pwc-archive/papers-with-abstracts huggingface dataset:
A Tangled Web: The Faint Signals of Deception in Text - Boulder Lies and Truth Corpus (BLT-C)
Authors: Franco Salvetti, John B. Lowe, James H. Martin
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Deception Detection

We present an approach to creating corpora for use in detecting deception in text, including a discussion of the challenges peculiar to this task. Our approach is based on soliciting several types of reviews from writers and was implemented using Amazon Mechanical Turk. We describe the multi-dimensional corpus of reviews built using this approach, available free of charge from LDC as the Boulder Lies and Truth Corpus (BLT-C). Challenges for both corpus creation and the deception detection include the fact that human performance on the task is typically at chance, that the signal is faint, that paid writers such as turkers are sometimes deceptive, and that deception is a complex human behavior; manifestations of deception depend on details of domain, intrinsic properties of the deceiver (such as education, linguistic competence, and the nature of the intention), and specifics of the deceptive act (e.g., lying vs. fabricating.) To overcome the inherent lack of ground truth, we have developed a set of semi-automatic techniques to ensure corpus validity. We present some preliminary results on the task of deception detection which suggest that the BLT-C is an improvement in the quality of resources available for this task.
Finding Alternative Translations in a Large Corpus of Movie Subtitle
Authors: J{\"o}rg Tiedemann
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Machine Translation, Sentence, Translation

OpenSubtitles.org provides a large collection of user contributed subtitles in various languages for movies and TV programs. Subtitle translations are valuable resources for cross-lingual studies and machine translation research. A less explored feature of the collection is the inclusion of alternative translations, which can be very useful for training paraphrase systems or collecting multi-reference test suites for machine translation. However, differences in translation may also be due to misspellings, incomplete or corrupt data files, or wrongly aligned subtitles. This paper reports our efforts in recognising and classifying alternative subtitle translations with language independent techniques. We use time-based alignment with lexical re-synchronisation techniques and BLEU score filters and sort alternative translations into categories using edit distance metrics and heuristic rules. Our approach produces large numbers of sentence-aligned translation alternatives for over 50 languages provided via the OPUS corpus collection.
Exploiting a Large Strongly Comparable Corpus
Authors: Thierry Etchegoyhen, Andoni Azpeitia, Naiara P{\'e}rez
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Machine Translation, Translation

This article describes a large comparable corpus for Basque and Spanish and the methods employed to build a parallel resource from the original data. The EITB corpus, a strongly comparable corpus in the news domain, is to be shared with the research community, as an aid for the development and testing of methods in comparable corpora exploitation, and as basis for the improvement of data-driven machine translation systems for this language pair. Competing approaches were explored for the alignment of comparable segments in the corpus, resulting in the design of a simple method which outperformed a state-of-the-art method on the corpus test sets. The method we present is highly portable, computationally efficient, and significantly reduces deployment work, a welcome result for the exploitation of comparable corpora.
The United Nations Parallel Corpus v1.0
Authors: Micha{\l} Ziemski, Marcin Junczys-Dowmunt, Bruno Pouliquen
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Translation

This paper describes the creation process and statistics of the official United Nations Parallel Corpus, the first parallel corpus composed from United Nations documents published by the original data creator. The parallel corpus presented consists of manually translated UN documents from the last 25 years (1990 to 2014) for the six official UN languages, Arabic, Chinese, English, French, Russian, and Spanish. The corpus is freely available for download under a liberal license. Apart from the pairwise aligned documents, a fully aligned subcorpus for the six official UN languages is distributed. We provide baseline BLEU scores of our Moses-based SMT systems trained with the full data of language pairs involving English and for all possible translation directions of the six-way subcorpus.
WAGS: A Beautiful English-Italian Benchmark Supporting Word Alignment Evaluation on Rare Words
Authors: Luisa Bentivogli, Mauro Cettolo, M. Amin Farajian, Marcello Federico
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Sentence, Word Alignment

This paper presents WAGS (Word Alignment Gold Standard), a novel benchmark which allows extensive evaluation of WA tools on out-of-vocabulary (OOV) and rare words. WAGS is a subset of the Common Test section of the Europarl English-Italian parallel corpus, and is specifically tailored to OOV and rare words. WAGS is composed of 6,715 sentence pairs containing 11,958 occurrences of OOV and rare words up to frequency 15 in the Europarl Training set (5,080 English words and 6,878 Italian words), representing almost 3{\%} of the whole text. Since WAGS is focused on OOV/rare words, manual alignments are provided for these words only, and not for the whole sentences. Two off-the-shelf word aligners have been evaluated on WAGS, and results have been compared to those obtained on an existing benchmark tailored to full text alignment. The results obtained confirm that WAGS is a valuable resource, which allows a statistically sound evaluation of WA systems{'} performance on OOV and rare words, as well as extensive data analyses. WAGS is publicly released under a Creative Commons Attribution license.
Manual and Automatic Paraphrases for MT Evaluation
Authors: Ale{\v{s}} Tamchyna, Petra Baran{\v{c}}{\'\i}kov{\'a}
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Machine Translation, Translation

