{\~o}es, John Philip McCrae, Rute Costa
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This paper reports on an ongoing task of monolingual word sense alignment in which a comparative study between the Portuguese Academy of Sciences Dictionary and the Dicion{\'a}rio Aberto is carried out in the context of the ELEXIS (European Lexicographic Infrastructure) project. Word sense alignment involves searching for matching senses within dictionary entries of different lexical resources and linking them, which poses significant challenges. The lexicographic criteria are not always entirely consistent within individual dictionaries and even less so across different projects where different options may have been assumed in terms of structure and especially wording techniques of lexicographic glosses. This hinders the task of matching senses. We aim to present our annotation workflow in Portuguese using the Semantic Web technologies. The results obtained are useful for the discussion within the community.
A Lime-Flavored REST API for Alignment Services
Authors: Manuel Fiorelli, Arm Stellato, o
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

A practical alignment service should be flexible enough to handle the varied alignment scenarios that arise in the real world, while minimizing the need for manual configuration. MAPLE, an orchestration framework for ontology alignment, supports this goal by coordinating a few loosely coupled actors, which communicate and cooperate to solve a matching task using explicit metadata about the input ontologies, other available resources and the task itself. The alignment task is thus summarized by a report listing its characteristics and suggesting alignment strategies. The schema of the report is based on several metadata vocabularies, among which the Lime module of the OntoLex-Lemon model is particularly important, summarizing the lexical content of the input ontologies and describing external language resources that may be exploited for performing the alignment. In this paper, we propose a REST API that enables the participation of downstream alignment services in the process orchestrated by MAPLE, helping them self-adapt in order to handle heterogeneous alignment tasks and scenarios. The realization of this alignment orchestration effort has been performed through two main phases: we first described its API as an OpenAPI specification (a la API-first), which we then exploited to generate server stubs and compliant client libraries. Finally, we switched our focus to the integration of existing alignment systems, with one fully integrated system and an additional one being worked on, in the effort to propose the API as a valuable addendum to any system being developed.
Using OntoLex-Lemon for Representing and Interlinking Lexicographic Collections of Bavarian Dialects
Authors: Yalemisew Abgaz
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This paper describes the ongoing work in converting the lexicographic collection of a non-standard German language dataset (Bavarian Dialects) into a Linguistic Linked Open Data (LLOD) format. The collection is divided into two: questionnaire dataset (DB{\"O}) which contains details of the questionnaires, questions, collectors, paper slips etc., and the lexical dataset (WB{\"O}) which contains lexical entries (answers) collected in response to the questions. In its current form, the lexical dataset is available in a TEI/XML format separately from the questionnaire dataset. This paper presents the mapping of the lexical entries in the TEI/XML format into LLOD format using the Ontolex-Lemon model. The paper shows how the data in TEI/XML format is transformed into LLOD and produces a lexicon for Bavarian Dialects. It also presents the approach used to interlink the original questions with the lexical entries. The resulting lexicon complements the questionnaire dataset, which is already in a LLOD format, by semantically interlinking the original questions with the answers and vice-versa.
Involving Lexicographers in the LLOD Cloud with LexO, an Easy-to-use Editor of Lemon Lexical Resources
Authors: Bell, Andrea i, Emiliano Giovannetti
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

In this contribution, we show LexO, a user-friendly web collaborative editor of lexical resources based on the lemon model. LexO has been developed in the context of Digital Humanities projects, in which a key point in the design of an editor was the ease of use by lexicographers with no skill in Linked Data or Semantic Web technologies. Though the tool already allows creating a lemon lexicon from scratch and lets a team of users work on it collaboratively, many developments are possible. The involvement of the LLOD community appears now crucial both to find new users and application fields where to test it, and, even more importantly, to understand in which way it should evolve.
Supervised Hypernymy Detection in Spanish through Order Embeddings
Authors: Gun Woo Lee, Mathias Etcheverry, Fern, Daniel ez Sanchez, Dina Wonsever
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Transfer Learning

This paper addresses the task of supervised hypernymy detection in Spanish through an order embedding and using pretrained word vectors as input. Although the task has been widely addressed in English, there is not much work in Spanish, and according to our knowledge there is not any available dataset for supervised hypernymy detection in Spanish. We built a supervised hypernymy dataset for Spanish from WordNet and corpus statistics information, with different versions according to the lexical intersection between its partitions: random and lexical split. We show the results of using the resulting dataset within an order embedding consuming pretrained word vectors as input. We show the ability of pretrained word vectors to transfer learning to unseen lexical units according to the results in the lexical split dataset. To finish, we study the results of giving additional information in training time, such as, cohyponym links and instances extracted through patterns.
Lexemes in Wikidata: 2020 status
Authors: Finn Nielsen
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Descriptive

