pwc-archive/papers-with-abstracts huggingface dataset:
Building and Modelling Multilingual Subjective Corpora
Authors: Motaz Saad, David Langlois, Kamel Sma{\"\i}li
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Modelling, Machine Translation, Opinion Mining, Sentiment Analysis, Subjectivity Analysis

Building multilingual opinionated models requires multilingual corpora annotated with opinion labels. Unfortunately, such kind of corpora are rare. We consider opinions in this work as subjective or objective. In this paper, we introduce an annotation method that can be reliably transferred across topic domains and across languages. The method starts by building a classifier that annotates sentences into subjective/objective label using a training data from {``}movie reviews{''} domain which is in English language. The annotation can be transferred to another language by classifying English sentences in parallel corpora and transferring the same annotation to the same sentences of the other language. We also shed the light on the link between opinion mining and statistical language modelling, and how such corpora are useful for domain specific language modelling. We show the distinction between subjective and objective sentences which tends to be stable across domains and languages. Our experiments show that language models trained on objective (respectively subjective) corpus lead to better perplexities on objective (respectively subjective) test.
GRASS: the Graz corpus of Read And Spontaneous Speech
Authors: Barbara Schuppler, Martin Hagmueller, Juan A. Morales-Cordovilla, Hannes Pessentheiner
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Speech Recognition

This paper provides a description of the preparation, the speakers, the recordings, and the creation of the orthographic transcriptions of the first large scale speech database for Austrian German. It contains approximately 1900 minutes of (read and spontaneous) speech produced by 38 speakers. The corpus consists of three components. First, the Conversation Speech (CS) component contains free conversations of one hour length between friends, colleagues, couples, or family members. Second, the Commands Component (CC) contains commands and keywords which were either read or elicited by pictures. Third, the Read Speech (RS) component contains phonetically balanced sentences and digits. The speech of all components has been recorded at super-wideband quality in a soundproof recording-studio with head-mounted microphones, large-diaphragm microphones, a laryngograph, and with a video camera. The orthographic transcriptions, which have been created and subsequently corrected manually, contain approximately 290 000 word tokens from 15 000 different word types.
Linguistic resources and cats: how to use ISOcat, RELcat and SCHEMAcat
Authors: Menzo Windhouwer, Ineke Schuurman
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

Within the European CLARIN infrastructure ISOcat is used to enable both humans and computer programs to find specific resources even when they use different terminology or data structures. In order to do so, it should be clear which concepts are used in these resources, both at the level of metadata for the resource as well as its content, and what is meant by them. The concepts can be specified in ISOcat. SCHEMAcat enables us to relate the concepts used by a resource, while RELcat enables to type these relationships and add relationships beyond resource boundaries. This way these three registries together allow us (and the programs) to find what we are looking for.
Bring vs. MTRoget: Evaluating automatic thesaurus translation
Authors: Lars Borin, Jens Allwood, Gerard de Melo
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Translation

Evaluation of automatic language-independent methods for language technology resource creation is difficult, and confounded by a largely unknown quantity, viz. to what extent typological differences among languages are significant for results achieved for one language or language pair to be applicable across languages generally. In the work presented here, as a simplifying assumption, language-independence is taken as axiomatic within certain specified bounds. We evaluate the automatic translation of Roget{'}s {``}Thesaurus{''} from English into Swedish using an independently compiled Roget-style Swedish thesaurus, S.C. Bring{'}s {``}Swedish vocabulary arranged into conceptual classes{''} (1930). Our expectation is that this explicit evaluation of one of the thesaureses created in the MTRoget project will provide a good estimate of the quality of the other thesauruses created using similar methods.
Introducing a Framework for the Evaluation of Music Detection Tools
Authors: Paula Lopez-Otero, Docio-Fern, Laura ez, Carmen Garcia-Mateo
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Information Retrieval, Music Information Retrieval, Retrieval

The huge amount of multimedia information available nowadays makes its manual processing prohibitive, requiring tools for automatic labelling of these contents. This paper describes a framework for assessing a music detection tool; this framework consists of a database, composed of several hours of radio recordings that include different types of radio programmes, and a set of evaluation measures for evaluating the performance of a music detection tool in detail. A tool for automatically detecting music in audio streams, with application to music information retrieval tasks, is presented as well. The aim of this tool is to discard the audio excerpts that do not contain music in order to avoid their unnecessary processing. This tool applies fingerprinting to different acoustic features extracted from the audio signal in order to remove perceptual irrelevancies, and a support vector machine is trained for classifying these fingerprints in classes music and no-music. The validity of this tool is assessed in the proposed evaluation framework.
WordNet---Wikipedia---Wiktionary: Construction of a Three-way Alignment
Authors: Tristan Miller, Iryna Gurevych
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Question Answering, Word Sense Disambiguation