Paraphrasing of reference translations has been shown to improve the correlation with human judgements in automatic evaluation of machine translation (MT) outputs. In this work, we present a new dataset for evaluating English-Czech translation based on automatic paraphrases. We compare this dataset with an existing set of manually created paraphrases and find that even automatic paraphrases can improve MT evaluation. We have also propose and evaluate several criteria for selecting suitable reference translations from a larger set.
Poly-GrETEL: Cross-Lingual Example-based Querying of Syntactic Constructions
Authors: Liesbeth Augustinus, V, Vincent eghinste, Tom Vanallemeersch
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Sentence, Translation

We present Poly-GrETEL, an online tool which enables syntactic querying in parallel treebanks, based on the monolingual GrETEL environment. We provide online access to the Europarl parallel treebank for Dutch and English, allowing users to query the treebank using either an XPath expression or an example sentence in order to look for similar constructions. We provide automatic alignments between the nodes. By combining example-based query functionality with node alignments, we limit the need for users to be familiar with the query language and the structure of the trees in the source and target language, thus facilitating the use of parallel corpora for comparative linguistics and translation studies.
NorGramBank: A `Deep' Treebank for Norwegian
Authors: Helge Dyvik, Paul Meurer, Victoria Ros{\'e}n, Koenraad De Smedt, Petter Haugereid, Gyri Sm{\o}rdal Losnegaard, Gunn Inger Lyse, Martha Thunes
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

We present NorGramBank, a treebank for Norwegian with highly detailed LFG analyses. It is one of many treebanks made available through the INESS treebanking infrastructure. NorGramBank was constructed as a parsebank, i.e. by automatically parsing a corpus, using the wide coverage grammar NorGram. One part consisting of 350,000 words has been manually disambiguated using computer-generated discriminants. A larger part of 50 M words has been stochastically disambiguated. The treebank is dynamic: by global reparsing at certain intervals it is kept compatible with the latest versions of the grammar and the lexicon, which are continually further developed in interaction with the annotators. A powerful query language, INESS Search, has been developed for search across formalisms in the INESS treebanks, including LFG c- and f-structures. Evaluation shows that the grammar provides about 85{\%} of randomly selected sentences with good analyses. Agreement among the annotators responsible for manual disambiguation is satisfactory, but also suggests desirable simplifications of the grammar.
Accurate Deep Syntactic Parsing of Graphs: The Case of French
Authors: Corentin Ribeyre, Eric Villemonte de La Clergerie, Djam{\'e} Seddah
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

Parsing predicate-argument structures in a deep syntax framework requires graphs to be predicted. Argument structures represent a higher level of abstraction than the syntactic ones and are thus more difficult to predict even for highly accurate parsing models on surfacic syntax. In this paper we investigate deep syntax parsing, using a French data set (Ribeyre et al., 2014a). We demonstrate that the use of topologically different types of syntactic features, such as dependencies, tree fragments, spines or syntactic paths, brings a much needed context to the parser. Our higher-order parsing model, gaining thus up to 4 points, establishes the state of the art for parsing French deep syntactic structures.
Explicit Fine grained Syntactic and Semantic Annotation of the Idafa Construction in Arabic
Authors: Abdelati Hawwari, Mohammed Attia, Mahmoud Ghoneim, Mona Diab
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: General Classification

Idafa in traditional Arabic grammar is an umbrella construction that covers several phenomena including what is expressed in English as noun-noun compounds and Saxon and Norman genitives. Additionally, Idafa participates in some other constructions, such as quantifiers, quasi-prepositions, and adjectives. Identifying the various types of the Idafa construction (IC) is of importance to Natural Language processing (NLP) applications. Noun-Noun compounds exhibit special behavior in most languages impacting their semantic interpretation. Hence distinguishing them could have an impact on downstream NLP applications. The most comprehensive syntactic representation of the Arabic language is the LDC Arabic Treebank (ATB). In the ATB, ICs are not explicitly labeled and furthermore, there is no distinction between ICs of noun-noun relations and other traditional ICs. Hence, we devise a detailed syntactic and semantic typification process of the IC phenomenon in Arabic. We target the ATB as a platform for this classification. We render the ATB annotated with explicit IC labels but with the further semantic characterization which is useful for syntactic, semantic and cross language processing. Our typification of IC comprises 3 main syntactic IC types: FIC, GIC, and TIC, and they are further divided into 10 syntactic subclasses. The TIC group is further classified into semantic relations. We devise a method for automatic IC labeling and compare its yield against the CATiB treebank. Our evaluation shows that we achieve the same level of accuracy, but with the additional fine-grained classification into the various syntactic and semantic types.
Compasses, Magnets, Water Microscopes: Annotation of Terminology in a Diachronic Corpus of Scientific Texts
Authors: Anne-Kathrin Schumann, Stefan Fischer
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

