pwc-archive/papers-with-abstracts huggingface dataset:
ARRAU: Linguistically-Motivated Annotation of Anaphoric Descriptions
Authors: Olga Uryupina, Ron artstein, Antonella Bristot, Federica Cavicchio, Kepa Rodriguez, Massimo Poesio
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper presents a second release of the ARRAU dataset: a multi-domain corpus with thorough linguistically motivated annotation of anaphora and related phenomena. Building upon the first release almost a decade ago, a considerable effort had been invested in improving the data both quantitatively and qualitatively. Thus, we have doubled the corpus size, expanded the selection of covered phenomena to include referentiality and genericity and designed and implemented a methodology for enforcing the consistency of the manual annotation. We believe that the new release of ARRAU provides a valuable material for ongoing research in complex cases of coreference as well as for a variety of related tasks. The corpus is publicly available through LDC.
An Annotated Corpus and Method for Analysis of Ad-Hoc Structures Embedded in Text
Authors: Eric Yeh, John Niekrasz, Dayne Freitag, Richard Rohwer
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Table Extraction, Text Segmentation

We describe a method for identifying and performing functional analysis of structured regions that are embedded in natural language documents, such as tables or key-value lists. Such regions often encode information according to ad hoc schemas and avail themselves of visual cues in place of natural language grammar, presenting problems for standard information extraction algorithms. Unlike previous work in table extraction, which assumes a relatively noiseless two-dimensional layout, our aim is to accommodate a wide variety of naturally occurring structure types. Our approach has three main parts. First, we collect and annotate a a diverse sample of {``}naturally{''} occurring structures from several sources. Second, we use probabilistic text segmentation techniques, featurized by skip bigrams over spatial and token category cues, to automatically identify contiguous regions of structured text that share a common schema. Finally, we identify the records and fields within each structured region using a combination of distributional similarity and sequence alignment methods, guided by minimal supervision in the form of a single annotated record. We evaluate the last two components individually, and conclude with a discussion of further work.
Learning Thesaurus Relations from Distributional Features
Authors: Rosa Tsegaye Aga, Christian Wartena, Lucas Drumond, Lars Schmidt-Thieme
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Document Classification, Relation

In distributional semantics words are represented by aggregated context features. The similarity of words can be computed by comparing their feature vectors. Thus, we can predict whether two words are synonymous or similar with respect to some other semantic relation. We will show on six different datasets of pairs of similar and non-similar words that a supervised learning algorithm on feature vectors representing pairs of words outperforms cosine similarity between vectors representing single words. We compared different methods to construct a feature vector representing a pair of words. We show that simple methods like pairwise addition or multiplication give better results than a recently proposed method that combines different types of features. The semantic relation we consider is relatedness of terms in thesauri for intellectual document classification. Thus our findings can directly be applied for the maintenance and extension of such thesauri. To the best of our knowledge this relation was not considered before in the field of distributional semantics.
Factuality Annotation and Learning in Spanish Texts
Authors: Dina Wonsever, Aiala Ros{\'a}, Marisa Malcuori
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

We present a proposal for the annotation of factuality of event mentions in Spanish texts and a free available annotated corpus. Our factuality model aims to capture a pragmatic notion of factuality, trying to reflect a casual reader judgements about the realis / irrealis status of mentioned events. Also, some learning experiments (SVM and CRF) have been held, showing encouraging results.
NNBlocks: A Deep Learning Framework for Computational Linguistics Neural Network Models
Authors: Frederico Tommasi Caroli, Andr{\'e} Freitas, Jo{\~a}o Carlos Pereira da Silva, H, Siegfried schuh
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

Lately, with the success of Deep Learning techniques in some computational linguistics tasks, many researchers want to explore new models for their linguistics applications. These models tend to be very different from what standard Neural Networks look like, limiting the possibility to use standard Neural Networks frameworks. This work presents NNBlocks, a new framework written in Python to build and train Neural Networks that are not constrained by a specific kind of architecture, making it possible to use it in computational linguistics.
Automatic identification of Mild Cognitive Impairment through the analysis of Italian spontaneous speech productions
Authors: Daniela Beltrami, Laura Calz{\`a}, Gloria Gagliardi, Enrico Ghidoni, Norina Marcello, Rema Rossini Favretti, Fabio Tamburini
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper presents some preliminary results of the OPLON project. It aimed at identifying early linguistic symptoms of cognitive decline in the elderly. This pilot study was conducted on a corpus composed of spontaneous speech sample collected from 39 subjects, who underwent a neuropsychological screening for visuo-spatial abilities, memory, language, executive functions and attention. A rich set of linguistic features was extracted from the digitalised utterances (at phonetic, suprasegmental, lexical, morphological and syntactic levels) and the statistical significance in pinpointing the pathological process was measured. Our results show remarkable trends for what concerns both the linguistic traits selection and the automatic classifiers building.
On the Use of a Serious Game for Recording a Speech Corpus of People with Intellectual Disabilities
Authors: Mario Corrales-Astorgano, David Escudero-Mancebo, Yurena Guti{\'e}rrez-Gonz{\'a}lez, Valle Flores-Lucas, C{\'e}sar Gonz{\'a}lez-Ferreras, Valent{\'\i}n Carde{\~n}oso-Payo
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: General Classification

