pwc-archive/papers-with-abstracts huggingface dataset:
Multi-prototype Chinese Character Embedding
Authors: Yanan Lu, Yue Zhang, Donghong Ji
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: NER, Position

Chinese sentences are written as sequences of characters, which are elementary units of syntax and semantics. Characters are highly polysemous in forming words. We present a position-sensitive skip-gram model to learn multi-prototype Chinese character embeddings, and explore the usefulness of such character embeddings to Chinese NLP tasks. Evaluation on character similarity shows that multi-prototype embeddings are significantly better than a single-prototype baseline. In addition, used as features in the Chinese NER task, the embeddings result in a 1.74{\%} F-score improvement over a state-of-the-art baseline.
A comparison of Named-Entity Disambiguation and Word Sense Disambiguation
Authors: Angel Chang, Valentin I. Spitkovsky, Christopher D. Manning, Eneko Agirre
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Entity Disambiguation, Word Sense Disambiguation

Named Entity Disambiguation (NED) is the task of linking a named-entity mention to an instance in a knowledge-base, typically Wikipedia-derived resources like DBpedia. This task is closely related to word-sense disambiguation (WSD), where the mention of an open-class word is linked to a concept in a knowledge-base, typically WordNet. This paper analyzes the relation between two annotated datasets on NED and WSD, highlighting the commonalities and differences. We detail the methods to construct a NED system following the WSD word-expert approach, where we need a dictionary and one classifier is built for each target entity mention string. Constructing a dictionary for NED proved challenging, and although similarity and ambiguity are higher for NED, the results are also higher due to the larger number of training data, and the more crisp and skewed meaning differences.
Leveraging RDF Graphs for Crossing Multiple Bilingual Dictionaries
Authors: Marta Villegas, Maite Melero, N{\'u}ria Bel, Jorge Gracia
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Translation

The experiments presented here exploit the properties of the Apertium RDF Graph, principally cycle density and nodes{'} degree, to automatically generate new translation relations between words, and therefore to enrich existing bilingual dictionaries with new entries. Currently, the Apertium RDF Graph includes data from 22 Apertium bilingual dictionaries and constitutes a large unified array of linked lexical entries and translations that are available and accessible on the Web (http://linguistic.linkeddata.es/apertium/). In particular, its graph structure allows for interesting exploitation opportunities, some of which are addressed in this paper. Two {`}massive{'} experiments are reported: in the first one, the original EN-ES translation set was removed from the Apertium RDF Graph and a new EN-ES version was generated. The results were compared against the previously removed EN-ES data and against the Concise Oxford Spanish Dictionary. In the second experiment, a new non-existent EN-FR translation set was generated. In this case the results were compared against a converted wiktionary English-French file. The results we got are really good and perform well for the extreme case of correlated polysemy. This lead us to address the possibility to use cycles and nodes degree to identify potential oddities in the source data. If cycle density proves efficient when considering potential targets, we can assume that in dense graphs nodes with low degree may indicate potential errors.
PreMOn: a Lemon Extension for Exposing Predicate Models as Linked Data
Authors: Francesco Corcoglioniti, Marco Rospocher, Alessio Palmero Aprosio, Sara Tonelli
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

We introduce PreMOn (predicate model for ontologies), a linguistic resource for exposing predicate models (PropBank, NomBank, VerbNet, and FrameNet) and mappings between them (e.g, SemLink) as Linked Open Data. It consists of two components: (i) the PreMOn Ontology, an extension of the lemon model by the W3C Ontology-Lexica Community Group, that enables to homogeneously represent data from the various predicate models; and, (ii) the PreMOn Dataset, a collection of RDF datasets integrating various versions of the aforementioned predicate models and mapping resources. PreMOn is freely available and accessible online in different ways, including through a dedicated SPARQL endpoint.
Semantic Links for Portuguese
Authors: Fabricio Chalub, Livy Real, Alex Rademaker, re, Valeria de Paiva
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper describes work on incorporating Princenton{'}s WordNet morphosemantics links to the fabric of the Portuguese OpenWordNet-PT. Morphosemantic links are relations between verbs and derivationally related nouns that are semantically typed (such as for tune-tuner â€• in Portuguese {``}afinar-afinador{''} {--} linked through an {``}agent{''} link). Morphosemantic links have been discussed for Princeton{'}s WordNet for a while, but have not been added to the official database. These links are very useful, they help us to improve our Portuguese WordNet. Thus we discuss the integration of these links in our base and the issues we encountered with the integration.
Creating Linked Data Morphological Language Resources with MMoOn - The Hebrew Morpheme Inventory
Authors: Bettina Klimek, Natanael Arndt, Sebastian Krause, Timotheus Arndt
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

