pwc-archive/papers-with-abstracts huggingface dataset:
Collecting Language Resources for the Latvian e-Government Machine Translation Platform
Authors: Roberts Rozis, Andrejs Vasi{\c{l}}jevs, Raivis Skadi{\c{n}}{\v{s}}
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Machine Translation, Miscellaneous, Translation

This paper describes corpora collection activity for building large machine translation systems for Latvian e-Government platform. We describe requirements for corpora, selection and assessment of data sources, collection of the public corpora and creation of new corpora from miscellaneous sources. Methodology, tools and assessment methods are also presented along with the results achieved, challenges faced and conclusions made. Several approaches to address the data scarceness are discussed. We summarize the volume of obtained corpora and provide quality metrics of MT systems trained on this data. Resulting MT systems for English-Latvian, Latvian English and Latvian Russian are integrated in the Latvian e-service portal and are freely available on website HUGO.LV. This paper can serve as a guidance for similar activities initiated in other countries, particularly in the context of European Language Resource Coordination action.
Nederlab: Towards a Single Portal and Research Environment for Diachronic Dutch Text Corpora
Authors: Hennie Brugman, Martin Reynaert, Nicoline van der Sijs, Ren{\'e} van Stipriaan, Erik Tjong Kim Sang, Antal Van den Bosch
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Cultural Vocal Bursts Intensity Prediction

The Nederlab project aims to bring together all digitized texts relevant to the Dutch national heritage, the history of the Dutch language and culture (circa 800 {--} present) in one user friendly and tool enriched open access web interface. This paper describes Nederlab halfway through the project period and discusses the collections incorporated, back-office processes, system back-end as well as the Nederlab Research Portal end-user web application.
A Semi-Supervised Approach for Gender Identification
Authors: Juan Soler, Leo Wanner
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Author Profiling

In most of the research studies on Author Profiling, large quantities of correctly labeled data are used to train the models. However, this does not reflect the reality in forensic scenarios: in practical linguistic forensic investigations, the resources that are available to profile the author of a text are usually scarce. To pay tribute to this fact, we implemented a Semi-Supervised Learning variant of the k nearest neighbors algorithm that uses small sets of labeled data and a larger amount of unlabeled data to classify the authors of texts by gender (man vs woman). We describe the enriched KNN algorithm and show that the use of unlabeled instances improves the accuracy of our gender identification model. We also present a feature set that facilitates the use of a very small number of instances, reaching accuracies higher than 70{\%} with only 113 instances to train the model. It is also shown that the algorithm also performs well using publicly available data.
Ensemble Classification of Grants using LDA-based Features
Authors: Yannis Korkontzelos, Beverley Thomas, Makoto Miwa, Sophia Ananiadou
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Classification, Decision Making, General Classification

Classifying research grants into useful categories is a vital task for a funding body to give structure to the portfolio for analysis, informing strategic planning and decision-making. Automating this classification process would save time and effort, providing the accuracy of the classifications is maintained. We employ five classification models to classify a set of BBSRC-funded research grants in 21 research topics based on unigrams, technical terms and Latent Dirichlet Allocation models. To boost precision, we investigate methods for combining their predictions into five aggregate classifiers. Evaluation confirmed that ensemble classification models lead to higher precision.It was observed that there is not a single best-performing aggregate method for all research topics. Instead, the best-performing method for a research topic depends on the number of positive training instances available for this topic. Subject matter experts considered the predictions of aggregate models to correct erroneous or incomplete manual assignments.
Edit Categories and Editor Role Identification in Wikipedia
Authors: Diyi Yang, Aaron Halfaker, Robert Kraut, Eduard Hovy
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

In this work, we introduced a corpus for categorizing edit types in Wikipedia. This fine-grained taxonomy of edit types enables us to differentiate editing actions and find editor roles in Wikipedia based on their low-level edit types. To do this, we first created an annotated corpus based on 1,996 edits obtained from 953 article revisions and built machine-learning models to automatically identify the edit categories associated with edits. Building on this automated measurement of edit types, we then applied a graphical model analogous to Latent Dirichlet Allocation to uncover the latent roles in editors{'} edit histories. Applying this technique revealed eight different roles editors play, such as Social Networker, Substantive Expert, etc.
Morphologically Annotated Corpora and Morphological Analyzers for Moroccan and Sanaani Yemeni Arabic
Authors: Faisal Al-Shargi, Aidan Kaplan, Esk, Ramy er, Nizar Habash, Owen Rambow
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

