pwc-archive/papers-with-abstracts huggingface dataset:
Building Evaluation Datasets for Consumer-Oriented Information Retrieval
Authors: Lorraine Goeuriot, Liadh Kelly, Guido Zuccon, Joao Palotti
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Information Retrieval, Retrieval

Common people often experience difficulties in accessing relevant, correct, accurate and understandable health information online. Developing search techniques that aid these information needs is challenging. In this paper we present the datasets created by CLEF eHealth Lab from 2013-2015 for evaluation of search solutions to support common people finding health information online. Specifically, the CLEF eHealth information retrieval (IR) task of this Lab has provided the research community with benchmarks for evaluating consumer-centered health information retrieval, thus fostering research and development aimed to address this challenging problem. Given consumer queries, the goal of the task is to retrieve relevant documents from the provided collection of web pages. The shared datasets provide a large health web crawl, queries representing people{'}s real world information needs, and relevance assessment judgements for the queries.
A Dataset for Open Event Extraction in English
Authors: Kiem-Hieu Nguyen, Xavier Tannier, Olivier Ferret, Romaric Besan{\c{c}}on
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Event Extraction

This article presents a corpus for development and testing of event schema induction systems in English. Schema induction is the task of learning templates with no supervision from unlabeled texts, and to group together entities corresponding to the same role in a template. Most of the previous work on this subject relies on the MUC-4 corpus. We describe the limits of using this corpus (size, non-representativeness, similarity of roles across templates) and propose a new, partially-annotated corpus in English which remedies some of these shortcomings. We make use of Wikinews to select the data inside the category Laws {\&} Justice, and query Google search engine to retrieve different documents on the same events. Only Wikinews documents are manually annotated and can be used for evaluation, while the others can be used for unsupervised learning. We detail the methodology used for building the corpus and evaluate some existing systems on this new data.
Phoneme Alignment Using the Information on Phonological Processes in Continuous Speech
Authors: Daniil Kocharov
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

The current study focuses on optimization of Levenshtein algorithm for the purpose of computing the optimal alignment between two phoneme transcriptions of spoken utterance containing sequences of phonetic symbols. The alignment is computed with the help of a confusion matrix in which costs for phonetic symbol deletion, insertion and substitution are defined taking into account various phonological processes that occur in fluent speech, such as anticipatory assimilation, phone elision and epenthesis. The corpus containing about 30 hours of Russian read speech was used to evaluate the presented algorithms. The experimental results have shown significant reduction of misalignment rate in comparison with the baseline Levenshtein algorithm: the number of errors has been reduced from 1.1 {\%} to 0.28 {\%}
CoRuSS - a New Prosodically Annotated Corpus of Russian Spontaneous Speech
Authors: Tatiana Kachkovskaia, Daniil Kocharov, Pavel Skrelin, Nina Volskaya
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper describes speech data recording, processing and annotation of a new speech corpus CoRuSS (Corpus of Russian Spontaneous Speech), which is based on connected communicative speech recorded from 60 native Russian male and female speakers of different age groups (from 16 to 77). Some Russian speech corpora available at the moment contain plain orthographic texts and provide some kind of limited annotation, but there are no corpora providing detailed prosodic annotation of spontaneous conversational speech. This corpus contains 30 hours of high quality recorded spontaneous Russian speech, half of it has been transcribed and prosodically labeled. The recordings consist of dialogues between two speakers, monologues (speakers{'} self-presentations) and reading of a short phonetically balanced text. Since the corpus is labeled for a wide range of linguistic - phonetic and prosodic - information, it provides basis for empirical studies of various spontaneous speech phenomena as well as for comparison with those we observe in prepared read speech. Since the corpus is designed as a open-access resource of speech data, it will also make possible to advance corpus-based analysis of spontaneous speech data across languages and speech technology development as well.
Defining and Counting Phonological Classes in Cross-linguistic Segment Databases
Authors: Dan Dediu, Scott Moisik
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Diversity

Recently, there has been an explosion in the availability of large, good-quality cross-linguistic databases such as WALS (Dryer {\&} Haspelmath, 2013), Glottolog (Hammarstrom et al., 2015) and Phoible (Moran {\&} McCloy, 2014). Databases such as Phoible contain the actual segments used by various languages as they are given in the primary language descriptions. However, this segment-level representation cannot be used directly for analyses that require generalizations over classes of segments that share theoretically interesting features. Here we present a method and the associated R (R Core Team, 2014) code that allows the flexible definition of such meaningful classes and that can identify the sets of segments falling into such a class for any language inventory. The method and its results are important for those interested in exploring cross-linguistic patterns of phonetic and phonological diversity and their relationship to extra-linguistic factors and processes such as climate, economics, history or human genetics.
Introducing the SEA\_AP: an Enhanced Tool for Automatic Prosodic Analysis
Authors: Marta Mart{\'\i}nez, Roc{\'\i}o Varela, Carmen Garc{\'\i}a Mateo, Elisa Fern{\'a}ndez Rei, Adela Mart{\'\i}nez Calvo
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Segmentation

