pwc-archive/papers-with-abstracts huggingface dataset:
The DBOX Corpus Collection of Spoken Human-Human and Human-Machine Dialogues
Authors: Volha Petukhova, Martin Gropp, Dietrich Klakow, Gregor Eigner, Mario Topf, Stefan Srb, Petr Motlicek, Blaise Potard, John Dines, Olivier Deroo, Ronny Egeler, Uwe Meinz, Steffen Liersch, Anna Schmidt
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Question Answering

This paper describes the data collection and annotation carried out within the DBOX project ( Eureka project, number E! 7152). This project aims to develop interactive games based on spoken natural language human-computer dialogues, in 3 European languages: English, German and French. We collect the DBOX data continuously. We first start with human-human Wizard of Oz experiments to collect human-human data in order to model natural human dialogue behaviour, for better understanding of phenomena of human interactions and predicting interlocutors actions, and then replace the human Wizard by an increasingly advanced dialogue system, using evaluation data for system improvement. The designed dialogue system relies on a Question-Answering (QA) approach, but showing truly interactive gaming behaviour, e.g., by providing feedback, managing turns and contact, producing social signals and acts, e.g., encouraging vs. downplaying, polite vs. rude, positive vs. negative attitude towards players or their actions, etc. The DBOX dialogue corpus has required substantial investment. We expect it to have a great impact on the rest of the project. The DBOX project consortium will continue to maintain the corpus and to take an interest in its growth, e.g., expand to other languages. The resulting corpus will be publicly released.
The Database for Spoken German --- DGD2
Authors: Thomas Schmidt
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

The Database for Spoken German (Datenbank f{\"u}r Gesprochenes Deutsch, DGD2, http://dgd.ids-mannheim.de) is the central platform for publishing and disseminating spoken language corpora from the Archive of Spoken German (Archiv f{\"u}r Gesprochenes Deutsch, AGD, http://agd.ids-mannheim.de) at the Institute for the German Language in Mannheim. The corpora contained in the DGD2 come from a variety of sources, some of them in-house projects, some of them external projects. Most of the corpora were originally intended either for research into the (dialectal) variation of German or for studies in conversation analysis and related fields. The AGD has taken over the task of permanently archiving these resources and making them available for reuse to the research community. To date, the DGD2 offers access to 19 different corpora, totalling around 9000 speech events, 2500 hours of audio recordings or 8 million transcribed words. This paper gives an overview of the data made available via the DGD2, of the technical basis for its implementation, and of the most important functionalities it offers. The paper concludes with information about the users of the database and future plans for its development.
Building a Dataset of Multilingual Cognates for the Romanian Lexicon
Authors: Liviu Dinu, Alina Maria Ciobanu
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Cross-Lingual Information Retrieval, Information Retrieval, Language Acquisition, Machine Translation, Retrieval, Semantic Textual Similarity, Translation

Identifying cognates is an interesting task with applications in numerous research areas, such as historical and comparative linguistics, language acquisition, cross-lingual information retrieval, readability and machine translation. We propose a dictionary-based approach to identifying cognates based on etymology and etymons. We account for relationships between languages and we extract etymology-related information from electronic dictionaries. We employ the dataset of cognates that we obtain as a gold standard for evaluating to which extent orthographic methods can be used to detect cognate pairs. The question that arises is whether they are able to discriminate between cognates and non-cognates, given the orthographic changes undergone by foreign words when entering new languages. We investigate some orthographic approaches widely used in this research area and some original metrics as well. We run our experiments on the Romanian lexicon, but the method we propose is adaptable to any language, as far as resources are available.
Benchmarking the Extraction and Disambiguation of Named Entities on the Semantic Web
Authors: Giuseppe Rizzo, Marieke van Erp, Rapha{\"e}l Troncy
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Benchmarking, Entity Linking, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

