pwc-archive/papers-with-abstracts huggingface dataset:
MorphAGram, Evaluation and Framework for Unsupervised Morphological Segmentation
Authors: Esk, Ramy er, Francesca Callejas, Elizabeth Nichols, Judith Klavans, Smar Muresan, a
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Segmentation

Computational morphological segmentation has been an active research topic for decades as it is beneficial for many natural language processing tasks. With the high cost of manually labeling data for morphology and the increasing interest in low-resource languages, unsupervised morphological segmentation has become essential for processing a typologically diverse set of languages, whether high-resource or low-resource. In this paper, we present and release MorphAGram, a publicly available framework for unsupervised morphological segmentation that uses Adaptor Grammars (AG) and is based on the work presented by Eskander et al. (2016). We conduct an extensive quantitative and qualitative evaluation of this framework on 12 languages and show that the framework achieves state-of-the-art results across languages of different typologies (from fusional to polysynthetic and from high-resource to low-resource).
CTAP for Italian: Integrating Components for the Analysis of Italian into a Multilingual Linguistic Complexity Analysis Tool
Authors: Nadezda Okinina, Jennifer-Carmen Frey, Zarah Weiss
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Diversity

Linguistic complexity research being a very actively developing field, an increasing number of text analysis tools are created that use natural language processing techniques for the automatic extraction of quantifiable measures of linguistic complexity. While most tools are designed to analyse only one language, the CTAP open source linguistic complexity measurement tool is capable of processing multiple languages, making cross-lingual comparisons possible. Although it was originally developed for English, the architecture has been ex-tended to support multi-lingual analyses. Here we present the Italian component of CTAP, describe its implementation and compare it to the existing linguistic complexity tools for Italian. Offering general text length statistics and features for lexical, syntactic, and morpho-syntactic complexity (including measures of lexical frequency, lexical diversity, lexical and syntactical variation, part-of-speech density), CTAP is currently the most comprehensive linguistic complexity measurement tool for Italian and the only one allowing the comparison of Italian texts to multiple other languages within one tool.
Do you Feel Certain about your Annotation? A Web-based Semantic Frame Annotation Tool Considering Annotators' Concerns and Behaviors
Authors: Regina Stodden, Behrang Qasemizadeh, Laura Kallmeyer
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Active Learning, Task 2

In this system demonstration paper, we present an open-source web-based application with a responsive design for modular semantic frame annotation (SFA). Besides letting experienced and inexperienced users do suggestion-based and slightly-controlled annotations, the system keeps track of the time and changes during the annotation process and stores the users{'} confidence with the current annotation. This collected metadata can be used to get insights regarding the difficulty of an annotation with the same type or frame or can be used as an input of an annotation cost measurement for an active learning algorithm. The tool was already used to build a manually annotated corpus with semantic frames and its arguments for task 2 of SemEval 2019 regarding unsupervised lexical frame induction (QasemiZadeh et al., 2019). Although English sentences from the Wall Street Journal corpus of the Penn Treebank were annotated for this task, it is also possible to use the proposed tool for the annotation of sentences in other languages.
Seq2SeqPy: A Lightweight and Customizable Toolkit for Neural Sequence-to-Sequence Modeling
Authors: Raheel Qader, Fran{\c{c}}ois Portet, Cyril Labbe
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

We present Seq2SeqPy a lightweight toolkit for sequence-to-sequence modeling that prioritizes simplicity and ability to customize the standard architectures easily. The toolkit supports several known architectures such as Recurrent Neural Networks, Pointer Generator Networks, and transformer model. We evaluate the toolkit on two datasets and we show that the toolkit performs similarly or even better than a very widely used sequence-to-sequence toolkit.
Profiling-UD: a Tool for Linguistic Profiling of Texts
Authors: Dominique Brunato, Andrea Cimino, Felice Dell{'}Orletta, Giulia Venturi, Simonetta Montemagni
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Author Profiling

In this paper, we introduce Profiling{--}UD, a new text analysis tool inspired to the principles of linguistic profiling that can support language variation research from different perspectives. It allows the extraction of more than 130 features, spanning across different levels of linguistic description. Beyond the large number of features that can be monitored, a main novelty of Profiling{--}UD is that it has been specifically devised to be multilingual since it is based on the Universal Dependencies framework. In the second part of the paper, we demonstrate the effectiveness of these features in a number of theoretical and applicative studies in which they were successfully used for text and author profiling.
EstNLTK 1.6: Remastered Estonian NLP Pipeline
Authors: Sven Laur, Siim Orasmaa, Dage S{\"a}rg, Paul Tammo
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Morphological Analysis