The development of standard models for describing general lexical resources has led to the emergence of numerous lexical datasets of various languages in the Semantic Web. However, equivalent models covering the linguistic domain of morphology do not exist. As a result, there are hardly any language resources of morphemic data available in RDF to date. This paper presents the creation of the Hebrew Morpheme Inventory from a manually compiled tabular dataset comprising around 52.000 entries. It is an ongoing effort of representing the lexemes, word-forms and morphologigal patterns together with their underlying relations based on the newly created Multilingual Morpheme Ontology (MMoOn). It will be shown how segmented Hebrew language data can be granularly described in a Linked Data format, thus, serving as an exemplary case for creating morpheme inventories of any inflectional language with MMoOn. The resulting dataset is described a) according to the structure of the underlying data format, b) with respect to the Hebrew language characteristic of building word-forms directly from roots, c) by exemplifying how inflectional information is realized and d) with regard to its enrichment with external links to sense resources.
Parallel Global Voices: a Collection of Multilingual Corpora with Citizen Media Stories
Authors: Prokopis Prokopidis, Vassilis Papavassiliou, Stelios Piperidis
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

We present a new collection of multilingual corpora automatically created from the content available in the Global Voices websites, where volunteers have been posting and translating citizen media stories since 2004. We describe how we crawled and processed this content to generate parallel resources comprising 302.6K document pairs and 8.36M segment alignments in 756 language pairs. For some language pairs, the segment alignments in this resource are the first open examples of their kind. In an initial use of this resource, we discuss how a set of document pair detection algorithms performs on the Greek-English corpus.
Large Multi-lingual, Multi-level and Multi-genre Annotation Corpus
Authors: Xuansong Li, Martha Palmer, Nianwen Xue, Lance Ramshaw, Mohamed Maamouri, Ann Bies, Kathryn Conger, Stephen Grimes, Stephanie Strassel
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Information Retrieval, Retrieval, Translation, Word Alignment

High accuracy for automated translation and information retrieval calls for linguistic annotations at various language levels. The plethora of informal internet content sparked the demand for porting state-of-art natural language processing (NLP) applications to new social media as well as diverse language adaptation. Effort launched by the BOLT (Broad Operational Language Translation) program at DARPA (Defense Advanced Research Projects Agency) successfully addressed the internet information with enhanced NLP systems. BOLT aims for automated translation and linguistic analysis for informal genres of text and speech in online and in-person communication. As a part of this program, the Linguistic Data Consortium (LDC) developed valuable linguistic resources in support of the training and evaluation of such new technologies. This paper focuses on methodologies, infrastructure, and procedure for developing linguistic annotation at various language levels, including Treebank (TB), word alignment (WA), PropBank (PB), and co-reference (CoRef). Inspired by the OntoNotes approach with adaptations to the tasks to reflect the goals and scope of the BOLT project, this effort has introduced more annotation types of informal and free-style genres in English, Chinese and Egyptian Arabic. The corpus produced is by far the largest multi-lingual, multi-level and multi-genre annotation corpus of informal text and speech.
C4Corpus: Multilingual Web-size Corpus with Free License
Authors: Ivan Habernal, Omnia Zayed, Iryna Gurevych
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: CPU

Large Web corpora containing full documents with permissive licenses are crucial for many NLP tasks. In this article we present the construction of 12 million-pages Web corpus (over 10 billion tokens) licensed under CreativeCommons license family in 50+ languages that has been extracted from CommonCrawl, the largest publicly available general Web crawl to date with about 2 billion crawled URLs. Our highly-scalable Hadoop-based framework is able to process the full CommonCrawl corpus on 2000+ CPU cluster on the Amazon Elastic Map/Reduce infrastructure. The processing pipeline includes license identification, state-of-the-art boilerplate removal, exact duplicate and near-duplicate document removal, and language detection. The construction of the corpus is highly configurable and fully reproducible, and we provide both the framework (DKPro C4CorpusTools) and the resulting data (C4Corpus) to the research community.
OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles
Authors: Pierre Lison, J{\"o}rg Tiedemann
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Optical Character Recognition (OCR)

We present a new major release of the OpenSubtitles collection of parallel corpora. The release is compiled from a large database of movie and TV subtitles and includes a total of 1689 bitexts spanning 2.6 billion sentences across 60 languages. The release also incorporates a number of enhancements in the preprocessing and alignment of the subtitles, such as the automatic correction of OCR errors and the use of meta-data to estimate the quality of each subtitle and score subtitle pairs.
LexFr: Adapting the LexIt Framework to Build a Corpus-based French Subcategorization Lexicon
Authors: Giulia Rambelli, Gianluca Lebani, Laurent Pr{\'e}vot, Aless Lenci, ro
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper introduces LexFr, a corpus-based French lexical resource built by adapting the framework LexIt, originally developed to describe the combinatorial potential of Italian predicates. As in the original framework, the behavior of a group of target predicates is characterized by a series of syntactic (i.e., subcategorization frames) and semantic (i.e., selectional preferences) statistical information (a.k.a. distributional profiles) whose extraction process is mostly unsupervised. The first release of LexFr includes information for 2,493 verbs, 7,939 nouns and 2,628 adjectives. In these pages we describe the adaptation process and evaluated the final resource by comparing the information collected for 20 test verbs against the information available in a gold standard dictionary. In the best performing setting, we obtained 0.74 precision, 0.66 recall and 0.70 F-measure.
Polarity Lexicon Building: to what Extent Is the Manual Effort Worth?
Authors: I{\~n}aki San Vicente, Xabier Saralegi
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