Wikidata now records data about lexemes, senses and lexical forms and exposes them as Linguistic Linked Open Data. Since lexemes in Wikidata was first established in 2018, this data has grown considerable in size. Links between lexemes in different languages can be made, e.g., through a derivation property or senses. We present some descriptive statistics about the lexemes of Wikidata, focusing on the multilingual aspects and show that there are still relatively few multilingual links.
Extrapolating Binder Style Word Embeddings to New Words
Authors: Jacob Turton, David Vinson, Robert Smith
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Semantic Similarity, Semantic Textual Similarity, Word Embeddings

Word embeddings such as Word2Vec not only uniquely identify words but also encode important semantic information about them. However, as single entities they are difficult to interpret and their individual dimensions do not have obvious meanings. A more intuitive and interpretable feature space based on neural representations of words was presented by Binder and colleagues (2016) but is only available for a very limited vocabulary. Previous research (Utsumi, 2018) indicates that Binder features can be predicted for words from their embedding vectors (such as Word2Vec), but only looked at the original Binder vocabulary. This paper aimed to demonstrate that Binder features can effectively be predicted for a large number of new words and that the predicted values are sensible. The results supported this, showing that correlations between predicted feature values were consistent with those in the original Binder dataset. Additionally, vectors of predicted values performed comparatively to established embedding models in tests of word-pair semantic similarity. Being able to predict Binder feature space vectors for any number of new words opens up many uses not possible with the original vocabulary size.
Towards the First Dyslexic Font in Russian
Authors: Svetlana Alexeeva, Aleks Dobrego, ra, Vladislav Zubov
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Sentence

Texts comprise a large part of visual information that we process every day, so one of the tasks of language science is to make them more accessible. However, often the text design process is focused on the font size, but not on its type; which might be crucial especially for the people with reading disabilities. The current paper represents a study on text accessibility and the first attempt to create a research-based accessible font for Cyrillic letters. This resulted in the dyslexic-specific font, LexiaD. Its design rests on the reduction of inter-letter similarity of the Russian alphabet. In evaluation stage, dyslexic and non-dyslexic children were asked to read sentences from the Children version of the Russian Sentence Corpus. We tested the readability of LexiaD compared to PT Sans and PT Serif fonts. The results showed that all children had some advantage in letter feature extraction and information integration while reading in LexiaD, but lexical access was improved when sentences were rendered in PT Sans or PT Serif. Therefore, in several aspects, LexiaD proved to be faster to read and could be recommended to use by dyslexics who have visual deficiency or those who struggle with text understanding resulting in re-reading.
Towards Best Practices for Leveraging Human Language Processing Signals for Natural Language Processing
Authors: Nora Hollenstein, Maria Barrett, Lisa Beinborn
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: EEG, Electroencephalogram (EEG)

NLP models are imperfect and lack intricate capabilities that humans access automatically when processing speech or reading a text. Human language processing data can be leveraged to increase the performance of models and to pursue explanatory research for a better understanding of the differences between human and machine language processing. We review recent studies leveraging different types of cognitive processing signals, namely eye-tracking, M/EEG and fMRI data recorded during language understanding. We discuss the role of cognitive data for machine learning-based NLP methods and identify fundamental challenges for processing pipelines. Finally, we propose practical strategies for using these types of cognitive signals to enhance NLP models.
Language Models for Cloze Task Answer Generation in Russian
Authors: Anastasia Nikiforova, Sergey Pletenev, Daria Sinitsyna, Semen Sorokin, Anastasia Lopukhina, Nick Howell
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Answer Generation, Language Modeling, Language Modelling, valid

Linguistics predictability is the degree of confidence in which language unit (word, part of speech, etc.) will be the next in the sequence. Experiments have shown that the correct prediction simplifies the perception of a language unit and its integration into the context. As a result of an incorrect prediction, language processing slows down. Currently, to get a measure of the language unit predictability, a neurolinguistic experiment known as a cloze task has to be conducted on a large number of participants. Cloze tasks are resource-consuming and are criticized by some researchers as an insufficiently valid measure of predictability. In this paper, we compare different language models that attempt to simulate human respondents{'} performance on the cloze task. Using a language model to create cloze task simulations would require significantly less time and conduct studies related to linguistic predictability.