The specialised lexicon belongs to the most prominent attributes of specialised writing: Terms function as semantically dense encodings of specialised concepts, which, in the absence of terms, would require lengthy explanations and descriptions. In this paper, we argue that terms are the result of diachronic processes on both the semantic and the morpho-syntactic level. Very little is known about these processes. We therefore present a corpus annotation project aiming at revealing how terms are coined and how they evolve to fit their function as semantically and morpho-syntactically dense encodings of specialised knowledge. The scope of this paper is two-fold: Firstly, we outline our methodology for annotating terminology in a diachronic corpus of scientific publications. Moreover, we provide a detailed analysis of our annotation results and suggest methods for improving the accuracy of annotations in a setting as difficult as ours. Secondly, we present results of a pilot study based on the annotated terms. The results suggest that terms in older texts are linguistically relatively simple units that are hard to distinguish from the lexicon of general language. We believe that this supports our hypothesis that terminology undergoes diachronic processes of densification and specialisation.
KorAP Architecture â€• Diving in the Deep Sea of Corpus Data
Authors: Nils Diewald, Michael Hanl, Eliza Margaretha, Joachim Bingel, Marc Kupietz, Piotr Ba{\'n}ski, Andreas Witt
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Management

KorAP is a corpus search and analysis platform, developed at the Institute for the German Language (IDS). It supports very large corpora with multiple annotation layers, multiple query languages, and complex licensing scenarios. KorAP{'}s design aims to be scalable, flexible, and sustainable to serve the German Reference Corpus DeReKo for at least the next decade. To meet these requirements, we have adopted a highly modular microservice-based architecture. This paper outlines our approach: An architecture consisting of small components that are easy to extend, replace, and maintain. The components include a search backend, a user and corpus license management system, and a web-based user frontend. We also describe a general corpus query protocol used by all microservices for internal communications. KorAP is open source, licensed under BSD-2, and available on GitHub.
Text Segmentation of Digitized Clinical Texts
Authors: Cyril Grouin
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Segmentation, Text Segmentation

In this paper, we present the experiments we made to recover the original page layout structure into two columns from layout damaged digitized files. We designed several CRF-based approaches, either to identify column separator or to classify each token from each line into left or right columns. We achieved our best results with a model trained on homogeneous corpora (only files composed of 2 columns) when classifying each token into left or right columns (overall F-measure of 0.968). Our experiments show it is possible to recover the original layout in columns of digitized documents with results of quality.
A Turkish Database for Psycholinguistic Studies Based on Frequency, Age of Acquisition, and Imageability
Authors: Elif Ahsen Acar, Deniz Zeyrek, Murathan Kurfal{\i}, Cem Boz{\c{s}}ahin
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This study primarily aims to build a Turkish psycholinguistic database including three variables: word frequency, age of acquisition (AoA), and imageability, where AoA and imageability information are limited to nouns. We used a corpus-based approach to obtain information about the AoA variable. We built two corpora: a child literature corpus (CLC) including 535 books written for 3-12 years old children, and a corpus of transcribed children{'}s speech (CSC) at ages 1;4-4;8. A comparison between the word frequencies of CLC and CSC gave positive correlation results, suggesting the usability of the CLC to extract AoA information. We assumed that frequent words of the CLC would correspond to early acquired words whereas frequent words of a corpus of adult language would correspond to late acquired words. To validate AoA results from our corpus-based approach, a rated AoA questionnaire was conducted on adults. Imageability values were collected via a different questionnaire conducted on adults. We conclude that it is possible to deduce AoA information for high frequency words with the corpus-based approach. The results about low frequency words were inconclusive, which is attributed to the fact that corpus-based AoA information is affected by the strong negative correlation between corpus frequency and rated AoA.
Domain-Specific Corpus Expansion with Focused Webcrawling
Authors: Steffen Remus, Chris Biemann
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This work presents a straightforward method for extending or creating in-domain web corpora by focused webcrawling. The focused webcrawler uses statistical N-gram language models to estimate the relatedness of documents and weblinks and needs as input only N-grams or plain texts of a predefined domain and seed URLs as starting points. Two experiments demonstrate that our focused crawler is able to stay focused in domain and language. The first experiment shows that the crawler stays in a focused domain, the second experiment demonstrates that language models trained on focused crawls obtain better perplexity scores on in-domain corpora. We distribute the focused crawler as open source software.
Corpus-Based Diacritic Restoration for South Slavic Languages
Authors: Nikola Ljube{\v{s}}i{\'c}, Toma{\v{z}} Erjavec, Darja Fi{\v{s}}er
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