Polarity lexicons are a basic resource for analyzing the sentiments and opinions expressed in texts in an automated way. This paper explores three methods to construct polarity lexicons: translating existing lexicons from other languages, extracting polarity lexicons from corpora, and annotating sentiments Lexical Knowledge Bases. Each of these methods require a different degree of human effort. We evaluate how much manual effort is needed and to what extent that effort pays in terms of performance improvement. Experiment setup includes generating lexicons for Basque, and evaluating them against gold standard datasets in different domains. Results show that extracting polarity lexicons from corpora is the best solution for achieving a good performance with reasonable human effort.
Al Qamus al Muhit, a Medieval Arabic Lexicon in LMF
Authors: Ouafae Nahli, Francesca Frontini, Monica Monachini, Fahad Khan, Arsalan Zarghili, Mustapha Khalfi
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper describes the conversion into LMF, a standard lexicographic digital format of {`}al-q{\=a}m{\=u}s al-muḥ{\=\i}ṭ, a Medieval Arabic lexicon. The lexicon is first described, then all the steps required for the conversion are illustrated. The work is will produce a useful lexicographic resource for Arabic NLP, but is also interesting per se, to study the implications of adapting the LMF model to the Arabic language. Some reflections are offered as to the status of roots with respect to previously suggested representations. In particular, roots are, in our opinion are to be not treated as lexical entries, but modeled as lexical metadata for classifying and identifying lexical entries. In this manner, each root connects all entries that are derived from it.
CASSAurus: A Resource of Simpler Spanish Synonyms
Authors: Ricardo Baeza-Yates, Luz Rello, Julia Dembowski
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Lexical Simplification

In this work we introduce and describe a language resource composed of lists of simpler synonyms for Spanish. The synonyms are divided in different senses taken from the Spanish OpenThesaurus, where context disambiguation was performed by using statistical information from the Web and Google Books Ngrams. This resource is freely available online and can be used for different NLP tasks such as lexical simplification. Indeed, so far it has been already integrated into four tools.
Measuring Lexical Quality of a Historical Finnish Newspaper Collection â€• Analysis of Garbled OCR Data with Basic Language Technology Tools and Means
Authors: Kimmo Kettunen, Tuula P{\"a}{\"a}kk{\"o}nen
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Information Retrieval, Optical Character Recognition (OCR), Retrieval

The National Library of Finland has digitized a large proportion of the historical newspapers published in Finland between 1771 and 1910 (Bremer-Laamanen 2001). This collection contains approximately 1.95 million pages in Finnish and Swedish. Finnish part of the collection consists of about 2.39 billion words. The National Library{'}s Digital Collections are offered via the digi.kansalliskirjasto.fi web service, also known as Digi. Part of this material is also available freely downloadable in The Language Bank of Finland provided by the Fin-CLARIN consortium . The collection can also be accessed through the Korp environment that has been developed by Spr{\"a}kbanken at the University of Gothenburg and extended by FIN-CLARIN team at the University of Helsinki to provide concordances of text resources. A Cranfield-style information retrieval test collection has been produced out of a small part of the Digi newspaper material at the University of Tampere (J{\"a}rvelin et al., 2015). The quality of the OCRed collections is an important topic in digital humanities, as it affects general usability and searchability of collections. There is no single available method to assess the quality of large collections, but different methods can be used to approximate the quality. This paper discusses different corpus analysis style ways to approximate the overall lexical quality of the Finnish part of the Digi collection.
Using SMT for OCR Error Correction of Historical Texts
Authors: Haithem Afli, Zhengwei Qiu, Andy Way, P{\'a}raic Sheridan
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Articles, Language Modelling, Machine Translation, Optical Character Recognition, Optical Character Recognition (OCR), Translation

