pwc-archive/papers-with-abstracts huggingface dataset:
FLELex: a graded lexical resource for French foreign learners
Authors: Thomas Fran{\c{c}}ois, N{\`u}ria Gala, Patrick Watrin, C{\'e}drick Fairon
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Acquisition, Optical Character Recognition (OCR), Text Simplification

In this paper we present FLELex, the first graded lexicon for French as a foreign language (FFL) that reports word frequencies by difficulty level (according to the CEFR scale). It has been obtained from a tagged corpus of 777,000 words from available textbooks and simplified readers intended for FFL learners. Our goal is to freely provide this resource to the community to be used for a variety of purposes going from the assessment of the lexical difficulty of a text, to the selection of simpler words within text simplification systems, and also as a dictionary in assistive tools for writing.
Building Domain Specific Bilingual Dictionaries
Authors: Lucas Hilgert, Lucelene Lopes, Artur Freitas, Renata Vieira, Denise Hogetop, Aline Vanin
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Morphological Analysis

This paper proposes a method to build bilingual dictionaries for specific domains defined by a parallel corpora. The proposed method is based on an original method that is not domain specific. Both the original and the proposed methods are constructed with previously available natural language processing tools. Therefore, this paper contribution resides in the choice and parametrization of the chosen tools. To illustrate the proposed method benefits we conduct an experiment over technical manuals in English and Portuguese. The results of our proposed method were analyzed by human specialists and our results indicates significant increases in precision for unigrams and muli-grams. Numerically, the precision increase is as big as 15{\%} according to our evaluation.
A Corpus of Machine Translation Errors Extracted from Translation Students Exercises
Authors: Guillaume Wisniewski, Natalie K{\"u}bler, Fran{\c{c}}ois Yvon
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Translation

In this paper, we present a freely available corpus of automatic translations accompanied with post-edited versions, annotated with labels identifying the different kinds of errors made by the MT system. These data have been extracted from translation students exercises that have been corrected by a senior professor. This corpus can be useful for training quality estimation tools and for analyzing the types of errors made MT system.
Finding Romanized Arabic Dialect in Code-Mixed Tweets
Authors: Clare Voss, Stephen Tratz, Jamal Laoudi, Douglas Briesch
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Dialect Identification, Language Identification

Recent computational work on Arabic dialect identification has focused primarily on building and annotating corpora written in Arabic script. Arabic dialects however also appear written in Roman script, especially in social media. This paper describes our recent work developing tweet corpora and a token-level classifier that identifies a Romanized Arabic dialect and distinguishes it from French and English in tweets. We focus on Moroccan Darija, one of several spoken vernaculars in the family of Maghrebi Arabic dialects. Even given noisy, code-mixed tweets,the classifier achieved token-level recall of 93.2{\%} on Romanized Arabic dialect, 83.2{\%} on English, and 90.1{\%} on French. The classifier, now integrated into our tweet conversation annotation tool (Tratz et al. 2013), has semi-automated the construction of a Romanized Arabic-dialect lexicon. Two datasets, a full list of Moroccan Darija surface token forms and a table of lexical entries derived from this list with spelling variants, as extracted from our tweet corpus collection, will be made available in the LRE MAP.
Co-Training for Classification of Live or Studio Music Recordings
Authors: Nicolas Auguin, Pascale Fung
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Classification, General Classification, Information Retrieval, Music Information Retrieval

The fast-spreading development of online streaming services has enabled people from all over the world to listen to music. However, it is not always straightforward for a given user to find the {``}right{''} song version he or she is looking for. As streaming services may be affected by the potential dissatisfaction among their customers, the quality of songs and the presence of tags (or labels) associated with songs returned to the users are very important. Thus, the need for precise and reliable metadata becomes paramount. In this work, we are particularly interested in distinguishing between live and studio versions of songs. Specifically, we tackle the problem in the case where very little-annotated training data are available, and demonstrate how an original co-training algorithm in a semi-supervised setting can alleviate the problem of data scarcity to successfully discriminate between live and studio music recordings.
\#mygoal: Finding Motivations on Twitter
Authors: Marc Tomlinson, David Bracewell, Wayne Krug, David Hinote
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Modeling, Language Modelling, Time Series Analysis

Our everyday language reflects our psychological and cognitive state and effects the states of other individuals. In this contribution we look at the intersection between motivational state and language. We create a set of hashtags, which are annotated for the degree to which they are used by individuals to mark-up language that is indicative of a collection of factors that interact with an individual{'}s motivational state. We look for tags that reflect a goal mention, reward, or a perception of control. Finally, we present results for a language-model based classifier which is able to predict the presence of one of these factors in a tweet with between 69{\textbackslash}{\%} and 80{\textbackslash}{\%} accuracy on a balanced testing set. Our approach suggests that hashtags can be used to understand, not just the language of topics, but the deeper psychological and social meaning of a tweet.
The RATS Collection: Supporting HLT Research with Degraded Audio Data
Authors: David Graff, Kevin Walker, Stephanie Strassel, Xiaoyi Ma, Karen Jones, Ann Sawyer
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Action Detection, Activity Detection, Keyword Spotting, Language Identification, Speaker Identification