The goal of the EstNLTK Python library is to provide a unified programming interface for natural language processing in Estonian. As such, previous versions of the library have been immensely successful both in academic and industrial circles. However, they also contained serious structural limitations {--} it was hard to add new components and there was a lack of fine-grained control needed for back-end programming. These issues have been explicitly addressed in the EstNLTK library while preserving the intuitive interface for novices. We have remastered the basic NLP pipeline by adding many data cleaning steps that are necessary for analyzing real-life texts, and state of the art components for morphological analysis and fact extraction. Our evaluation on unlabelled data shows that the remastered basic NLP pipeline outperforms both the previous version of the toolkit, as well as neural models of StanfordNLP. In addition, EstNLTK contains a new interface for storing, processing and querying text objects in Postgres database which greatly simplifies processing of large text collections. EstNLTK is freely available under the GNU GPL version 2 license, which is standard for academic software.
A Tree Extension for CoNLL-RDF
Authors: Christian Chiarcos, Luis Glaser
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Knowledge Graphs, Sentence

The technological bridges between knowledge graphs and natural language processing are of utmost importance for the future development of language technology. CoNLL-RDF is a technology that provides such a bridge for popular one-word-per-line formats as widely used in NLP (e.g., the CoNLL Shared Tasks), annotation (Universal Dependencies, Unimorph), corpus linguistics (Corpus WorkBench, CWB) and digital lexicography (SketchEngine): Every empty-line separated table (usually a sentence) is parsed into an graph, can be freely manipulated and enriched using W3C-standardized RDF technology, and then be serialized back into in a TSV format, RDF or other formats. An important limitation is that CoNLL-RDF provides native support for word-level annotations only. This does include dependency syntax and semantic role annotations, but neither phrase structures nor text structure. We describe the extension of the CoNLL-RDF technology stack for two vocabulary extensions of CoNLL-TSV, the PTB bracket notation used in earlier CoNLL Shared Tasks and the extension with XML markup elements featured by CWB and SketchEngine. In order to represent the necessary extensions of the CoNLL vocabulary in an adequate fashion, we employ the POWLA vocabulary for representing and navigating in tree structures.
Lemmatising Verbs in Middle English Corpora: The Benefit of Enriching the Penn-Helsinki Parsed Corpus of Middle English 2 (PPCME2), the Parsed Corpus of Middle English Poetry (PCMEP), and A Parsed Linguistic Atlas of Early Middle English (PLAEME)
Authors: Carola Trips, Michael Percillier
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: LEMMA

This paper describes the lemmatisation of three annotated corpora of Middle English{---}the Penn-Helsinki Parsed Corpus of Middle English 2 (PPCME2), the Parsed Corpus of Middle English Poetry (PCMEP), and A Parsed Linguistic Atlas of Early Middle English (PLAEME) {---} which is a prerequisite for systematically investigating the argument structures of verbs of the given time. Creating this tool and enriching existing parsed corpora of Middle English is part of the project Borrowing of Argument Structure in Contact Situations (BASICS) which seeks to explain to which extent verbs copied from Old French had an impact on the grammar of Middle English. First, we lemmatised the PPCME2 by (1) creating an inventory of form-lemma correspondences linking forms in the PPCME2 to lemmas in the MED, and (2) inserting this lemma information into the corpus (precision: 94.85{\%}, recall: 98.92{\%}). Second, we enriched the PCMEP and PLAEME, which adopted the annotation format of the PPCME2, with verb lemmas to undertake studies that fill the well-known data gap in the subperiod (1250--1350) of the PPCME2. The case study of reflexives shows that with our method we gain much more reliable results in terms of diachrony, diatopy and contact-induced change.
CoCo: A Tool for Automatically Assessing Conceptual Complexity of Texts
Authors: Sanja Stajner, Sergiu Nisioi, Ioana Hulpu{\textcommabelow{s}}
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Text Simplification

Traditional text complexity assessment usually takes into account only syntactic and lexical text complexity. The task of automatic assessment of conceptual text complexity, important for maintaining reader{'}s interest and text adaptation for struggling readers, has only been proposed recently. In this paper, we present CoCo - a tool for automatic assessment of conceptual text complexity, based on using the current state-of-the-art unsupervised approach. We make the code and API freely available for research purposes, and describe the code and the possibility for its personalization and adaptation in details. We compare the current implementation with the state of the art, discussing the influence of the choice of entity linker on the performances of the tool. Finally, we present results obtained on two widely used text simplification corpora, discussing the full potential of the tool.
PyVallex: A Processing System for Valency Lexicon Data
Authors: Jonathan Verner, Anna Vernerov{\'a}
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

