pwc-archive/papers-with-abstracts huggingface dataset:
Building a Crisis Management Term Resource for Social Media: The Case of Floods and Protests
Authors: Irina Temnikova, Andrea Varga, Dogan Biyikli
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Descriptive, Information Retrieval, Management, Named Entity Recognition (NER), Question Answering

Extracting information from social media is being currently exploited for a variety of tasks, including the recognition of emergency events in Twitter. This is done in order to supply Crisis Management agencies with additional crisis information. The existing approaches, however, mostly rely on geographic location and hashtags/keywords, obtained via a manual Twitter search. As we expect that Twitter crisis terminology would differ from existing crisis glossaries, we start collecting a specialized terminological resource to support this task. The aim of this resource is to contain sets of crisis-related Twitter terms which are the same for different instances of the same type of event. This article presents a preliminary investigation of the nature of terms used in four events of two crisis types, tests manual and automatic ways to collect these terms and comes up with an initial collection of terms for these two types of events. As contributions, a novel annotation schema is presented, along with important insights into the differences in annotations between different specialists, descriptive term statistics, and performance results of existing automatic terminology recognition approaches for this task.
ILLINOISCLOUDNLP: Text Analytics Services in the Cloud
Authors: Hao Wu, Zhiye Fei, Aaron Dai, Mark Sammons, Dan Roth, Stephen Mayhew
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Knowledge Base Population

Natural Language Processing (NLP) continues to grow in popularity in a range of research and commercial applications. However, installing, maintaining, and running NLP tools can be time consuming, and many commercial and research end users have only intermittent need for large processing capacity. This paper describes ILLINOISCLOUDNLP, an on-demand framework built around NLPCURATOR and Amazon Web ServicesÂ’ Elastic Compute Cloud (EC2). This framework provides a simple interface to end users via which they can deploy one or more NLPCURATOR instances on EC2, upload plain text documents, specify a set of Text Analytics tools (NLP annotations) to apply, and process and store or download the processed data. It can also allow end users to use a model trained on their own data: ILLINOISCLOUDNLP takes care of training, hosting, and applying it to new data just as it does with existing models within NLPCURATOR. As a representative use case, we describe our use of ILLINOISCLOUDNLP to process 3.05 million documents used in the 2012 and 2013 Text Analysis Conference Knowledge Base Population tasks at a relatively deep level of processing, in approximately 20 hours, at an approximate cost of US{\$}500; this is about 20 times faster than doing so on a single server and requires no human supervision and no NLP or Machine Learning expertise.
Text Readability and Word Distribution in Japanese
Authors: Satoshi Sato
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

This paper reports the relation between text readability and word distribution in the Japanese language. There was no similar study in the past due to three major obstacles: (1) unclear definition of Japanese {``}word{''}, (2) no balanced corpus, and (3) no readability measure. Compilation of the Balanced Corpus of Contemporary Written Japanese (BCCWJ) and development of a readability predictor remove these three obstacles and enable this study. First, we have counted the frequency of each word in each text in the corpus. Then we have calculated the frequency rank of words both in the whole corpus and in each of three readability bands. Three major findings are: (1) the proportion of high-frequent words to tokens in Japanese is lower than that in English; (2) the type-coverage curve of words in the difficult-band draws an unexpected shape; (3) the size of the intersection between high-frequent words in the easy-band and these in the difficult-band is unexpectedly small.
The Use of a FileMaker Pro Database in Evaluating Sign Language Notation Systems
Authors: Julie Hochgesang
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

In this paper, FileMaker Pro has been used to create a database in order to evaluate sign language notation systems used for representing hand configurations. The database cited in this paper focuses on child acquisition data, particularly the dataset of one child and one adult productions of the same American Sign Language (ASL) signs produced in a two-year span. The hand configurations in selected signs have been coded using Stokoe notation (Stokoe, Casterline {\&} Croneberg, 1965), the Hamburg Notation System or HamNoSys (Prillwitz et al, 1989), the revised Prosodic Model Handshape Coding system or PM (Eccarius {\&} Brentari, 2008) and Sign Language Phonetic Annotation or SLPA, a notation system that has grown from the Movement-Hold Model (Johnson {\&} Liddell, 2010, 2011a, 2011b, 2012). Data was pulled from ELAN transcripts, organized and notated in a FileMaker Pro database created to investigate the representativeness of each system. Representativeness refers to the ability of the notation system to represent the hand configurations in the dataset. This paper briefly describes the design of the FileMaker Pro database intended to provide both quantitative and qualitative information in order to allow the sign language researcher to examine the representativeness of sign language notation systems.
Mapping CPA Patterns onto OntoNotes Senses
Authors: Octavian Popescu, Martha Palmer, Patrick Hanks
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Sentence