This paper describes the recording of a speech corpus focused on prosody of people with intellectual disabilities. To do this, a video game is used with the aim of improving the user{'}s motivation. Moreover, the player{'}s profiles and the sentences recorded during the game sessions are described. With the purpose of identifying the main prosodic troubles of people with intellectual disabilities, some prosodic features are extracted from recordings, like fundamental frequency, energy and pauses. After that, a comparison is made between the recordings of people with intellectual disabilities and people without intellectual disabilities. This comparison shows that pauses are the best discriminative feature between these groups. To check this, a study has been done using machine learning techniques, with a classification rate superior to 80{\%}.
Building Language Resources for Exploring Autism Spectrum Disorders
Authors: Julia Parish-Morris, Christopher Cieri, Mark Liberman, Leila Bateman, Emily Ferguson, Robert T. Schultz
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Diagnostic

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that would benefit from low-cost and reliable improvements to screening and diagnosis. Human language technologies (HLTs) provide one possible route to automating a series of subjective decisions that currently inform {``}Gold Standard{''} diagnosis based on clinical judgment. In this paper, we describe a new resource to support this goal, comprised of 100 20-minute semi-structured English language samples labeled with child age, sex, IQ, autism symptom severity, and diagnostic classification. We assess the feasibility of digitizing and processing sensitive clinical samples for data sharing, and identify areas of difficulty. Using the methods described here, we propose to join forces with researchers and clinicians throughout the world to establish an international repository of annotated language samples from individuals with ASD and related disorders. This project has the potential to improve the lives of individuals with ASD and their families by identifying linguistic features that could improve remote screening, inform personalized intervention, and promote advancements in clinically-oriented HLTs.
Vocal Pathologies Detection and Mispronounced Phonemes Identification: Case of Arabic Continuous Speech
Authors: Naim Terbeh, Mounir Zrigui
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: General Classification

We propose in this work a novel acoustic phonetic study for Arabic people suffering from language disabilities and non-native learners of Arabic language to classify Arabic continuous speech to pathological or healthy and to identify phonemes that pose pronunciation problems (case of pathological speeches). The main idea can be summarized in comparing between the phonetic model reference to Arabic spoken language and that proper to concerned speaker. For this task, we use techniques of automatic speech processing like forced alignment and artificial neural network (ANN) (Basheer, 2000). Based on a test corpus containing 100 speech sequences, recorded by different speakers (healthy/pathological speeches and native/foreign speakers), we attain 97{\%} as classification rate. Algorithms used in identifying phonemes that pose pronunciation problems show high efficiency: we attain an identification rate of 100{\%}.
Wikipedia Titles As Noun Tag Predictors
Authors: Armin Hoenen
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Machine Translation, Part-Of-Speech Tagging, Sentiment Analysis, TAG, Translation, Word Sense Disambiguation

In this paper, we investigate a covert labeling cue, namely the probability that a title (by example of the Wikipedia titles) is a noun. If this probability is very large, any list such as or comparable to the Wikipedia titles can be used as a reliable word-class (or part-of-speech tag) predictor or noun lexicon. This may be especially useful in the case of Low Resource Languages (LRL) where labeled data is lacking and putatively for Natural Language Processing (NLP) tasks such as Word Sense Disambiguation, Sentiment Analysis and Machine Translation. Profitting from the ease of digital publication on the web as opposed to print, LRL speaker communities produce resources such as Wikipedia and Wiktionary, which can be used for an assessment. We provide statistical evidence for a strong noun bias for the Wikipedia titles from 2 corpora (English, Persian) and a dictionary (Japanese) and for a typologically balanced set of 17 languages including LRLs. Additionally, we conduct a small experiment on predicting noun tags for out-of-vocabulary items in part-of-speech tagging for English.
Japanese Word―Color Associations with and without Contexts
Authors: Jun Harashima
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