The DARPA RATS program was established to foster development of language technology systems that can perform well on speaker-to-speaker communications over radio channels that evince a wide range in the type and extent of signal variability and acoustic degradation. Creating suitable corpora to address this need poses an equally wide range of challenges for the collection, annotation and quality assessment of relevant data. This paper describes the LDCÂ’s multi-year effort to build the RATS data collection, summarizes the content and properties of the resulting corpora, and discusses the novel problems and approaches involved in ensuring that the data would satisfy its intended use, to provide speech recordings and annotations for training and evaluating HLT systems that perform 4 specific tasks on difficult radio channels: Speech Activity Detection (SAD), Language Identification (LID), Speaker Identification (SID) and Keyword Spotting (KWS).
Modeling Language Proficiency Using Implicit Feedback
Authors: Chris Hokamp, Rada Mihalcea, Peter Schuelke
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Reading Comprehension, Text Simplification

We describe the results of several experiments with interactive interfaces for native and L2 English students, designed to collect implicit feedback from students as they complete a reading activity. In this study, implicit means that all data is obtained without asking the user for feedback. To test the value of implicit feedback for assessing student proficiency, we collect features of user behavior and interaction, which are then used to train classification models. Based upon the feedback collected during these experiments, a studentÂ’s performance on a quiz and proficiency relative to other students can be accurately predicted, which is a step on the path to our goal of providing automatic feedback and unintrusive evaluation in interactive learning environments.
Event Extraction Using Distant Supervision
Authors: Kevin Reschke, Martin Jankowiak, Mihai Surdeanu, Christopher Manning, Daniel Jurafsky
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Event Extraction, Knowledge Base Population, Named Entity Recognition (NER), Slot Filling

Distant supervision is a successful paradigm that gathers training data for information extraction systems by automatically aligning vast databases of facts with text. Previous work has demonstrated its usefulness for the extraction of binary relations such as a person{'}s employer or a film{'}s director. Here, we extend the distant supervision approach to template-based event extraction, focusing on the extraction of passenger counts, aircraft types, and other facts concerning airplane crash events. We present a new publicly available dataset and event extraction task in the plane crash domain based on Wikipedia infoboxes and newswire text. Using this dataset, we conduct a preliminary evaluation of four distantly supervised extraction models which assign named entity mentions in text to entries in the event template. Our results indicate that joint inference over sequences of candidate entity mentions is beneficial. Furthermore, we demonstrate that the Searn algorithm outperforms a linear-chain CRF and strong baselines with local inference.
First Insight into Quality-Adaptive Dialogue
Authors: Stefan Ultes, H{\"u}seyin Dikme, Wolfgang Minker
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Dialogue Management, Management, Spoken Dialogue Systems

While Spoken Dialogue Systems have gained in importance in recent years, most systems applied in the real world are still static and error-prone. To overcome this, the user is put into the focus of dialogue management. Hence, an approach for adapting the course of the dialogue to Interaction Quality, an objective variant of user satisfaction, is presented in this work. In general, rendering the dialogue adaptive to user satisfaction enables the dialogue system to improve the course of the dialogue and to handle problematic situations better. In this contribution, we present a pilot study of quality-adaptive dialogue. By selecting the confirmation strategy based on the current IQ value, the course of the dialogue is adapted in order to improve the overall user experience. In a user experiment comparing three different confirmation strategies in a train booking domain, the adaptive strategy performs successful and is among the two best rated strategies based on the overall user experience.
TLAXCALA: a multilingual corpus of independent news
Authors: Antonio Toral
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Identification, Machine Translation, Sentence

We acquire corpora from the domain of independent news from the Tlaxcala website. We build monolingual corpora for 15 languages and parallel corpora for all the combinations of those 15 languages. These corpora include languages for which only very limited such resources exist (e.g. Tamazight). We present the acquisition process in detail and we also present detailed statistics of the produced corpora, concerning mainly quantitative dimensions such as the size of the corpora per language (for the monolingual corpora) and per language pair (for the parallel corpora). To the best of our knowledge, these are the first publicly available parallel and monolingual corpora for the domain of independent news. We also create models for unsupervised sentence splitting for all the languages of the study.
Benchmarking of English-Hindi parallel corpora
Authors: Jayendra Rakesh Yeka, Prasanth Kolachina, Dipti Misra Sharma
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Benchmarking, Machine Translation, Translation