In this paper we present an alignment experiment between patterns of verb use discovered by Corpus Pattern Analysis (CPA; Hanks 2004, 2008, 2012) and verb senses in OntoNotes (ON; Hovy et al. 2006, Weischedel et al. 2011). We present a probabilistic approach for mapping one resource into the other. Firstly we introduce a basic model, based on conditional probabilities, which determines for any given sentence the best CPA pattern match. On the basis of this model, we propose a joint source channel model (JSCM) that computes the probability of compatibility of semantic types between a verb phrase and a pattern, irrespective of whether the verb phrase is a norm or an exploitation. We evaluate the accuracy of the proposed mapping using cluster similarity metrics based on entropy.
Language CoLLAGE: Grammatical Description with the LinGO Grammar Matrix
Authors: Emily M. Bender
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

Language CoLLAGE is a collection of grammatical descriptions developed in the context of a grammar engineering graduate course with the LinGO Grammar Matrix. These grammatical descriptions include testsuites in well-formed interlinear glossed text (IGT) format, high-level grammatical characterizations called choices files, HPSG grammar fragments (capable of parsing and generation), and documentation. As of this writing, Language CoLLAGE includes resources for 52 typologically and areally diverse languages and this number is expected to grow over time. The resources for each language cover a similar range of core grammatical phenomena and are implemented in a uniform framework, compatible with the DELPH-IN suite of processing tools.
Applying Accessibility-Oriented Controlled Language (CL) Rules to Improve Appropriateness of Text Alternatives for Images: an Exploratory Study
Authors: Silvia Rodr{\'\i}guez V{\'a}zquez, Pierrette Bouillon, Anton Bolfing
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

At present, inappropriate text alternatives for images in the Web continue to pose web accessibility barriers for people with special needs. Although research efforts have been devoted to define how to write text equivalents for visual content in websites, existing guidelines often lack direct linguistic-oriented recommendations. Similarly, most web accessibility evaluation tools just provide users with an automated functionality to check the presence of text alternatives within the element, rather than a platform to verify their content. This paper presents an overview of the findings from an exploratory study carried out to investigate if the appropriateness level of text alternatives for images in French can be improved when applying controlled language (CL) rules. Results gathered suggest that using accessibility-oriented alt style rules can have a significant impact on text alternativesÂ’ appropriateness. Although more data would be needed to draw further conclusions about our proposal, this preliminary study already offers an interest insight into the potential use of CL checkers such as Acrolinx for language-based web accessibility evaluation.
A Multi-Dialect, Multi-Genre Corpus of Informal Written Arabic
Authors: Ryan Cotterell, Chris Callison-Burch
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Dialect Identification

This paper presents a multi-dialect, multi-genre, human annotated corpus of dialectal Arabic. We collected utterances in five Arabic dialects: Levantine, Gulf, Egyptian, Iraqi and Maghrebi. We scraped newspaper websites for user commentary and Twitter for two distinct types of dialectal content. To the best of the authors knowledge, this work is the most diverse corpus of dialectal Arabic in both the source of the content and the number of dialects. Every utterance in the corpus was human annotated on Amazons Mechanical Turk; this stands in contrast to Al-Sabbagh and Girju (2012) where only a small subset was human annotated in order to train a classifier to automatically annotate the remainder of the corpus. We provide a discussion of the methodology used for the annotation in addition to the performance of the individual workers. We extend the Arabic dialect identification task to the Iraqi and Maghrebi dialects and improve the results of Zaidan and Callison-Burch (2011a) on Levantine, Gulf and Egyptian.
Reconstructing the Semantic Landscape of Natural Language Processing
Authors: Elisa Omodei, Jean-Philippe Cointet, Thierry Poibeau
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

This paper investigates the evolution of the computational linguistics domain through a quantitative analysis of the ACL Anthology (containing around 12,000 papers published between 1985 and 2008). Our approach combines complex system methods with natural language processing techniques. We reconstruct the socio-semantic landscape of the domain by inferring a co-authorship and a semantic network from the analysis of the corpus. First, keywords are extracted using a hybrid approach mixing linguistic patterns with statistical information. Then, the semantic network is built using a co-occurrence analysis of these keywords within the corpus. Combining temporal and network analysis techniques, we are able to examine the main evolutions of the field and the more active subfields over time. Lastly we propose a model to explore the mutual influence of the social and the semantic network over time, leading to a socio-semantic co-evolutionary system.
Discovering and Visualising Stories in News
Authors: Marieke van Erp, Gleb Satyukov, Piek Vossen, Marit Nijsen
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Articles