Although some words carry strong associations with specific colors (e.g., the word danger is associated with the color red), few studies have investigated these relationships. This may be due to the relative rarity of databases that contain large quantities of such information. Additionally, these resources are often limited to particular languages, such as English. Moreover, the existing resources often do not consider the possible contexts of words in assessing the associations between a word and a color. As a result, the influence of context on word―color associations is not fully understood. In this study, we constructed a novel language resource for word―color associations. The resource has two characteristics: First, our resource is the first to include Japanese word―color associations, which were collected via crowdsourcing. Second, the word―color associations in the resource are linked to contexts. We show that word―color associations depend on language and that associations with certain colors are affected by context information.
The VU Sound Corpus: Adding More Fine-grained Annotations to the Freesound Database
Authors: Emiel van Miltenburg, Benjamin Timmermans, Lora Aroyo
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper presents a collection of annotations (tags or keywords) for a set of 2,133 environmental sounds taken from the Freesound database (www.freesound.org). The annotations are acquired through an open-ended crowd-labeling task, in which participants were asked to provide keywords for each of three sounds. The main goal of this study is to find out (i) whether it is feasible to collect keywords for a large collection of sounds through crowdsourcing, and (ii) how people talk about sounds, and what information they can infer from hearing a sound in isolation. Our main finding is that it is not only feasible to perform crowd-labeling for a large collection of sounds, it is also very useful to highlight different aspects of the sounds that authors may fail to mention. Our data is freely available, and can be used to ground semantic models, improve search in audio databases, and to study the language of sound.
Crowdsourcing a Large Dataset of Domain-Specific Context-Sensitive Semantic Verb Relations
Authors: Maria Sukhareva, Judith Eckle-Kohler, Ivan Habernal, Iryna Gurevych
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

We present a new large dataset of 12403 context-sensitive verb relations manually annotated via crowdsourcing. These relations capture fine-grained semantic information between verb-centric propositions, such as temporal or entailment relations. We propose a novel semantic verb relation scheme and design a multi-step annotation approach for scaling-up the annotations using crowdsourcing. We employ several quality measures and report on agreement scores. The resulting dataset is available under a permissive CreativeCommons license at www.ukp.tu-darmstadt.de/data/verb-relations/. It represents a valuable resource for various applications, such as automatic information consolidation or automatic summarization.
Acquiring Opposition Relations among Italian Verb Senses using Crowdsourcing
Authors: Anna Feltracco, Simone Magnolini, Elisabetta Jezek, Bernardo Magnini
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Sentence

We describe an experiment for the acquisition of opposition relations among Italian verb senses, based on a crowdsourcing methodology. The goal of the experiment is to discuss whether the types of opposition we distinguish (i.e. complementarity, antonymy, converseness and reversiveness) are actually perceived by the crowd. In particular, we collect data for Italian by using the crowdsourcing platform CrowdFlower. We ask annotators to judge the type of opposition existing among pairs of sentences -previously judged as opposite- that differ only for a verb: the verb in the first sentence is opposite of the verb in second sentence. Data corroborate the hypothesis that some opposition relations exclude each other, while others interact, being recognized as compatible by the contributors.
Crowdsourcing a Multi-lingual Speech Corpus: Recording, Transcription and Annotation of the CrowdIS Corpora
Authors: Andrew Caines, Christian Bentz, Calbert Graham, Tim Polzehl, Paula Buttery
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Sentence, valid

We announce the release of the CROWDED CORPUS: a pair of speech corpora collected via crowdsourcing, containing a native speaker corpus of English (CROWDED{\_}ENGLISH), and a corpus of German/English bilinguals (CROWDED{\_}BILINGUAL). Release 1 of the CROWDED CORPUS contains 1000 recordings amounting to 33,400 tokens collected from 80 speakers and is freely available to other researchers. We recruited participants via the Crowdee application for Android. Recruits were prompted to respond to business-topic questions of the type found in language learning oral tests. We then used the CrowdFlower web application to pass these recordings to crowdworkers for transcription and annotation of errors and sentence boundaries. Finally, the sentences were tagged and parsed using standard natural language processing tools. We propose that crowdsourcing is a valid and economical method for corpus collection, and discuss the advantages and disadvantages of this approach.
The REAL Corpus: A Crowd-Sourced Corpus of Human Generated and Evaluated Spatial References to Real-World Urban Scenes
Authors: Phil Bartie, William Mackaness, Dimitra Gkatzia, Verena Rieser
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