Named entity recognition and disambiguation are of primary importance for extracting information and for populating knowledge bases. Detecting and classifying named entities has traditionally been taken on by the natural language processing community, whilst linking of entities to external resources, such as those in DBpedia, has been tackled by the Semantic Web community. As these tasks are treated in different communities, there is as yet no oversight on the performance of these tasks combined. We present an approach that combines the state-of-the art from named entity recognition in the natural language processing domain and named entity linking from the semantic web community. We report on experiments and results to gain more insights into the strengths and limitations of current approaches on these tasks. Our approach relies on the numerous web extractors supported by the NERD framework, which we combine with a machine learning algorithm to optimize recognition and linking of named entities. We test our approach on four standard data sets that are composed of two diverse text types, namely newswire and microposts.
Automatic Expansion of the MRC Psycholinguistic Database Imageability Ratings
Authors: Ting Liu, Kit Cho, G. Aaron Broadwell, Samira Shaikh, Tomek Strzalkowski, John Lien, Sarah Taylor, Laurie Feldman, Boris Yamrom, Nick Webb, Umit Boz, Ignacio Cases, Ching-Sheng Lin
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

Recent studies in metaphor extraction across several languages (Broadwell et al., 2013; Strzalkowski et al., 2013) have shown that word imageability ratings are highly correlated with the presence of metaphors in text. Information about imageability of words can be obtained from the MRC Psycholinguistic Database (MRCPD) for English words and L{\'e}xico Informatizado del Espa{\~n}ol Programa (LEXESP) for Spanish words, which is a collection of human ratings obtained in a series of controlled surveys. Unfortunately, word imageability ratings were collected for only a limited number of words: 9,240 words in English, 6,233 in Spanish; and are unavailable at all in the other two languages studied: Russian and Farsi. The present study describes an automated method for expanding the MRCPD by conferring imageability ratings over the synonyms and hyponyms of existing MRCPD words, as identified in Wordnet. The result is an expanded MRCPD+ database with imagea-bility scores for more than 100,000 words. The appropriateness of this expansion process is assessed by examining the structural coherence of the expanded set and by validating the expanded lexicon against human judgment. Finally, the performance of the metaphor extraction system is shown to improve significantly with the expanded database. This paper describes the process for English MRCPD+ and the resulting lexical resource. The process is analogous for other languages.
Towards building a Kashmiri Treebank: Setting up the Annotation Pipeline
Authors: Riyaz Ahmad Bhat, Shahid Mushtaq Bhat, Dipti Misra Sharma
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Dependency Parsing, POS

Kashmiri is a resource poor language with very less computational and language resources available for its text processing. As the main contribution of this paper, we present an initial version of the Kashmiri Dependency Treebank. The treebank consists of 1,000 sentences (17,462 tokens), annotated with part-of-speech (POS), chunk and dependency information. The treebank has been manually annotated using the Paninian Computational Grammar (PCG) formalism (Begum et al., 2008; Bharati et al., 2009). This version of Kashmiri treebank is an extension of its earlier verion of 500 sentences (Bhat, 2012), a pilot experiment aimed at defining the annotation guidelines on a small subset of Kashmiri corpora. In this paper, we have refined the guidelines with some significant changes and have carried out inter-annotator agreement studies to ascertain its quality. We also present a dependency parsing pipeline, consisting of a tokenizer, a stemmer, a POS tagger, a chunker and an inter-chunk dependency parser. It, therefore, constitutes the first freely available, open source dependency parser of Kashmiri, setting the initial baseline for Kashmiri dependency parsing.
SenTube: A Corpus for Sentiment Analysis on YouTube Social Media
Authors: Olga Uryupina, Barbara Plank, Aliaksei Severyn, Agata Rotondi, Aless Moschitti, ro
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Document Classification, Informativeness, Sentiment Analysis, Spam detection, Text Categorization