We present new language resources for Moroccan and Sanaani Yemeni Arabic. The resources include corpora for each dialect which have been morphologically annotated, and morphological analyzers for each dialect which are derived from these corpora. These are the first sets of resources for Moroccan and Yemeni Arabic. The resources will be made available to the public.
Merging Data Resources for Inflectional and Derivational Morphology in Czech
Authors: Zden{\v{e}}k {\v{Z}}abokrtsk{\'y}, Magda {\v{S}}ev{\v{c}}{\'\i}kov{\'a}, Milan Straka, Jon{\'a}{\v{s}} Vidra, Ad{\'e}la Limbursk{\'a}
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Lemmatization, Morphological Analysis

The paper deals with merging two complementary resources of morphological data previously existing for Czech, namely the inflectional dictionary MorfFlex CZ and the recently developed lexical network DeriNet. The MorfFlex CZ dictionary has been used by a morphological analyzer capable of analyzing/generating several million Czech word forms according to the rules of Czech inflection. The DeriNet network contains several hundred thousand Czech lemmas interconnected with links corresponding to derivational relations (relations between base words and words derived from them). After summarizing basic characteristics of both resources, the process of merging is described, focusing on both rather technical aspects (growth of the data, measuring the quality of newly added derivational relations) and linguistic issues (treating lexical homonymy and vowel/consonant alternations). The resulting resource contains 970 thousand lemmas connected with 715 thousand derivational relations and is publicly available on the web under the CC-BY-NC-SA license. The data were incorporated in the MorphoDiTa library version 2.0 (which provides morphological analysis, generation, tagging and lemmatization for Czech) and can be browsed and searched by two web tools (DeriNet Viewer and DeriNet Search tool).
A New Integrated Open-source Morphological Analyzer for Hungarian
Authors: Attila Nov{\'a}k, Borb{\'a}la Sikl{\'o}si, Csaba Oravecz
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

The goal of a Hungarian research project has been to create an integrated Hungarian natural language processing framework. This infrastructure includes tools for analyzing Hungarian texts, integrated into a standardized environment. The morphological analyzer is one of the core components of the framework. The goal of this paper is to describe a fast and customizable morphological analyzer and its development framework, which synthesizes and further enriches the morphological knowledge implemented in previous tools existing for Hungarian. In addition, we present the method we applied to add semantic knowledge to the lexical database of the morphology. The method utilizes neural word embedding models and morphological and shallow syntactic knowledge.
New release of Mixer-6: Improved validity for phonetic study of speaker variation and identification
Authors: Eleanor Chodroff, Matthew Maciejewski, Jan Trmal, Sanjeev Khudanpur, John Godfrey
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Speaker Recognition

The Mixer series of speech corpora were collected over several years, principally to support annual NIST evaluations of speaker recognition (SR) technologies. These evaluations focused on conversational speech over a variety of channels and recording conditions. One of the series, Mixer-6, added a new condition, read speech, to support basic scientific research on speaker characteristics, as well as technology evaluation. With read speech it is possible to make relatively precise measurements of phonetic events and features, which can be correlated with the performance of speaker recognition algorithms, or directly used in phonetic analysis of speaker variability. The read speech, as originally recorded, was adequate for large-scale evaluations (e.g., fixed-text speaker ID algorithms) but only marginally suitable for acoustic-phonetic studies. Numerous errors due largely to speaker behavior remained in the corpus, with no record of their locations or rate of occurrence. We undertook the effort to correct this situation with automatic methods supplemented by human listening and annotation. The present paper describes the tools and methods, resulting corrections, and some examples of the kinds of research studies enabled by these enhancements.
Assessing the Prosody of Non-Native Speakers of English: Measures and Feature Sets
Authors: Eduardo Coutinho, Florian H{\"o}nig, Yue Zhang, Simone Hantke, Anton Batliner, Elmar N{\"o}th, Bj{\"o}rn Schuller
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: regression

In this paper, we describe a new database with audio recordings of non-native (L2) speakers of English, and the perceptual evaluation experiment conducted with native English speakers for assessing the prosody of each recording. These annotations are then used to compute the gold standard using different methods, and a series of regression experiments is conducted to evaluate their impact on the performance of a regression model predicting the degree of naturalness of L2 speech. Further, we compare the relevance of different feature groups modelling prosody in general (without speech tempo), speech rate and pauses modelling speech tempo (fluency), voice quality, and a variety of spectral features. We also discuss the impact of various fusion strategies on performance.Overall, our results demonstrate that the prosody of non-native speakers of English as L2 can be reliably assessed using supra-segmental audio features; prosodic features seem to be the most important ones.
The IFCASL Corpus of French and German Non-native and Native Read Speech
Authors: Juergen Trouvain, Anne Bonneau, Vincent Colotte, Camille Fauth, Dominique Fohr, Denis Jouvet, Jeanin J{\"u}gler, Yves Laprie, Odile Mella, Bernd M{\"o}bius, Frank Zimmerer
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