Our interest is in people{'}s capacity to efficiently and effectively describe geographic objects in urban scenes. The broader ambition is to develop spatial models capable of equivalent functionality able to construct such referring expressions. To that end we present a newly crowd-sourced data set of natural language references to objects anchored in complex urban scenes (In short: The REAL Corpus â€• Referring Expressions Anchored Language). The REAL corpus contains a collection of images of real-world urban scenes together with verbal descriptions of target objects generated by humans, paired with data on how successful other people were able to identify the same object based on these descriptions. In total, the corpus contains 32 images with on average 27 descriptions per image and 3 verifications for each description. In addition, the corpus is annotated with a variety of linguistically motivated features. The paper highlights issues posed by collecting data using crowd-sourcing with an unrestricted input format, as well as using real-world urban scenes.
Introducing the Weighted Trustability Evaluator for Crowdsourcing Exemplified by Speaker Likability Classification
Authors: Simone Hantke, Erik Marchi, Bj{\"o}rn Schuller
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: General Classification

Crowdsourcing is an arising collaborative approach applicable among many other applications to the area of language and speech processing. In fact, the use of crowdsourcing was already applied in the field of speech processing with promising results. However, only few studies investigated the use of crowdsourcing in computational paralinguistics. In this contribution, we propose a novel evaluator for crowdsourced-based ratings termed Weighted Trustability Evaluator (WTE) which is computed from the rater-dependent consistency over the test questions. We further investigate the reliability of crowdsourced annotations as compared to the ones obtained with traditional labelling procedures, such as constrained listening experiments in laboratories or in controlled environments. This comparison includes an in-depth analysis of obtainable classification performances. The experiments were conducted on the Speaker Likability Database (SLD) already used in the INTERSPEECH Challenge 2012, and the results lend further weight to the assumption that crowdsourcing can be applied as a reliable annotation source for computational paralinguistics given a sufficient number of raters and suited measurements of their reliability.
Comparison of Emotional Understanding in Modality-Controlled Environments using Multimodal Online Emotional Communication Corpus
Authors: Yoshiko Arimoto, Kazuo Okanoya
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

In online computer-mediated communication, speakers were considered to have experienced difficulties in catching their partner{'}s emotions and in conveying their own emotions. To explain why online emotional communication is so difficult and to investigate how this problem should be solved, multimodal online emotional communication corpus was constructed by recording approximately 100 speakers{'} emotional expressions and reactions in a modality-controlled environment. Speakers communicated over the Internet using video chat, voice chat or text chat; their face-to-face conversations were used for comparison purposes. The corpora incorporated emotional labels by evaluating the speaker{'}s dynamic emotional states and the measurements of the speaker{'}s facial expression, vocal expression and autonomic nervous system activity. For the initial study of this project, which used a large-scale emotional communication corpus, the accuracy of online emotional understanding was assessed to demonstrate the emotional labels evaluated by the speakers and to summarize the speaker{'}s answers on the questionnaire regarding the difference between an online chat and face-to-face conversations in which they actually participated. The results revealed that speakers have difficulty communicating their emotions in online communication environments, regardless of the type of communication modality and that inaccurate emotional understanding occurs more frequently in online computer-mediated communication than in face-to-face communication.
AVAB-DBS: an Audio-Visual Affect Bursts Database for Synthesis
Authors: Kevin El Haddad, H{\"u}seyin {\c{C}}akmak, St{\'e}phane Dupont, Thierry Dutoit
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

It has been shown that adding expressivity and emotional expressions to an agent{'}s communication systems would improve the interaction quality between this agent and a human user. In this paper we present a multimodal database of affect bursts, which are very short non-verbal expressions with facial, vocal, and gestural components that are highly synchronized and triggered by an identifiable event. This database contains motion capture and audio data of affect bursts representing disgust, startle and surprise recorded at three different levels of arousal each. This database is to be used for synthesis purposes in order to generate affect bursts of these emotions on a continuous arousal level scale.
Construction of Japanese Audio-Visual Emotion Database and Its Application in Emotion Recognition
Authors: Nurul Lubis, R Gomez, y, Sakriani Sakti, Keisuke Nakamura, Koichiro Yoshino, Satoshi Nakamura, Kazuhiro Nakadai
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Emotion Recognition

Emotional aspects play a vital role in making human communication a rich and dynamic experience. As we introduce more automated system in our daily lives, it becomes increasingly important to incorporate emotion to provide as natural an interaction as possible. To achieve said incorporation, rich sets of labeled emotional data is prerequisite. However, in Japanese, existing emotion database is still limited to unimodal and bimodal corpora. Since emotion is not only expressed through speech, but also visually at the same time, it is essential to include multiple modalities in an observation. In this paper, we present the first audio-visual emotion corpora in Japanese, collected from 14 native speakers. The corpus contains 100 minutes of annotated and transcribed material. We performed preliminary emotion recognition experiments on the corpus and achieved an accuracy of 61.42{\%} for five classes of emotion.