In this paper we present SenTube -- a dataset of user-generated comments on YouTube videos annotated for information content and sentiment polarity. It contains annotations that allow to develop classifiers for several important NLP tasks: (i) sentiment analysis, (ii) text categorization (relatedness of a comment to video and/or product), (iii) spam detection, and (iv) prediction of comment informativeness. The SenTube corpus favors the development of research on indexing and searching YouTube videos exploiting information derived from comments. The corpus will cover several languages: at the moment, we focus on English and Italian, with Spanish and Dutch parts scheduled for the later stages of the project. For all the languages, we collect videos for the same set of products, thus offering possibilities for multi- and cross-lingual experiments. The paper provides annotation guidelines, corpus statistics and annotator agreement details.
CIEMPIESS: A New Open-Sourced Mexican Spanish Radio Corpus
Authors: Carlos Daniel Hern Mena, ez, Abel Herrera Camacho
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Modelling

Corpus de Investigaci{\'o}n en Espa{\~n}ol de M{\'e}xico del Posgrado de Ingenier{\'\i}a El{\'e}ctrica y Servicio Social{''} (CIEMPIESS) is a new open-sourced corpus extracted from Spanish spoken FM podcasts in the dialect of the center of Mexico. The CIEMPIESS corpus was designed to be used in the field of automatic speech recongnition (ASR) and it is provided with two different kind of pronouncing dictionaries, one of them containing the phonemes of Mexican Spanish and the other containing this same phonemes plus allophones. Corpus annotation took into account the tonic vowel of every word and the four different sounds that letter {``}x{''} presents in the Spanish language. CIEMPIESS corpus is also provided with two different language models extracted from electronic newsletters, one of them takes into account the tonic vowels but not the other one. Both the dictionaries and the language models allow users to experiment different scenarios for the recognition task in order to adequate the corpus to their needs.
SAVAS: Collecting, Annotating and Sharing Audiovisual Language Resources for Automatic Subtitling
Authors: Arantza del Pozo, Alipr, Carlo i, Aitor {\'A}lvarez, Carlos Mendes, Joao P. Neto, S{\'e}rgio Paulo, Nicola Piccinini, Matteo Raffaelli
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Modelling, Speech Recognition

This paper describes the data collection, annotation and sharing activities carried out within the FP7 EU-funded SAVAS project. The project aims to collect, share and reuse audiovisual language resources from broadcasters and subtitling companies to develop large vocabulary continuous speech recognisers in specific domains and new languages, with the purpose of solving the automated subtitling needs of the media industry.
Exploring and Visualizing Variation in Language Resources
Authors: Peter Fankhauser, J{\"o}rg Knappen, Elke Teich
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

Language resources are often compiled for the purpose of variational analysis, such as studying differences between genres, registers, and disciplines, regional and diachronic variation, influence of gender, cultural context, etc. Often the sheer number of potentially interesting contrastive pairs can get overwhelming due to the combinatorial explosion of possible combinations. In this paper, we present an approach that combines well understood techniques for visualization heatmaps and word clouds with intuitive paradigms for exploration drill down and side by side comparison to facilitate the analysis of language variation in such highly combinatorial situations. Heatmaps assist in analyzing the overall pattern of variation in a corpus, and word clouds allow for inspecting variation at the level of words.
Simple Effective Microblog Named Entity Recognition: Arabic as an Example
Authors: Kareem Darwish, Wei Gao
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Domain Adaptation, Information Retrieval, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Despite many recent papers on Arabic Named Entity Recognition (NER) in the news domain, little work has been done on microblog NER. NER on microblogs presents many complications such as informality of language, shortened named entities, brevity of expressions, and inconsistent capitalization (for cased languages). We introduce simple effective language-independent approaches for improving NER on microblogs, based on using large gazetteers, domain adaptation, and a two-pass semi-supervised method. We use Arabic as an example language to compare the relative effectiveness of the approaches and when best to use them. We also present a new dataset for the task. Results of combining the proposed approaches show an improvement of 35.3 F-measure points over a baseline system trained on news data and an improvement of 19.9 F-measure points over the same system but trained on microblog data.
Priberam Compressive Summarization Corpus: A New Multi-Document Summarization Corpus for European Portuguese
Authors: Miguel B. Almeida, Mariana S. C. Almeida, Andr{\'e} F. T. Martins, Helena Figueira, Pedro Mendes, Cl{\'a}udia Pinto
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Document Summarization, Information Retrieval, Multi-Document Summarization, Sentence, Sentence Compression, Text Generation

