The Corpus Encoding Standard (CES) is a part of the EAGLES Guidelines developed by the Expert Advisory Group on Language Engineering Standards (EAGLES) that provides a set of encoding standards for corpus-based work in natural language processing applications. We have instantiated the CES as an XML application called XCES, based on the same data architecture comprised of a primary encoded text and "standoff" annotation in separate documents. Conversion to XML enables use of some of the more powerful mechanisms provided in the XML framework, including the XSLT Transformation Language, XML Schemas, and support for interrescue reference together with an extensive path syntax for pointers. In this paper, we describe the differences between the CES and XCES DTDs and demonstrate how XML mechanisms can be used to select from and manipulate annotated corpora encoded according to XCES specifications. We also provide a general overview of XML and the XML mechanisms that are most relevant to language engineering research and applications.

XCES : An XML-based Encoding Standard for Linguistic Corpora XML Conversion of the CES

We introduce $k$-nearest-neighbor machine translation ($k$NN-MT), which predicts tokens with a nearest neighbor classifier over a large datastore of cached examples, using representations from a neural translation model for similarity search. This approach requires no additional training and scales to give the decoder direct access to billions of examples at test time, resulting in a highly expressive model that consistently improves performance across many settings. Simply adding nearest neighbor search improves a state-of-the-art German-English translation model by 1.5 BLEU. $k$NN-MT allows a single model to be adapted to diverse domains by using a domain-specific datastore, improving results by an average of 9.2 BLEU over zero-shot transfer, and achieving new state-of-the-art results -- without training on these domains. A massively multilingual model can also be specialized for particular language pairs, with improvements of 3 BLEU for translating from English into German and Chinese. Qualitatively, $k$NN-MT is easily interpretable; it combines source and target context to retrieve highly relevant examples.

Nearest Neighbor Machine Translation

Multiword expressions (MWEs) are known as a "pain in the neck" for NLP due to their idiosyncratic behaviour While some categories of MWEs have been addressed by many studies, verbal MWEs (VMWEs), such as to take a decision, to break one's heart or to turn off, have been rarely modelled This is notably due to their syntactic variability, which hinders treating them as " words with spaces " We describe an initiative meant to bring about substantial progress in understanding, modelling and processing VMWEs It is a joint effort, carried out within a European research network, to elaborate universal terminologies and annotation guidelines for 18 languages Its main outcome is a multilingual 5-million-word annotated corpus which underlies a shared task on automatic identification of VMWEs This paper presents the corpus annotation methodology and outcome, the shared task organisation and the results of the participating systems

/pdf/the-parseme-shared-task-on-automatic-identification-of-1rm7lz8rwl.pdf

The PARSEME Shared Task on Automatic Identification of Verbal Multiword Expressions

MOTIVATION: Annotation tools are applied to build training and test corpora, which are essential for the development and evaluation of new natural language processing algorithms. Further, annotation tools are also used to extract new information for a particular use case. However, owing to the high number of existing annotation tools, finding the one that best fits particular needs is a demanding task that requires searching the scientific literature followed by installing and trying various tools. 
METHODS: We searched for annotation tools and selected a subset of them according to five requirements with which they should comply, such as being Web-based or supporting the definition of a schema. We installed the selected tools (when necessary), carried out hands-on experiments and evaluated them using 26 criteria that covered functional and technical aspects. We defined each criterion on three levels of matches and a score for the final evaluation of the tools. 

RESULTS: We evaluated 78 tools and selected the following 15 for a detailed evaluation: BioQRator, brat, Catma, Djangology, ezTag, FLAT, LightTag, MAT, MyMiner, PDFAnno, prodigy, tagtog, TextAE, WAT-SL and WebAnno. Full compliance with our 26 criteria ranged from only 9 up to 20 criteria, which demonstrated that some tools are comprehensive and mature enough to be used on most annotation projects. The highest score of 0.81 was obtained by WebAnno (of a maximum value of 1.0).

/pdf/an-extensive-review-of-tools-for-manual-annotation-of-3xsbqo68ot.pdf

An Extensive Review of Tools for Manual Annotation of Documents

Agata Savary1, Marie Candito2, Verginica BarbuMititelu3, Eduard Bejček4, Fabienne Cap5, Slavomír Čéplö6, Silvio Ricardo Cordeiro7, Gülşen Eryiğit8, Voula Giouli9, Maarten van Gompel10, Yaakov HaCohen-Kerner11, Jolanta Kovalevskaitė12, SimonKrek13, Chaya Liebeskind11, JohannaMonti14, Carla Parra Escartín15, Lonneke van der Plas6, Behrang QasemiZadeh16, Carlos Ramisch7, Federico Sangati17, Ivelina Stoyanova18 & Veronika Vincze19 1Université de Tours (France), 2Université Paris Diderot (France), 3Romanian Academy Research Institute for Artificial Intelligence (Romania), 4Charles University (Czech Republic), 5Uppsala University (Sweden), 6University of Malta (Malta), 7Aix Marseille University (France), 8Istanbul Technical University (Turkey), 9Athena Research Center in Athens (Greece), 10Radboud University in Nijmegen (Netherlands), 11Jerusalem College of Technology (Israel), 12Vytautas Magnus University in Kaunas (Lithuania), 13Jožef Stefan Institute in Ljubljana (Slovenia), 14“L’Orientale” University of Naples (Italy), 15ADAPT Centre, Dublin City University (Ireland), 16University of Düsseldorf (Germany), 17independent researcher (Italy), 18Bulgarian Academy of Sciences in Sofia (Bulgaria), 19Univer-