Daily news streams often revolve around topics that span over a longer period of time such as the global financial crisis or the healthcare debate in the US. The length and depth of these stories can be such that they become difficult to track for information specialists who need to reconstruct exactly what happened for policy makers and companies. We present a framework to model stories from news: we describe the characteristics that make up interesting stories, how these translate to filters on our data and we present a first use case in which we detail the steps to visualising story lines extracted from news articles about the global automotive industry.
Supervised Within-Document Event Coreference using Information Propagation
Authors: Zhengzhong Liu, Jun Araki, Eduard Hovy, Teruko Mitamura
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: coreference-resolution, Coreference Resolution, Event Coreference Resolution

Event coreference is an important task for full text analysis. However, previous work uses a variety of approaches, sources and evaluation, making the literature confusing and the results incommensurate. We provide a description of the differences to facilitate future research. Second, we present a supervised method for event coreference resolution that uses a rich feature set and propagates information alternatively between events and their arguments, adapting appropriately for each type of argument.
VOCE Corpus: Ecologically Collected Speech Annotated with Physiological and Psychological Stress Assessments
Authors: Ana Aguiar, Mariana Kaiseler, Hugo Meinedo, Pedro Almeida, Mariana Cunha, Jorge Silva
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Emotion Recognition

Public speaking is a widely requested professional skill, and at the same time an activity that causes one of the most common adult phobias (Miller and Stone, 2009). It is also known that the study of stress under laboratory conditions, as it is most commonly done, may provide only limited ecological validity (Wilhelm and Grossman, 2010). Previously, we introduced an inter-disciplinary methodology to enable collecting a large amount of recordings under consistent conditions (Aguiar et al., 2013). This paper introduces the VOCE corpus of speech annotated with stress indicators under naturalistic public speaking (PS) settings, and makes it available at http://paginas.fe.up.pt/{\textasciitilde}voce/articles.html. The novelty of this corpus is that the recordings are carried out in objectively stressful PS situations, as recommended in (Zanstra and Johnston, 2011). The current database contains a total of 38 recordings, 13 of which contain full psychologic and physiologic annotation. We show that the collected recordings validate the assumptions of the methodology, namely that participants experience stress during the PS events. We describe the various metrics that can be used for physiologic and psychologic annotation, and we characterise the sample collected so far, providing evidence that demographics do not affect the relevant psychologic or physiologic annotation. The collection activities are on-going, and we expect to increase the number of complete recordings in the corpus to 30 by June 2014.
Language Resource Addition: Dictionary or Corpus?
Authors: Shinsuke Mori, Graham Neubig
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Active Learning, Domain Adaptation, Morphological Analysis, Part-Of-Speech Tagging, Segmentation, Sentence

In this paper, we investigate the relative effect of two strategies of language resource additions to the word segmentation problem and part-of-speech tagging problem in Japanese. The first strategy is adding entries to the dictionary and the second is adding annotated sentences to the training corpus. The experimental results showed that the annotated sentence addition to the training corpus is better than the entries addition to the dictionary. And the annotated sentence addition is efficient especially when we add new words with contexts of three real occurrences as partially annotated sentences. According to this knowledge, we executed annotation on the invention disclosure texts and observed word segmentation accuracy.
The DIRHA simulated corpus
Authors: Luca Cristoforetti, Mirco Ravanelli, Maurizio Omologo, Aless Sosi, ro, Alberto Abad, Martin Hagmueller, Petros Maragos
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Dialogue Management, Distant Speech Recognition, Speaker Identification, speech-recognition, Speech Recognition

This paper describes a multi-microphone multi-language acoustic corpus being developed under the EC project Distant-speech Interaction for Robust Home Applications (DIRHA). The corpus is composed of several sequences obtained by convolution of dry acoustic events with more than 9000 impulse responses measured in a real apartment equipped with 40 microphones. The acoustic events include in-domain sentences of different typologies uttered by native speakers in four different languages and non-speech events representing typical domestic noises. To increase the realism of the resulting corpus, background noises were recorded in the real home environment and then added to the generated sequences. The purpose of this work is to describe the simulation procedure and the data sets that were created and used to derive the corpus. The corpus contains signals of different characteristics making it suitable for various multi-microphone signal processing and distant speech recognition tasks.
The Slovak Categorized News Corpus
Authors: Daniel Hladek, Jan Stas, Jozef Juhar
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Boundary Detection, Language Modelling, Sentence

The presented corpus aims to be the first attempt to create a representative sample of the contemporary Slovak language from various domains with easy searching and automated processing. This first version of the corpus contains words and automatic morphological and named entity annotations and transcriptions of abbreviations and numerals. Integral part of the proposed paper is a word boundary and sentence boundary detection algorithm that utilizes characteristic features of the language.
High Quality Word Lists as a Resource for Multiple Purposes
Authors: Uwe Quasthoff, Dirk Goldhahn, Thomas Eckart, Erla Hallsteinsd{\'o}ttir, Sabine Fiedler
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Articles, Vocal Bursts Intensity Prediction