In this paper, we introduce the Priberam Compressive Summarization Corpus, a new multi-document summarization corpus for European Portuguese. The corpus follows the format of the summarization corpora for English in recent DUC and TAC conferences. It contains 80 manually chosen topics referring to events occurred between 2010 and 2013. Each topic contains 10 news stories from major Portuguese newspapers, radio and TV stations, along with two human generated summaries up to 100 words. Apart from the language, one important difference from the DUC/TAC setup is that the human summaries in our corpus are {\textbackslash}emph{compressive}: the annotators performed only sentence and word deletion operations, as opposed to generating summaries from scratch. We use this corpus to train and evaluate learning-based extractive and compressive summarization systems, providing an empirical comparison between these two approaches. The corpus is made freely available in order to facilitate research on automatic summarization.
Hope and Fear: How Opinions Influence Factuality
Authors: Chantal van Son, Marieke van Erp, Antske Fokkens, Piek Vossen
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Opinion Mining, Sentiment Analysis

Both sentiment and event factuality are fundamental information levels for our understanding of events mentioned in news texts. Most research so far has focused on either modeling opinions or factuality. In this paper, we propose a model that combines the two for the extraction and interpretation of perspectives on events. By doing so, we can explain the way people perceive changes in (their belief of) the world as a function of their fears of changes to the bad or their hopes of changes to the good. This study seeks to examine the effectiveness of this approach by applying factuality annotations, based on FactBank, on top of the MPQA Corpus, a corpus containing news texts annotated for sentiments and other private states. Our findings suggest that this approach can be valuable for the understanding of perspectives, but that there is still some work to do on the refinement of the integration.
Linking Pictographs to Synsets: Sclera2Cornetto
Authors: V, Vincent eghinste, Ineke Schuurman
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Translation

Social inclusion of people with Intellectual and Developmental Disabilities can be promoted by offering them ways to independently use the internet. People with reading or writing disabilities can use pictographs instead of text. We present a resource in which we have linked a set of 5710 pictographs to lexical-semantic concepts in Cornetto, a Wordnet-like database for Dutch. We show that, by using this resource in a text-to-pictograph translation system, we can greatly improve the coverage comparing with a baseline where words are converted into pictographs only if the word equals the filename.
Characterizing and Predicting Bursty Events: The Buzz Case Study on Twitter
Authors: Mohamed Morchid, Georges Linar{\`e}s, Richard Dufour
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Diversity, Prediction

The prediction of bursty events on the Internet is a challenging task. Difficulties are due to the diversity of information sources, the size of the Internet, dynamics of popularity, user behaviors... On the other hand, Twitter is a structured and limited space. In this paper, we present a new method for predicting bursty events using content-related indices. Prediction is performed by a neural network that combines three features in order to predict the number of retweets of a tweet on the Twitter platform. The indices are related to popularity, expressivity and singularity. Popularity index is based on the analysis of RSS streams. Expressivity uses a dictionary that contains words annotated in terms of expressivity load. Singularity represents outlying topic association estimated via a Latent Dirichlet Allocation (LDA) model. Experiments demonstrate the effectiveness of the proposal with a 72{\%} F-measure prediction score for the tweets that have been forwarded at least 60 times.
Information Extraction from German Patient Records via Hybrid Parsing and Relation Extraction Strategies
Authors: Hans-Ulrich Krieger, Christian Spurk, Hans Uszkoreit, Feiyu Xu, Yi Zhang, Frank M{\"u}ller, Thomas Tolxdorff
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Chunking, Medical Diagnosis, Morphological Analysis, Named Entity Recognition (NER), Relation, Relation Extraction