The coverage and quality of conceptual information contained in lexical semantic resources is crucial for many tasks in natural language processing. Automatic alignment of complementary resources is one way of improving this coverage and quality; however, past attempts have always been between pairs of specific resources. In this paper we establish some set-theoretic conventions for describing concepts and their alignments, and use them to describe a method for automatically constructing n-way alignments from arbitrary pairwise alignments. We apply this technique to the production of a three-way alignment from previously published WordNet-Wikipedia and WordNet-Wiktionary alignments. We then present a quantitative and informal qualitative analysis of the aligned resource. The three-way alignment was found to have greater coverage, an enriched sense representation, and coarser sense granularity than both the original resources and their pairwise alignments, though this came at the cost of accuracy. An evaluation of the induced word sense clusters in a word sense disambiguation task showed that they were no better than random clusters of equivalent granularity. However, use of the alignments to enrich a sense inventory with additional sense glosses did significantly improve the performance of a baseline knowledge-based WSD algorithm.
Cross-linguistic annotation of narrativity for English/French verb tense disambiguation
Authors: Cristina Grisot, Thomas Meyer
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Translation

This paper presents manual and automatic annotation experiments for a pragmatic verb tense feature (narrativity) in English/French parallel corpora. The feature is considered to play an important role for translating English Simple Past tense into French, where three different tenses are available. Whether the French Passe {\`I} Compose {\`I}, Passe {\`I} Simple or Imparfait should be used is highly dependent on a longer-range context, in which either narrative events ordered in time or mere non-narrative state of affairs in the past are described. This longer-range context is usually not available to current machine translation (MT) systems, that are trained on parallel corpora. Annotating narrativity prior to translation is therefore likely to help current MT systems. Our experiments show that narrativity can be reliably identified with kappa-values of up to 0.91 in manual annotation and with F1 scores of up to 0.72 in automatic annotation.
The taraX\"U corpus of human-annotated machine translations
Authors: Eleftherios Avramidis, Aljoscha Burchardt, Sabine Hunsicker, Maja Popovi{\'c}, Cindy Tscherwinka, David Vilar, Hans Uszkoreit
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: General Classification, Machine Translation, Translation

Human translators are the key to evaluating machine translation (MT) quality and also to addressing the so far unanswered question when and how to use MT in professional translation workflows. This paper describes the corpus developed as a result of a detailed large scale human evaluation consisting of three tightly connected tasks: ranking, error classification and post-editing.
Detecting Document Structure in a Very Large Corpus of UK Financial Reports
Authors: Mahmoud El-Haj, Paul Rayson, Steve Young, Martin Walker
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Text Generation

In this paper we present the evaluation of our automatic methods for detecting and extracting document structure in annual financial reports. The work presented is part of the Corporate Financial Information Environment (CFIE) project in which we are using Natural Language Processing (NLP) techniques to study the causes and consequences of corporate disclosure and financial reporting outcomes. We aim to uncover the determinants of financial reporting quality and the factors that influence the quality of information disclosed to investors beyond the financial statements. The CFIE consists of the supply of information by firms to investors, and the mediating influences of information intermediaries on the timing, relevance and reliability of information available to investors. It is important to compare and contrast specific elements or sections of each annual financial report across our entire corpus rather than working at the full document level. We show that the values of some metrics e.g. readability will vary across sections, thus improving on previous research research based on full texts.
Latent Semantic Analysis Models on Wikipedia and TASA
Authors: Dan {\textcommabelow{S}}tef{\u{a}}nescu, Rajendra Banjade, Vasile Rus
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Semantic Textual Similarity, Word Similarity

This paper introduces a collection of freely available Latent Semantic Analysis models built on the entire English Wikipedia and the TASA corpus. The models differ not only on their source, Wikipedia versus TASA, but also on the linguistic items they focus on: all words, content-words, nouns-verbs, and main concepts. Generating such models from large datasets (e.g. Wikipedia), that can provide a large coverage for the actual vocabulary in use, is computationally challenging, which is the reason why large LSA models are rarely available. Our experiments show that for the task of word-to-word similarity, the scores assigned by these models are strongly correlated with human judgment, outperforming many other frequently used measures, and comparable to the state of the art.
The Strategic Impact of META-NET on the Regional, National and International Level
Authors: Georg Rehm, Hans Uszkoreit, Sophia Ananiadou, N{\'u}ria Bel, Audron{\.e} Bielevi{\v{c}}ien{\.e}, Lars Borin, Ant{\'o}nio Branco, Gerhard Budin, Nicoletta Calzolari, Walter Daelemans, Radovan Garab{\'\i}k, Marko Grobelnik, Carmen Garc{\'\i}a-Mateo, Josef van Genabith, Jan Haji{\v{c}}, Inma Hern{\'a}ez, John Judge, Svetla Koeva, Simon Krek, Cvetana Krstev, Krister Lind{\'e}n, Bernardo Magnini, Joseph Mariani, John McNaught, Maite Melero, Monica Monachini, Asunci{\'o}n Moreno, Jan Odijk, Maciej Ogrodniczuk, Piotr P{\k{e}}zik, Stelios Piperidis, Adam Przepi{\'o}rkowski, Eir{\'\i}kur R{\"o}gnvaldsson, Michael Rosner, Bolette Pedersen, Inguna Skadi{\c{n}}a, Koenraad De Smedt, Marko Tadi{\'c}, Paul Thompson, Dan Tufi{\c{s}}, Tam{\'a}s V{\'a}radi, Andrejs Vasi{\c{l}}jevs, Kadri Vider, Jolanta Zabarskaite
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation

This article provides an overview of the dissemination work carried out in META-NET from 2010 until early 2014; we describe its impact on the regional, national and international level, mainly with regard to politics and the situation of funding for LT topics. This paper documents the initiativeÂ’s work throughout Europe in order to boost progress and innovation in our field.
The Weltmodell: A Data-Driven Commonsense Knowledge Base
Authors: Alan Akbik, Thilo Michael
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Diversity, Open Information Extraction

We present the Weltmodell, a commonsense knowledge base that was automatically generated from aggregated dependency parse fragments gathered from over 3.5 million English language books. We leverage the magnitude and diversity of this dataset to arrive at close to ten million distinct N-ary commonsense facts using techniques from open-domain Information Extraction (IE). Furthermore, we compute a range of measures of association and distributional similarity on this data. We present the results of our efforts using a browsable web demonstrator and publicly release all generated data for use and discussion by the research community. In this paper, we give an overview of our knowledge acquisition method and representation model, and present our web demonstrator.
German Alcohol Language Corpus - the Question of Dialect
Authors: Florian Schiel, Thomas Kisler
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

Speech uttered under the influence of alcohol is known to deviate from the speech of the same person when sober. This is an important feature in forensic investigations and could also be used to detect intoxication in the automotive environment. Aside from acoustic-phonetic features and speech content which have already been studied by others in this contribution we address the question whether speakers use dialectal variation or dialect words more frequently when intoxicated than when sober. We analyzed 300,000 recorded word tokens in read and spontaneous speech uttered by 162 female and male speakers within the German Alcohol Language Corpus. We found that contrary to our expectations the frequency of dialectal forms decreases significantly when speakers are under the influence. We explain this effect with a compensatory over-shoot mechanism: speakers are aware of their intoxication and that they are being monitored. In forensic analysis of speech this `awareness factor{'} must be taken into account.
CLARA: A New Generation of Researchers in Common Language Resources and Their Applications
Authors: Koenraad De Smedt, Erhard Hinrichs, Detmar Meurers, Inguna Skadi{\c{n}}a, Bolette Pedersen, Costanza Navarretta, N{\'u}ria Bel, Krister Lind{\'e}n, Mark{\'e}ta Lopatkov{\'a}, Jan Haji{\v{c}}, Gisle Andersen, Przemyslaw Lenkiewicz
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

CLARA (Common Language Resources and Their Applications) is a Marie Curie Initial Training Network which ran from 2009 until 2014 with the aim of providing researcher training in crucial areas related to language resources and infrastructure. The scope of the project was broad and included infrastructure design, lexical semantic modeling, domain modeling, multimedia and multimodal communication, applications, and parsing technologies and grammar models. An international consortium of 9 partners and 12 associate partners employed researchers in 19 new positions and organized a training program consisting of 10 thematic courses and summer/winter schools. The project has resulted in new theoretical insights as well as new resources and tools. Most importantly, the project has trained a new generation of researchers who can perform advanced research and development in language resources and technologies.
A Large Corpus of Product Reviews in Portuguese: Tackling Out-Of-Vocabulary Words
Authors: Nathan Hartmann, Lucas Avan{\c{c}}o, Pedro Balage, Magali Duran, Maria das Gra{\c{c}}as Volpe Nunes, Thiago Pardo, S Alu{\'\i}sio, ra
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Lexical Normalization, Opinion Mining

Web 2.0 has allowed a never imagined communication boom. With the widespread use of computational and mobile devices, anyone, in practically any language, may post comments in the web. As such, formal language is not necessarily used. In fact, in these communicative situations, language is marked by the absence of more complex syntactic structures and the presence of internet slang, with missing diacritics, repetitions of vowels, and the use of chat-speak style abbreviations, emoticons and colloquial expressions. Such language use poses severe new challenges for Natural Language Processing (NLP) tools and applications, which, so far, have focused on well-written texts. In this work, we report the construction of a large web corpus of product reviews in Brazilian Portuguese and the analysis of its lexical phenomena, which support the development of a lexical normalization tool for, in future work, subsidizing the use of standard NLP products for web opinion mining and summarization purposes.
Shata-Anuvadak: Tackling Multiway Translation of Indian Languages
Authors: Anoop Kunchukuttan, Abhijit Mishra, Rajen Chatterjee, Ritesh Shah, Pushpak Bhattacharyya
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Translation, Transliteration