PyVallex is a Python-based system for presenting, searching/filtering, editing/extending and automatic processing of machine-readable lexicon data originally available in a text-based format. The system consists of several components: a parser for the specific lexicon format used in several valency lexicons, a data-validation framework, a regular expression based search engine, a map-reduce style framework for querying the lexicon data and a web-based interface integrating complex search and some basic editing capabilities. PyVallex provides most of the typical functionalities of a Dictionary Writing System (DWS), such as multiple presentation modes for the underlying lexical database, automatic evaluation of consistency tests, and a mechanism of merging updates coming from multiple sources. The editing functionality is currently limited to the client-side interface and edits of existing lexical entries, but additional script-based operations on the database are also possible. The code is published under the open source MIT license and is also available in the form of a Python module for integrating into other software.
Editing OntoLex-Lemon in VocBench 3
Authors: Manuel Fiorelli, Arm Stellato, o, Tiziano Lorenzetti, Andrea Turbati, Peter Schmitz, Enrico Francesconi, Najeh Hajlaoui, Brahim Batouche
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

OntoLex-Lemon is a collection of RDF vocabularies for specifying the verbalization of ontologies in natural language. Beyond its original scope, OntoLex-Lemon, as well as its predecessor Monnet lemon, found application in the Linguistic Linked Open Data cloud to represent and interlink language resources on the Semantic Web. Unfortunately, generic ontology and RDF editors were considered inconvenient to use with OntoLex-Lemon because of its complex design patterns and other peculiarities, including indirection, reification and subtle integrity constraints. This perception led to the development of dedicated editors, trading the flexibility of RDF in combining different models (and the features already available in existing RDF editors) for a more direct and streamlined editing of OntoLex-Lemon patterns. In this paper, we investigate on the benefits gained by extending an already existing RDF editor, VocBench 3, with capabilities closely tailored to OntoLex-Lemon and on the challenges that such extension implies. The outcome of such investigation is twofold: a vertical assessment of a new editor for OntoLex-Lemon and, in the broader scope of RDF editor design, a new perspective on which flexibility and extensibility characteristics an editor should meet in order to cover new core modeling vocabularies, for which OntoLex-Lemon represents a use case.
MALT-IT2: A New Resource to Measure Text Difficulty in Light of CEFR Levels for Italian L2 Learning
Authors: Luciana Forti, Giuliana Grego Bolli, Filippo Santarelli, Valentino Santucci, Stefania Spina
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This paper presents a new resource for automatically assessing text difficulty in the context of Italian as a second or foreign language learning and teaching. It is called MALT-IT2, and it automatically classifies inputted texts according to the CEFR level they are more likely to belong to. After an introduction to the field of automatic text difficulty assessment, and an overview of previous related work, we describe the rationale of the project, the corpus and computational system it is based on. Experiments were conducted in order to investigate the reliability of the system. The results show that the system is able to obtain a good prediction accuracy, while a further analysis was conducted in order to identify the categories of features which mostly influenced the predictions.
Fintan - Flexible, Integrated Transformation and Annotation eNgineering
Authors: Christian F{\"a}th, Christian Chiarcos, Bj{\"o}rn Ebbrecht, Maxim Ionov
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Entity Linking, Management

We introduce the Flexible and Integrated Transformation and Annotation eNgeneering (Fintan) platform for converting heterogeneous linguistic resources to RDF. With its modular architecture, workflow management and visualization features, Fintan facilitates the development of complex transformation pipelines by integrating generic RDF converters and augmenting them with extended graph processing capabilities: Existing converters can be easily deployed to the system by means of an ontological data structure which renders their properties and the dependencies between transformation steps. Development of subsequent graph transformation steps for resource transformation, annotation engineering or entity linking is further facilitated by a novel visual rendering of SPARQL queries. A graphical workflow manager allows to easily manage the converter modules and combine them to new transformation pipelines. Employing the stream-based graph processing approach first implemented with CoNLL-RDF, we address common challenges and scalability issues when transforming resources and showcase the performance of Fintan by means of a purely graph-based transformation of the Universal Morphology data to RDF.
Contemplata, a Free Platform for Constituency Treebank Annotation
Authors: Jakub Waszczuk, Ilaine Wang, Jean-Yves Antoine, Ana{\"\i}s Halftermeyer
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This paper describes Contemplata, an annotation platform that offers a generic solution for treebank building as well as treebank enrichment with relations between syntactic nodes. Contemplata is dedicated to the annotation of constituency trees. The framework includes support for syntactic parsers, which provide automatic annotations to be manually revised. The balanced strategy of annotation between automatic parsing and manual revision allows to reduce the annotator workload, which favours data reliability. The paper presents the software architecture of Contemplata, describes its practical use and eventually gives two examples of annotation projects that were conducted on the platform.
Interchange Formats for Visualization: LIF and MMIF
Authors: Kyeongmin Rim, Kelley Lynch, Marc Verhagen, Nancy Ide, James Pustejovsky
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Data Visualization