Since 2011 the comprehensive, electronically available sources of the Leipzig Corpora Collection have been used consistently for the compilation of high quality word lists. The underlying corpora include newspaper texts, Wikipedia articles and other randomly collected Web texts. For many of the languages featured in this collection, it is the first comprehensive compilation to use a large-scale empirical base. The word lists have been used to compile dictionaries with comparable frequency data in the Frequency Dictionaries series. This includes frequency data of up to 1,000,000 word forms presented in alphabetical order. This article provides an introductory description of the data and the methodological approach used. In addition, language-specific statistical information is provided with regard to letters, word structure and structural changes. Such high quality word lists also provide the opportunity to explore comparative linguistic topics and such monolingual issues as studies of word formation and frequency-based examinations of lexical areas for use in dictionaries or language teaching. The results presented here can provide initial suggestions for subsequent work in several areas of research.
A Quality-based Active Sample Selection Strategy for Statistical Machine Translation
Authors: Varvara Logacheva, Lucia Specia
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Active Learning, Machine Translation, Sentence, Sentiment Analysis, Translation

This paper presents a new active learning technique for machine translation based on quality estimation of automatically translated sentences. It uses an error-driven strategy, i.e., it assumes that the more errors an automatically translated sentence contains, the more informative it is for the translation system. Our approach is based on a quality estimation technique which involves a wider range of features of the source text, automatic translation, and machine translation system compared to previous work. In addition, we enhance the machine translation system training data with post-edited machine translations of the sentences selected, instead of simulating this using previously created reference translations. We found that re-training systems with additional post-edited data yields higher quality translations regardless of the selection strategy used. We relate this to the fact that post-editions tend to be closer to source sentences as compared to references, making the rule extraction process more reliable.
The Multilingual Paraphrase Database
Authors: Juri Ganitkevitch, Chris Callison-Burch
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Document Summarization, Information Retrieval, Machine Translation, Multi-Document Summarization, Natural Language Inference, Paraphrase Generation, Question Answering, Semantic Parsing

We release a massive expansion of the paraphrase database (PPDB) that now includes a collection of paraphrases in 23 different languages. The resource is derived from large volumes of bilingual parallel data. Our collection is extracted and ranked using state of the art methods. The multilingual PPDB has over a billion paraphrase pairs in total, covering the following languages: Arabic, Bulgarian, Chinese, Czech, Dutch, Estonian, Finnish, French, German, Greek, Hungarian, Italian, Latvian, Lithuanian, Polish, Portugese, Romanian, Russian, Slovak, Slovenian, and Swedish.
The Development of Dutch and Afrikaans Language Resources for Compound Boundary Analysis.
Authors: Menno van Zaanen, Gerhard Van Huyssteen, Suzanne Aussems, Chris Emmery, Roald Eiselen
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Boundary Detection

In most languages, new words can be created through the process of compounding, which combines two or more words into a new lexical unit. Whereas in languages such as English the components that make up a compound are separated by a space, in languages such as Finnish, German, Afrikaans and Dutch these components are concatenated into one word. Compounding is very productive and leads to practical problems in developing machine translators and spelling checkers, as newly formed compounds cannot be found in existing lexicons. The Automatic Compound Processing (AuCoPro) project deals with the analysis of compounds in two closely-related languages, Afrikaans and Dutch. In this paper, we present the development and evaluation of two datasets, one for each language, that contain compound words with annotated compound boundaries. Such datasets can be used to train classifiers to identify the compound components in novel compounds. We describe the process of annotation and provide an overview of the annotation guidelines as well as global properties of the datasets. The inter-rater agreements between the annotators are considered highly reliable. Furthermore, we show the usability of these datasets by building an initial automatic compound boundary detection system, which assigns compound boundaries with approximately 90{\%} accuracy.
Language Processing Infrastructure in the XLike Project
Authors: Llu{\'\i}s Padr{\'o}, {\v{Z}}eljko Agi{\'c}, Xavier Carreras, Blaz Fortuna, Esteban Garc{\'\i}a-Cuesta, Zhixing Li, Tadej {\v{S}}tajner, Marko Tadi{\'c}
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Dependency Parsing, Semantic Role Labeling, Word Sense Disambiguation

This paper presents the linguistic analysis tools and its infrastructure developed within the XLike project. The main goal of the implemented tools is to provide a set of functionalities for supporting some of the main objectives of XLike, such as enabling cross-lingual services for publishers, media monitoring or developing new business intelligence applications. The services cover seven major and minor languages: English, German, Spanish, Chinese, Catalan, Slovenian, and Croatian. These analyzers are provided as web services following a lightweight SOA architecture approach, and they are publically callable and are catalogued in META-SHARE.