We present a compendium of 110 Statistical Machine Translation systems built from parallel corpora of 11 Indian languages belonging to both Indo-Aryan and Dravidian families. We analyze the relationship between translation accuracy and the language families involved. We feel that insights obtained from this analysis will provide guidelines for creating machine translation systems of specific Indian language pairs. We build phrase based systems and some extensions. Across multiple languages, we show improvements on the baseline phrase based systems using these extensions: (1) source side reordering for English-Indian language translation, and (2) transliteration of untranslated words for Indian language-Indian language translation. These enhancements harness shared characteristics of Indian languages. To stimulate similar innovation widely in the NLP community, we have made the trained models for these language pairs publicly available.
The CLARIN Research Infrastructure: Resources and Tools for eHumanities Scholars
Authors: Erhard Hinrichs, Steven Krauwer
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

CLARIN is the short name for the Common Language Resources and Technology Infrastructure, which aims at providing easy and sustainable access for scholars in the humanities and social sciences to digital language data and advanced tools to discover, explore, exploit, annotate, analyse or combine them, independent of where they are located. CLARIN is in the process of building a networked federation of European data repositories, service centers and centers of expertise, with single sign-on access for all members of the academic community in all participating countries. Tools and data from different centers will be interoperable so that data collections can be combined and tools from different sources can be chained to perform complex operations to support researchers in their work. Interoperability of language resources and tools in the federation of CLARIN Centers is ensured by adherence to TEI and ISO standards for text encoding, by the use of persistent identifiers, and by the observance of common protocols. The purpose of the present paper is to give an overview of language resources, tools, and services that CLARIN presently offers.
Sprinter: Language Technologies for Interactive and Multimedia Language Learning
Authors: Renlong Ai, Marcela Charfuelan, Walter Kasper, Tina Kl{\"u}wer, Hans Uszkoreit, Feiyu Xu, S Gasber, ra, Gien, Philip t
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

Modern language learning courses are no longer exclusively based on books or face-to-face lectures. More and more lessons make use of multimedia and personalized learning methods. Many of these are based on e-learning solutions. Learning via the Internet provides 7/24 services that require sizeable human resources. Therefore we witness a growing economic pressure to employ computer-assisted methods for improving language learning in quality, efficiency and scalability. In this paper, we will address three applications of language technologies for language learning: 1) Methods and strategies for pronunciation training in second language learning, e.g., multimodal feedback via visualization of sound features, speech verification and prosody transplantation; 2) Dialogue-based language learning games; 3) Application of parsing and generation technologies to the automatic generation of paraphrases for the semi-automatic production of learning material.
Bilingual Dictionary Induction as an Optimization Problem
Authors: Wushouer Mairidan, Toru Ishida, Donghui Lin, Katsutoshi Hirayama
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

Bilingual dictionaries are vital in many areas of natural language processing, but such resources are rarely available for lower-density language pairs, especially for those that are closely related. Pivot-based induction consists of using a third language to bridge a language pair. As an approach to create new dictionaries, it can generate wrong translations due to polysemy and ambiguous words. In this paper we propose a constraint approach to pivot-based dictionary induction for the case of two closely related languages. In order to take into account the word senses, we use an approach based on semantic distances, in which possibly missing translations are considered, and instance of induction is encoded as an optimization problem to generate new dictionary. Evaluations show that the proposal achieves 83.7{\%} accuracy and approximately 70.5{\%} recall, thus outperforming the baseline pivot-based method.
Two Approaches to Metaphor Detection
Authors: Brian MacWhinney, Davida Fromm
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Vocal Bursts Valence Prediction

Methods for automatic detection and interpretation of metaphors have focused on analysis and utilization of the ways in which metaphors violate selectional preferences (Martin, 2006). Detection and interpretation processes that rely on this method can achieve wide coverage and may be able to detect some novel metaphors. However, they are prone to high false alarm rates, often arising from imprecision in parsing and supporting ontological and lexical resources. An alternative approach to metaphor detection emphasizes the fact that many metaphors become conventionalized collocations, while still preserving their active metaphorical status. Given a large enough corpus for a given language, it is possible to use tools like SketchEngine (Kilgariff, Rychly, Smrz, {\&} Tugwell, 2004) to locate these high frequency metaphors for a given target domain. In this paper, we examine the application of these two approaches and discuss their relative strengths and weaknesses for metaphors in the target domain of economic inequality in English, Spanish, Farsi, and Russian.