In computer-mediated communication, Latin-based scripts users often omit diacritics when writing. Such text is typically easily understandable to humans but very difficult for computational processing because many words become ambiguous or unknown. Letter-level approaches to diacritic restoration generalise better and do not require a lot of training data but word-level approaches tend to yield better results. However, they typically rely on a lexicon which is an expensive resource, not covering non-standard forms, and often not available for less-resourced languages. In this paper we present diacritic restoration models that are trained on easy-to-acquire corpora. We test three different types of corpora (Wikipedia, general web, Twitter) for three South Slavic languages (Croatian, Serbian and Slovene) and evaluate them on two types of text: standard (Wikipedia) and non-standard (Twitter). The proposed approach considerably outperforms charlifter, so far the only open source tool available for this task. We make the best performing systems freely available.
Automatic Recognition of Linguistic Replacements in Text Series Generated from Keystroke Logs
Authors: Daniel Couto-Vale, Stella Neumann, Paula Niemietz
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper introduces a toolkit used for the purpose of detecting replacements of different grammatical and semantic structures in ongoing text production logged as a chronological series of computer interaction events (so-called keystroke logs). The specific case we use involves human translations where replacements can be indicative of translator behaviour that leads to specific features of translations that distinguish them from non-translated texts. The toolkit uses a novel CCG chart parser customised so as to recognise grammatical words independently of space and punctuation boundaries. On the basis of the linguistic analysis, structures in different versions of the target text are compared and classified as potential equivalents of the same source text segment by {`}equivalence judges{'}. In that way, replacements of grammatical and semantic structures can be detected. Beyond the specific task at hand the approach will also be useful for the analysis of other types of spaceless text such as Twitter hashtags and texts in agglutinative or spaceless languages like Finnish or Chinese.
Automatic Corpus Extension for Data-driven Natural Language Generation
Authors: Elena Manishina, Bassam Jabaian, St{\'e}phane Huet, Fabrice Lef{\`e}vre
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Text Generation

As data-driven approaches started to make their way into the Natural Language Generation (NLG) domain, the need for automation of corpus building and extension became apparent. Corpus creation and extension in data-driven NLG domain traditionally involved manual paraphrasing performed by either a group of experts or with resort to crowd-sourcing. Building the training corpora manually is a costly enterprise which requires a lot of time and human resources. We propose to automate the process of corpus extension by integrating automatically obtained synonyms and paraphrases. Our methodology allowed us to significantly increase the size of the training corpus and its level of variability (the number of distinct tokens and specific syntactic structures). Our extension solutions are fully automatic and require only some initial validation. The human evaluation results confirm that in many cases native speakers favor the outputs of the model built on the extended corpus.
Bilbo-Val: Automatic Identification of Bibliographical Zone in Papers
Authors: Amal Htait, Sebastien Fournier, Patrice Bellot
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Articles

In this paper, we present the automatic annotation of bibliographical references{'} zone in papers and articles of XML/TEI format. Our work is applied through two phases: first, we use machine learning technology to classify bibliographical and non-bibliographical paragraphs in papers, by means of a model that was initially created to differentiate between the footnotes containing or not containing bibliographical references. The previous description is one of BILBO{'}s features, which is an open source software for automatic annotation of bibliographic reference. Also, we suggest some methods to minimize the margin of error. Second, we propose an algorithm to find the largest list of bibliographical references in the article. The improvement applied on our model results an increase in the model{'}s efficiency with an Accuracy equal to 85.89. And by testing our work, we are able to achieve 72.23{\%} as an average for the percentage of success in detecting bibliographical references{'} zone.
Guidelines and Framework for a Large Scale Arabic Diacritized Corpus
Authors: Wajdi Zaghouani, Houda Bouamor, Abdelati Hawwari, Mona Diab, Ossama Obeid, Mahmoud Ghoneim, Sawsan Alqahtani, Kemal Oflazer
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper presents the annotation guidelines developed as part of an effort to create a large scale manually diacritized corpus for various Arabic text genres. The target size of the annotated corpus is 2 million words. We summarize the guidelines and describe issues encountered during the training of the annotators. We also discuss the challenges posed by the complexity of the Arabic language and how they are addressed. Finally, we present the diacritization annotation procedure and detail the quality of the resulting annotations.