https://hal.archives-ouvertes.fr/hal-01917174/document

PARSEME multilingual corpus of verbal multiword expressions

In this paper we present FoLiA, a Format for Linguistic Annotation, and conduct a comparative study with other annotation schemes, including the Linguistic Annotation Framework (LAF), the Text Encoding Initiative (TEI) and Text Corpus Format (TCF). An additional point of focus is the interoperability between FoLiA and metadata standards such as the Component MetaData Infrastructure (CMDI), as well as data category registries such as ISOcat. The aim of the paper is to present a clear image of the capabilities of FoLiA and how it relates to other formats. This should open discussion and aid users in their decision for a particular format.
FoLiA is a practically-oriented XML-based annotation format for the representation of language resources, explicitly supporting a wide variety of annotation types. It introduces a flexible and uniform paradigm and a representation independent of language or label set. It is designed to be highly expressive, generic, and formalised, whilst at the same time focussing on being as practical as possible to ease its adoption and implementation. The aspiration is to offer a generic format for storage, exchange, and machine-processing of linguistically annotated documents, preventing users as well as software tools from having to cope with a wide variety of different formats, which in the field regularly causes convertibility issues and proliferation of ad-hoc formats. FoLiA emerged from such a practical need in the context of Computational Linguistics in the Netherlands and Flanders. It has been successfully adopted by numerous projects within this community. FoLiA was developed in a bottom-up fashion, with special emphasis on software libraries and tools to handle it.

/pdf/folia-a-practical-xml-format-for-linguistic-annotation-a-13khsipycf.pdf

FoLiA: A practical XML Format for Linguistic Annotation - a descriptive and comparative study

State of the art Word Sense Disambiguation (WSD) systems require large sense-tagged corpora along with lexical databases to reach satisfactory results. The number of English language resources for developed WSD increased in the past years, while most other languages are still under-resourced. The situation is no different for Dutch. In order to overcome this data bottleneck, the DutchSemCor project will deliver a Dutch corpus that is sense-tagged with senses from the Cornetto lexical database. Part of this
corpus (circa 300K examples) is manually tagged. The remainder is automatically tagged using different WSD systems and validated by human annotators. The project uses existing corpora compiled in other projects; these are extended with Internet
examples for word senses that are less frequent and do not (sufficiently) appear in the corpora. We report on the status of the project and the evaluations of the WSD systems with the current training data.

DutchSemCor: Building a semantically annotated corpus for Dutch

CLARIN activities in the Netherlands in 2015 are in transition between the first national project CLARIN-NL and its successor CLARIAH. In this paper we give an overview of important infrastructure developments which have taken place throughout the first and which are taken to a further level in the second. We show how relatively small accomplishments in particular projects enable larger steps in further ones and how the synergy of these projects helps the national infrastructure to outgrow mere demonstrators and to move towards mature production systems. The paper centers around a new corpus building tool called PICCL. This integrated pipeline offers a comprehensive range of conversion facilities for legacy electronic text formats, Optical Character Recognition for text images, automatic text correction and normalization, linguistic annotation, and preparation for corpus exploration and exploitation environments. We give a concise overview of PICCL’s components, integrated now or to be incorporated in the foreseeable future.

PICCL: Philosophical Integrator of Computational and Corpus Libraries

We present a phrase-based extension to memory-based machine translation. This form of example-based machine translation employs lazy-learning classifiers to translate fragments of the source sentence to fragments of the target sentence. Source-side fragments consist of variable-length phrases in a local context of neighboring words, translated by the classifier to a target-language phrase. We compare three methods of phrase extraction, and present a new decoder that reassembles the translated fragments into one final translation. Results show that one of the proposed phrase-extraction methods—the one used in Moses—leads to a translation system that outperforms context-sensitive word-based approaches. The differences, however, are small, arguably because the word-based approaches already capture phrasal context implicitly due to their source-side and target-side context sensitivity.

/pdf/extending-memory-based-machine-translation-to-phrases-4yr4ywuyh7.pdf

Extending memory-based machine translation to phrases

In this paper we present a statistical machine translation (SMT) system for Frisian to Dutch and Dutch to Frisian. A parallel training corpus has been established, which has subsequently been used to automatically learn a phrase-based SMT model. The translation system is built around the open-source SMT software Moses. The resulting system, named Oersetter, is released as a website for human end users, as well as a web service for software to interact with. We here discuss the workings, setup and performance of our system, which to our knowledge is the very first Frisian-Dutch SMT system.

M. van Gompel

Papers

FoLiA: A practical XML Format for Linguistic Annotation - a descriptive and comparative study

DutchSemCor: Building a semantically annotated corpus for Dutch

PICCL: Philosophical Integrator of Computational and Corpus Libraries

Extending memory-based machine translation to phrases

Oersetter: Frisian-Dutch statistical machine translation