In this paper we present several parallel corpora for English{\^a}Hindi and talk about their natures and domains. We also discuss briefly a few previous attempts in MT for translation from English to Hindi. The lack of uniformly annotated data makes it difficult to compare these attempts and precisely analyze their strengths and shortcomings. With this in mind, we propose a standard pipeline to provide uniform linguistic annotations to these resources using state-of-art NLP technologies. We conclude the paper by presenting evaluation scores of different statistical MT systems on the corpora detailed in this paper for English{\^a}Hindi and present the proposed plans for future work. We hope that both these annotated parallel corpora resources and MT systems will serve as benchmarks for future approaches to MT in English{\^a}Hindi. This was and remains the main motivation for the attempts detailed in this paper.
A New Framework for Sign Language Recognition based on 3D Handshape Identification and Linguistic Modeling
Authors: Mark Dilsizian, Polina Yanovich, Shu Wang, Carol Neidle, Dimitris Metaxas
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: 3D Reconstruction, Sign Language Recognition, Structured Prediction

Current approaches to sign recognition by computer generally have at least some of the following limitations: they rely on laboratory conditions for sign production, are limited to a small vocabulary, rely on 2D modeling (and therefore cannot deal with occlusions and off-plane rotations), and/or achieve limited success. Here we propose a new framework that (1) provides a new tracking method less dependent than others on laboratory conditions and able to deal with variations in background and skin regions (such as the face, forearms, or other hands); (2) allows for identification of 3D hand configurations that are linguistically important in American Sign Language (ASL); and (3) incorporates statistical information reflecting linguistic constraints in sign production. For purposes of large-scale computer-based sign language recognition from video, the ability to distinguish hand configurations accurately is critical. Our current method estimates the 3D hand configuration to distinguish among 77 hand configurations linguistically relevant for ASL. Constraining the problem in this way makes recognition of 3D hand configuration more tractable and provides the information specifically needed for sign recognition. Further improvements are obtained by incorporation of statistical information about linguistic dependencies among handshapes within a sign derived from an annotated corpus of almost 10,000 sign tokens.
Evaluating Improvised Hip Hop Lyrics - Challenges and Observations
Authors: Karteek Addanki, Dekai Wu
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation

We investigate novel challenges involved in comparing model performance on the task of improvising responses to hip hop lyrics and discuss observations regarding inter-evaluator agreement on judging improvisation quality. We believe the analysis serves as a first step toward designing robust evaluation strategies for improvisation tasks, a relatively neglected area to date. Unlike most natural language processing tasks, improvisation tasks suffer from a high degree of subjectivity, making it difficult to design discriminative evaluation strategies to drive model development. We propose a simple strategy with fluency and rhyming as the criteria for evaluating the quality of generated responses, which we apply to both our inversion transduction grammar based FREESTYLE hip hop challenge-response improvisation system, as well as various contrastive systems. We report inter-evaluator agreement for both English and French hip hop lyrics, and analyze correlation with challenge length. We also compare the extent of agreement in evaluating fluency with that of rhyming, and quantify the difference in agreement with and without precise definitions of evaluation criteria.
Votter Corpus: A Corpus of Social Polling Language
Authors: Nathan Green, Septina Dian Larasati
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Opinion Mining, Sentiment Analysis

The Votter Corpus is a new annotated corpus of social polling questions and answers. The Votter Corpus is novel in its use of the mobile application format and novel in its coverage of specific demographics. With over 26,000 polls and close to 1 millions votes, the Votter Corpus covers everyday question and answer language, primarily for users who are female and between the ages of 13-24. The corpus is annotated by topic and by popularity of particular answers. The corpus contains many unique characteristics such as emoticons, common mobile misspellings, and images associated with many of the questions. The corpus is a collection of questions and answers from The Votter App on the Android operating system. Data is created solely on this mobile platform which differs from most social media corpora. The Votter Corpus is being made available online in XML format for research and non-commercial use. The Votter android app can be downloaded for free in most android app stores.
SinoCoreferencer: An End-to-End Chinese Event Coreference Resolver
Authors: Chen Chen, Vincent Ng
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: coreference-resolution, Coreference Resolution, Entity Extraction using GAN, Entity Typing, Event Coreference Resolution, Named Entity Recognition (NER), Part-Of-Speech Tagging

Compared to entity coreference resolution, there is a relatively small amount of work on event coreference resolution. Much work on event coreference was done for English. In fact, to our knowledge, there are no publicly available results on Chinese event coreference resolution. This paper describes the design, implementation, and evaluation of SinoCoreferencer, an end-to-end state-of-the-art ACE-style Chinese event coreference system. We have made SinoCoreferencer publicly available, in hope to facilitate the development of high-level Chinese natural language applications that can potentially benefit from event coreference information.
Developing an Egyptian Arabic Treebank: Impact of Dialectal Morphology on Annotation and Tool Development
Authors: Mohamed Maamouri, Ann Bies, Seth Kulick, Michael Ciul, Nizar Habash, Esk, Ramy er
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