A trend to digitize historical paper-based archives has emerged in recent years, with the advent of digital optical scanners. A lot of paper-based books, textbooks, magazines, articles, and documents are being transformed into electronic versions that can be manipulated by a computer. For this purpose, Optical Character Recognition (OCR) systems have been developed to transform scanned digital text into editable computer text. However, different kinds of errors in the OCR system output text can be found, but Automatic Error Correction tools can help in performing the quality of electronic texts by cleaning and removing noises. In this paper, we perform a qualitative and quantitative comparison of several error-correction techniques for historical French documents. Experimentation shows that our Machine Translation for Error Correction method is superior to other Language Modelling correction techniques, with nearly 13{\%} relative improvement compared to the initial baseline.
OCR Post-Correction Evaluation of Early Dutch Books Online - Revisited
Authors: Martin Reynaert
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Optical Character Recognition (OCR)

We present further work on evaluation of the fully automatic post-correction of Early Dutch Books Online, a collection of 10,333 18th century books. In prior work we evaluated the new implementation of Text-Induced Corpus Clean-up (TICCL) on the basis of a single book Gold Standard derived from this collection. In the current paper we revisit the same collection on the basis of a sizeable 1020 item random sample of OCR post-corrected strings from the full collection. Both evaluations have their own stories to tell and lessons to teach.
Crowdsourcing an OCR Gold Standard for a German and French Heritage Corpus
Authors: Simon Clematide, Lenz Furrer, Martin Volk
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Optical Character Recognition, Optical Character Recognition (OCR)

Crowdsourcing approaches for post-correction of OCR output (Optical Character Recognition) have been successfully applied to several historic text collections. We report on our crowd-correction platform Kokos, which we built to improve the OCR quality of the digitized yearbooks of the Swiss Alpine Club (SAC) from the 19th century. This multilingual heritage corpus consists of Alpine texts mainly written in German and French, all typeset in Antiqua font. Finding and engaging volunteers for correcting large amounts of pages into high quality text requires a carefully designed user interface, an easy-to-use workflow, and continuous efforts for keeping the participants motivated. More than 180,000 characters on about 21,000 pages were corrected by volunteers in about 7 month, achieving an OCR gold standard with a systematically evaluated accuracy of 99.7{\%} on the word level. The crowdsourced OCR gold standard and the corresponding original OCR recognition results from Abby FineReader 7 for each page are available as a resource. Additionally, the scanned images (300dpi) of all pages are included in order to facilitate tests with other OCR software.
Argument Mining: the Bottleneck of Knowledge and Language Resources
Authors: Patrick Saint-Dizier
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Argument Mining

Given a controversial issue, argument mining from natural language texts (news papers, and any form of text on the Internet) is extremely challenging: domain knowledge is often required together with appropriate forms of inferences to identify arguments. This contribution explores the types of knowledge that are required and how they can be paired with reasoning schemes, language processing and language resources to accurately mine arguments. We show via corpus analysis that the Generative Lexicon, enhanced in different manners and viewed as both a lexicon and a domain knowledge representation, is a relevant approach. In this paper, corpus annotation for argument mining is first developed, then we show how the generative lexicon approach must be adapted and how it can be paired with language processing patterns to extract and specify the nature of arguments. Our approach to argument mining is thus knowledge driven.
From Interoperable Annotations towards Interoperable Resources: A Multilingual Approach to the Analysis of Discourse
Authors: Ekaterina Lapshinova-Koltunski, Kerstin Anna Kunz, Anna Nedoluzhko
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Machine Translation, Semantic Similarity, Semantic Textual Similarity, Translation

In the present paper, we analyse variation of discourse phenomena in two typologically different languages, i.e. in German and Czech. The novelty of our approach lies in the nature of the resources we are using. Advantage is taken of existing resources, which are, however, annotated on the basis of two different frameworks. We use an interoperable scheme unifying discourse phenomena in both frameworks into more abstract categories and considering only those phenomena that have a direct match in German and Czech. The discourse properties we focus on are relations of identity, semantic similarity, ellipsis and discourse relations. Our study shows that the application of interoperable schemes allows an exploitation of discourse-related phenomena analysed in different projects and on the basis of different frameworks. As corpus compilation and annotation is a time-consuming task, positive results of this experiment open up new paths for contrastive linguistics, translation studies and NLP, including machine translation.
Falling silent, lost for words ... Tracing personal involvement in interviews with Dutch war veterans
Authors: Henk van den Heuvel, Nelleke Oostdijk
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

In sources used in oral history research (such as interviews with eye witnesses), passages where the degree of personal emotional involvement is found to be high can be of particular interest, as these may give insight into how historical events were experienced, and what moral dilemmas and psychological or religious struggles were encountered. In a pilot study involving a large corpus of interview recordings with Dutch war veterans, we have investigated if it is possible to develop a method for automatically identifying those passages where the degree of personal emotional involvement is high. The method is based on the automatic detection of exceptionally large silences and filled pause segments (using Automatic Speech Recognition), and cues taken from specific n-grams. The first results appear to be encouraging enough for further elaboration of the method.