The IFCASL corpus is a French-German bilingual phonetic learner corpus designed, recorded and annotated in a project on individualized feedback in computer-assisted spoken language learning. The motivation for setting up this corpus was that there is no phonetically annotated and segmented corpus for this language pair of comparable of size and coverage. In contrast to most learner corpora, the IFCASL corpus incorporate data for a language pair in both directions, i.e. in our case French learners of German, and German learners of French. In addition, the corpus is complemented by two sub-corpora of native speech by the same speakers. The corpus provides spoken data by about 100 speakers with comparable productions, annotated and segmented on the word and the phone level, with more than 50{\%} manually corrected data. The paper reports on inter-annotator agreement and the optimization of the acoustic models for forced speech-text alignment in exercises for computer-assisted pronunciation training. Example studies based on the corpus data with a phonetic focus include topics such as the realization of /h/ and glottal stop, final devoicing of obstruents, vowel quantity and quality, pitch range, and tempo.
LELIO: An Auto-Adaptative System to Acquire Domain Lexical Knowledge in Technical Texts
Authors: Patrick Saint-Dizier
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Language Acquisition

In this paper, we investigate some language acquisition facets of an auto-adaptative system that can automatically acquire most of the relevant lexical knowledge and authoring practices for an application in a given domain. This is the LELIO project: producing customized LELIE solutions. Our goal, within the framework of LELIE (a system that tags language uses that do not follow the Constrained Natural Language principles), is to automate the long, costly and error prone lexical customization of LELIE to a given application domain. Technical texts being relatively restricted in terms of syntax and lexicon, results obtained show that this approach is feasible and relatively reliable. By auto-adaptative, we mean that the system learns from a sample of the application corpus the various lexical terms and uses crucial for LELIE to work properly (e.g. verb uses, fuzzy terms, business terms, stylistic patterns). A technical writer validation method is developed at each step of the acquisition.
Wikification for Scriptio Continua
Authors: Yugo Murawaki, Shinsuke Mori
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Segmentation

The fact that Japanese employs scriptio continua, or a writing system without spaces, complicates the first step of an NLP pipeline. Word segmentation is widely used in Japanese language processing, and lexical knowledge is crucial for reliable identification of words in text. Although external lexical resources like Wikipedia are potentially useful, segmentation mismatch prevents them from being straightforwardly incorporated into the word segmentation task. If we intentionally violate segmentation standards with the direct incorporation, quantitative evaluation will be no longer feasible. To address this problem, we propose to define a separate task that directly links given texts to an external resource, that is, wikification in the case of Wikipedia. By doing so, we can circumvent segmentation mismatch that may not necessarily be important for downstream applications. As the first step to realize the idea, we design the task of Japanese wikification and construct wikification corpora. We annotated subsets of the Balanced Corpus of Contemporary Written Japanese plus Twitter short messages. We also implement a simple wikifier and investigate its performance on these corpora.
Accessing and Elaborating Walenty - a Valence Dictionary of Polish - via Internet Browser
Authors: Bart{\l}omiej Nito{\'n}, Tomasz Bartosiak, El{\.z}bieta Hajnicz
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This article presents Walenty - a new valence dictionary of Polish predicates, concentrating on its creation process and access via Internet browser. The dictionary contains two layers, syntactic and semantic. The syntactic layer describes syntactic and morphosyntactic constraints predicates put on their dependants. The semantic layer shows how predicates and their arguments are involved in a situation described in an utterance. These two layers are connected, representing how semantic arguments can be realised on the surface. Walenty also contains a powerful phraseological (idiomatic) component. Walenty has been created and can be accessed remotely with a dedicated tool called Slowal. In this article, we focus on most important functionalities of this system. First, we will depict how to access the dictionary and how built-in filtering system (covering both syntactic and semantic phenomena) works. Later, we will describe the process of creating dictionary by Slowal tool that both supports and controls the work of lexicographers.
CEPLEXicon ― A Lexicon of Child European Portuguese
Authors: Ana L{\'u}cia Santos, Maria Jo{\~a}o Freitas, Aida Cardoso
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Lemmatization