Promoting interoperrable computational linguistics (CL) and natural language processing (NLP) application platforms and interchange-able data formats have contributed improving discoverabilty and accessbility of the openly available NLP software. In this paper, wediscuss the enhanced data visualization capabilities that are also enabled by inter-operating NLP pipelines and interchange formats.For adding openly available visualization tools and graphical annotation tools to the Language Applications Grid (LAPPS Grid) andComputational Linguistics Applications for Multimedia Services (CLAMS) toolboxes, we have developed interchange formats that cancarry annotations and metadata for text and audiovisual source data. We descibe those data formats and present case studies where wesuccessfully adopt open-source visualization tools and combine them with CL tools.
Developing NLP Tools with a New Corpus of Learner Spanish
Authors: Sam Davidson, Aaron Yamada, Fern, Paloma ez Mira, Car, Agustina o, Claudia H. Sanchez Gutierrez, Kenji Sagae
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Grammatical Error Correction

The development of effective NLP tools for the L2 classroom depends largely on the availability of large annotated corpora of language learner text. While annotated learner corpora of English are widely available, large learner corpora of Spanish are less common. Those Spanish corpora that are available do not contain the annotations needed to facilitate the development of tools beneficial to language learners, such as grammatical error correction. As a result, the field has seen little research in NLP tools designed to benefit Spanish language learners and teachers. We introduce COWS-L2H, a freely available corpus of Spanish learner data which includes error annotations and parallel corrected text to help researchers better understand L2 development, to examine teaching practices empirically, and to develop NLP tools to better serve the Spanish teaching community. We demonstrate the utility of this corpus by developing a neural-network based grammatical error correction system for Spanish learner writing.
DeepNLPF: A Framework for Integrating Third Party NLP Tools
Authors: Francisco Rodrigues, Rinaldo Lima, William Domingues, Robson Fidalgo, Adrian Chifu, Bernard Espinasse, S{\'e}bastien Fournier
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Management

Natural Language Processing (NLP) of textual data is usually broken down into a sequence of several subtasks, where the output of one the subtasks becomes the input to the following one, which constitutes an NLP pipeline. Many third-party NLP tools are currently available, each performing distinct NLP subtasks. However, it is difficult to integrate several NLP toolkits into a pipeline due to many problems, including different input/output representations or formats, distinct programming languages, and tokenization issues. This paper presents DeepNLPF, a framework that enables easy integration of third-party NLP tools, allowing the user to preprocess natural language texts at lexical, syntactic, and semantic levels. The proposed framework also provides an API for complete pipeline customization including the definition of input/output formats, integration plugin management, transparent ultiprocessing execution strategies, corpus-level statistics, and database persistence. Furthermore, the DeepNLPF user-friendly GUI allows its use even by a non-expert NLP user. We conducted runtime performance analysis showing that DeepNLPF not only easily integrates existent NLP toolkits but also reduces significant runtime processing compared to executing the same NLP pipeline in a sequential manner.
Deep Convolutional Networks for Supervised Morpheme Segmentation of Russian Language
Authors: Sorokin A., Kravtsova A.
Date: 2018-09-27 00:00:00
Proceeding: Artificial Intelligence and Natural Language. AINL 2018. Communications in Computer and Information Science, vol 930. Springer, Cham 2018 9
Tasks: Segmentation

The present paper addresses the task of morphological segmentation for Russian language. We show that deep convolutional neural networks solve this problem with F1-score of 98% over morpheme boundaries and beat existing non-neural approaches.
Adaptive-Halting Policy Network for Early Classification
Authors: Thomas Hartvigsen, Cansu Sen, Xiangnan Kong, Elke Rundensteiner
Date: 2019-08-04 00:00:00
Proceeding: KDD 2019 8
Tasks: Classification, Early  Classification, General Classification, Time Series, Time Series Analysis

Early classification of time series is the prediction of the class label of a time series before it is observed in its entirety. In time-sensitive domains where information is collected over time it is worth sacrificing some classification accuracy in favor of earlier predictions,ideally early enough for actions to be taken. However, since ac-curacy and earliness are contradictory objectives, a solution must address this challenge to discover task-dependent trade-offs. We de-sign an early classification model, called EARLIEST, which tackles this multi-objective optimization problem, jointly learning (1) to classify time series and (2) at which timestep to halt and generate this prediction. By learning the objectives together, we achieve a user-controlled balance between these contradictory goals while capturing their natural relationship. Our model consists of the novel pairing of a recurrent discriminator network with a stochastic policy network, with the latter learning a halting-policy as a reinforcement learning task. The learned policy interprets representations generated by the recurrent model and controls its dynamics,sequentially deciding whether or not to request observations from future timesteps. For a rich variety of datasets (four synthetic and three real-world), we demonstrate that EARLIEST consistently out-performs state-of-the-art alternatives in accuracy and earliness while discovering signal locations without supervision.