This paper describes the parallel development of an Egyptian Arabic Treebank and a morphological analyzer for Egyptian Arabic (CALIMA). By the very nature of Egyptian Arabic, the data collected is informal, for example Discussion Forum text, which we use for the treebank discussed here. In addition, Egyptian Arabic, like other Arabic dialects, is sufficiently different from Modern Standard Arabic (MSA) that tools and techniques developed for MSA cannot be simply transferred over to work on Egyptian Arabic work. In particular, a morphological analyzer for Egyptian Arabic is needed to mediate between the written text and the segmented, vocalized form used for the syntactic trees. This led to the necessity of a feedback loop between the treebank team and the analyzer team, as improvements in each area were fed to the other. Therefore, by necessity, there needed to be close cooperation between the annotation team and the tool development team, which was to their mutual benefit. Collaboration on this type of challenge, where tools and resources are limited, proved to be remarkably synergistic and opens the way to further fruitful work on Arabic dialects.
A German Twitter Snapshot
Authors: Tatjana Scheffler
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

We present a new corpus of German tweets. Due to the relatively small number of German messages on Twitter, it is possible to collect a virtually complete snapshot of German twitter messages over a period of time. In this paper, we present our collection method which produced a 24 million tweet corpus, representing a large majority of all German tweets sent in April, 2013. Further, we analyze this representative data set and characterize the German twitterverse. While German Twitter data is similar to other Twitter data in terms of its temporal distribution, German Twitter users are much more reluctant to share geolocation information with their tweets. Finally, the corpus collection method allows for a study of discourse phenomena in the Twitter data, structured into discussion threads.
A Comparative Evaluation Methodology for NLG in Interactive Systems
Authors: Helen Hastie, Anja Belz
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Text Generation

Interactive systems have become an increasingly important type of application for deployment of NLG technology over recent years. At present, we do not yet have commonly agreed terminology or methodology for evaluating NLG within interactive systems. In this paper, we take steps towards addressing this gap by presenting a set of principles for designing new evaluations in our comparative evaluation methodology. We start with presenting a categorisation framework, giving an overview of different categories of evaluation measures, in order to provide standard terminology for categorising existing and new evaluation techniques. Background on existing evaluation methodologies for NLG and interactive systems is presented. The comparative evaluation methodology is presented. Finally, a methodology for comparative evaluation of NLG components embedded within interactive systems is presented in terms of the comparative evaluation methodology, using a specific task for illustrative purposes.
Relating Frames and Constructions in Japanese FrameNet
Authors: Kyoko Ohara
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

Relations between frames and constructions must be made explicit in FrameNet-style linguistic resources such as Berkeley FrameNet (Fillmore {\&} Baker, 2010, Fillmore, Lee-Goldman {\&} Rhomieux, 2012), Japanese FrameNet (Ohara, 2013), and Swedish Constructicon (Lyngfelt et al., 2013). On the basis of analyses of Japanese constructions for the purpose of building a constructicon in the Japanese FrameNet project, this paper argues that constructions can be classified based on whether they evoke frames or not. By recognizing such a distinction among constructions, it becomes possible for FrameNet-style linguistic resources to have a proper division of labor between frame annotations and construction annotations. In addition to the three kinds of meaningless constructions which have been proposed already, this paper suggests there may be yet another subtype of constructions without meanings. Furthermore, the present paper adds support to the claim that there may be constructions without meanings (Fillmore, Lee-Goldman {\&} Rhomieux, 2012) in a current debate concerning whether all constructions should be seen as meaning-bearing (Goldberg, 2006: 166-182).
TMO --- The Federated Ontology of the TrendMiner Project
Authors: Hans-Ulrich Krieger, Thierry Declerck
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

This paper describes work carried out in the European project TrendMiner which partly deals with the extraction and representation of real time information from dynamic data streams. The focus of this paper lies on the construction of an integrated ontology, TMO, the TrendMiner Ontology, that has been assembled from several independent multilingual taxonomies and ontologies which are brought together by an interface specification, expressed in OWL. Within TrendMiner, TMO serves as a common language that helps to interlink data, delivered from both symbolic and statistical components of the TrendMiner system. Very often, the extracted data is supplied as quintuples, RDF triples that are extended by two further temporal arguments, expressing the temporal extent in which an atemporal statement is true. In this paper, we will also sneak a peek on the temporal entailment rules and queries that are built into the semantic repository hosting the data and which can be used to derive useful new information.