CEPLEXicon (version 1.1) is a child lexicon resulting from the automatic tagging of two child corpora: the corpus Santos (Santos, 2006; Santos et al. 2014) and the corpus Child ― Adult Interaction (Freitas et al. 2012), which integrates information from the corpus Freitas (Freitas, 1997). This lexicon includes spontaneous speech produced by seven children (1;02.00 to 3;11.12) during approximately 86h of child-adult interaction. The automatic tagging comprised the lemmatization and morphosyntactic classification of the speech produced by the seven children included in the two child corpora; the lexicon contains information pertaining to lemmas and syntactic categories as well as absolute number of occurrences and frequencies in three age intervals: {\textless} 2 years; {\mbox{$\geq$}} 2 years and {\textless} 3 years; {\mbox{$\geq$}} 3 years. The information included in this lexicon and the format in which it is presented enables research in different areas and allows researchers to obtain measures of lexical growth. CEPLEXicon is available through the ELRA catalogue.
Extracting Weighted Language Lexicons from Wikipedia
Authors: Gregory Grefenstette
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Articles, Information Retrieval, Language Modelling, Optical Character Recognition, Optical Character Recognition (OCR), Retrieval, speech-recognition, Speech Recognition

Language models are used in applications as diverse as speech recognition, optical character recognition and information retrieval. They are used to predict word appearance, and to weight the importance of words in these applications. One basic element of language models is the list of words in a language. Another is the unigram frequency of each word. But this basic information is not available for most languages in the world. Since the multilingual Wikipedia project encourages the production of encyclopedic-like articles in many world languages, we can find there an ever-growing source of text from which to extract these two language modelling elements: word list and frequency. Here we present a simple technique for converting this Wikipedia text into lexicons of weighted unigrams for the more than 280 languages present currently present in Wikipedia. The lexicons produced, and the source code for producing them in a Linux-based system are here made available for free on the Web.
Wiktionnaire's Wikicode GLAWIfied: a Workable French Machine-Readable Dictionary
Authors: Nabil Hathout, Franck Sajous
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Diversity

GLAWI is a free, large-scale and versatile Machine-Readable Dictionary (MRD) that has been extracted from the French language edition of Wiktionary, called Wiktionnaire. In (Sajous and Hathout, 2015), we introduced GLAWI, gave the rationale behind the creation of this lexicographic resource and described the extraction process, focusing on the conversion and standardization of the heterogeneous data provided by this collaborative dictionary. In the current article, we describe the content of GLAWI and illustrate how it is structured. We also suggest various applications, ranging from linguistic studies, NLP applications to psycholinguistic experimentation. They all can take advantage of the diversity of the lexical knowledge available in GLAWI. Besides this diversity and extensive lexical coverage, GLAWI is also remarkable because it is the only free lexical resource of contemporary French that contains definitions. This unique material opens way to the renewal of MRD-based methods, notably the automated extraction and acquisition of semantic relations.
A Corpus of Images and Text in Online News
Authors: Laura Hollink, Adriatik Bedjeti, Martin van Harmelen, Desmond Elliott
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Articles

In recent years, several datasets have been released that include images and text, giving impulse to new methods that combine natural language processing and computer vision. However, there is a need for datasets of images in their natural textual context. The ION corpus contains 300K news articles published between August 2014 - 2015 in five online newspapers from two countries. The 1-year coverage over multiple publishers ensures a broad scope in terms of topics, image quality and editorial viewpoints. The corpus consists of JSON-LD files with the following data about each article: the original URL of the article on the news publisher{'}s website, the date of publication, the headline of the article, the URL of the image displayed with the article (if any), and the caption of that image. Neither the article text nor the images themselves are included in the corpus. Instead, the images are distributed as high-dimensional feature vectors extracted from a Convolutional Neural Network, anticipating their use in computer vision tasks. The article text is represented as a list of automatically generated entity and topic annotations in the form of Wikipedia/DBpedia pages. This facilitates the selection of subsets of the corpus for separate analysis or evaluation.
BosphorusSign: A Turkish Sign Language Recognition Corpus in Health and Finance Domains
Authors: Necati Cihan Camg{\"o}z, Ahmet Alp K{\i}nd{\i}ro{\u{g}}lu, Serpil Karab{\"u}kl{\"u}, Meltem Kelepir, Ay{\c{s}}e Sumru {\"O}zsoy, Lale Akarun
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Sign Language Recognition

There are as many sign languages as there are deaf communities in the world. Linguists have been collecting corpora of different sign languages and annotating them extensively in order to study and understand their properties. On the other hand, the field of computer vision has approached the sign language recognition problem as a grand challenge and research efforts have intensified in the last 20 years. However, corpora collected for studying linguistic properties are often not suitable for sign language recognition as the statistical methods used in the field require large amounts of data. Recently, with the availability of inexpensive depth cameras, groups from the computer vision community have started collecting corpora with large number of repetitions for sign language recognition research. In this paper, we present the BosphorusSign Turkish Sign Language corpus, which consists of 855 sign and phrase samples from the health, finance and everyday life domains. The corpus is collected using the state-of-the-art Microsoft Kinect v2 depth sensor, and will be the first in this sign language research field. Furthermore, there will be annotations rendered by linguists so that the corpus will appeal both to the linguistic and sign language recognition research communities.