In this paper, we report on first attempts and findings to analyzing German patient records, using a hybrid parsing architecture and a combination of two relation extraction strategies. On a practical level, we are interested in the extraction of concepts and relations among those concepts, a necessary cornerstone for building medical information systems. The parsing pipeline consists of a morphological analyzer, a robust chunk parser adapted to Latin phrases used in medical diagnosis, a repair rule stage, and a probabilistic context-free parser that respects the output from the chunker. The relation extraction stage is a combination of two systems: SProUT, a shallow processor which uses hand-written rules to discover relation instances from local text units and DARE which extracts relation instances from complete sentences, using rules that are learned in a bootstrapping process, starting with semantic seeds. Two small experiments have been carried out for the parsing pipeline and the relation extraction stage.
The MMASCS multi-modal annotated synchronous corpus of audio, video, facial motion and tongue motion data of normal, fast and slow speech
Authors: Dietmar Schabus, Michael Pucher, Phil Hoole
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Speech Synthesis

In this paper, we describe and analyze a corpus of speech data that we have recorded in multiple modalities simultaneously: facial motion via optical motion capturing, tongue motion via electro-magnetic articulography, as well as conventional video and high-quality audio. The corpus consists of 320 phonetically diverse sentences uttered by a male Austrian German speaker at normal, fast and slow speaking rate. We analyze the influence of speaking rate on phone durations and on tongue motion. Furthermore, we investigate the correlation between tongue and facial motion. The data corpus is available free of charge for research use, including phonetic annotations and a playback software which visualizes the 3D data, from the website http://cordelia.ftw.at/mmascs
Genres in the Prague Discourse Treebank
Authors: Lucie Pol{\'a}kov{\'a}, Pavl{\'\i}na J{\'\i}nov{\'a}, Ji{\v{r}}{\'\i} M{\'\i}rovsk{\'y}
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Sentence, Sentiment Analysis

We present the project of classification of Prague Discourse Treebank documents (Czech journalistic texts) for their genres. Our main interest lies in opening the possibility to observe how text coherence is realized in different types (in the genre sense) of language data and, in the future, in exploring the ways of using genres as a feature for multi-sentence-level language technologies. In the paper, we first describe the motivation and the concept of the genre annotation, and briefly introduce the Prague Discourse Treebank. Then, we elaborate on the process of manual annotation of genres in the treebank, from the annotators{'} manual work to post-annotation checks and to the inter-annotator agreement measurements. The annotated genres are subsequently analyzed together with discourse relations (already annotated in the treebank) â€• we present distributions of the annotated genres and results of studying distinctions of distributions of discourse relations across the individual genres.
Speech Recognition Web Services for Dutch
Authors: Joris Pelemans, Kris Demuynck, Hugo Van hamme, Patrick Wambacq
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Speech-to-Text

In this paper we present 3 applications in the domain of Automatic Speech Recognition for Dutch, all of which are developed using our in-house speech recognition toolkit SPRAAK. The speech-to-text transcriber is a large vocabulary continuous speech recognizer, optimized for Southern Dutch. It is capable to select components and adjust parameters on the fly, based on the observed conditions in the audio and was recently extended with the capability of adding new words to the lexicon. The grapheme-to-phoneme converter generates possible pronunciations for Dutch words, based on lexicon lookup and linguistic rules. The speech-text alignment system takes audio and text as input and constructs a time aligned output where every word receives exact begin and end times. All three of the applications (and others) are freely available, after registration, as a web application on http://www.spraak.org/webservice/ and in addition, can be accessed as a web service in automated tools.