SEA{\_}AP (Segmentador e Etiquetador Autom{\'a}tico para An{\'a}lise Pros{\'o}dica, Automatic Segmentation and Labelling for Prosodic Analysis) toolkit is an application that performs audio segmentation and labelling to create a TextGrid file which will be used to launch a prosodic analysis using Praat. In this paper, we want to describe the improved functionality of the tool achieved by adding a dialectometric analysis module using R scripts. The dialectometric analysis includes computing correlations among F0 curves and it obtains prosodic distances among the different variables of interest (location, speaker, structure, etc.). The dialectometric analysis requires large databases in order to be adequately computed, and automatic segmentation and labelling can create them thanks to a procedure less costly than the manual alternative. Thus, the integration of these tools into the SEA{\_}AP allows to propose a distribution of geoprosodic areas by means of a quantitative method, which completes the traditional dialectological point of view. The current version of the SEA{\_}AP toolkit is capable of analysing Galician, Spanish and Brazilian Portuguese data, and hence the distances between several prosodic linguistic varieties can be measured at present.
A Machine Learning based Music Retrieval and Recommendation System
Authors: Naziba Mostafa, Yan Wan, Unnayan Amitabh, Pascale Fung
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: BIG-bench Machine Learning, Retrieval

In this paper, we present a music retrieval and recommendation system using machine learning techniques. We propose a query by humming system for music retrieval that uses deep neural networks for note transcription and a note-based retrieval system for retrieving the correct song from the database. We evaluate our query by humming system using the standard MIREX QBSH dataset. We also propose a similar artist recommendation system which recommends similar artists based on acoustic features of the artists{'} music, online text descriptions of the artists and social media data. We use supervised machine learning techniques over all our features and compare our recommendation results to those produced by a popular similar artist recommendation website.
CirdoX: an on/off-line multisource speech and sound analysis software
Authors: Fr{\'e}d{\'e}ric Aman, Michel Vacher, Fran{\c{c}}ois Portet, William Duclot, Benjamin Lecouteux
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: General Classification

Vocal User Interfaces in domestic environments recently gained interest in the speech processing community. This interest is due to the opportunity of using it in the framework of Ambient Assisted Living both for home automation (vocal command) and for call for help in case of distress situations, i.e. after a fall. C IRDO X, which is a modular software, is able to analyse online the audio environment in a home, to extract the uttered sentences and then to process them thanks to an ASR module. Moreover, this system perfoms non-speech audio event classification; in this case, specific models must be trained. The software is designed to be modular and to process on-line the audio multichannel stream. Some exemples of studies in which C IRDO X was involved are described. They were operated in real environment, namely a Living lab environment.
Optimizing Computer-Assisted Transcription Quality with Iterative User Interfaces
Authors: Matthias Sperber, Graham Neubig, Satoshi Nakamura, Alex Waibel
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

Computer-assisted transcription promises high-quality speech transcription at reduced costs. This is achieved by limiting human effort to transcribing parts for which automatic transcription quality is insufficient. Our goal is to improve the human transcription quality via appropriate user interface design. We focus on iterative interfaces that allow humans to solve tasks based on an initially given suggestion, in this case an automatic transcription. We conduct a user study that reveals considerable quality gains for three variations of iterative interfaces over a non-iterative from-scratch transcription interface. Our iterative interfaces included post-editing, confidence-enhanced post-editing, and a novel retyping interface. All three yielded similar quality on average, but we found that the proposed retyping interface was less sensitive to the difficulty of the segment, and superior when the automatic transcription of the segment contained relatively many errors. An analysis using mixed-effects models allows us to quantify these and other factors and draw conclusions over which interface design should be chosen in which circumstance.
A Framework for Collecting Realistic Recordings of Dysarthric Speech - the homeService Corpus
Authors: Mauro Nicolao, Heidi Christensen, Stuart Cunningham, Phil Green, Thomas Hain
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper introduces a new British English speech database, named the homeService corpus, which has been gathered as part of the homeService project. This project aims to help users with speech and motor disabilities to operate their home appliances using voice commands. The audio recorded during such interactions consists of realistic data of speakers with severe dysarthria. The majority of the homeService corpus is recorded in real home environments where voice control is often the normal means by which users interact with their devices. The collection of the corpus is motivated by the shortage of realistic dysarthric speech corpora available to the scientific community. Along with the details on how the data is organised and how it can be accessed, a brief description of the framework used to make the recordings is provided. Finally, the performance of the homeService automatic recogniser for dysarthric speech trained with single-speaker data from the corpus is provided as an initial baseline. Access to the homeService corpus is provided through the dedicated web page at http://mini.dcs.shef.ac.uk/resources/homeservice-corpus/. This will also have the most updated description of the data. At the time of writing the collection process is still ongoing.
Automatic Anomaly Detection for Dysarthria across Two Speech Styles: Read vs Spontaneous Speech
Authors: Imed Laaridh, Corinne Fredouille, Christine Meunier
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Anomaly Detection

Perceptive evaluation of speech disorders is still the standard method in clinical practice for the diagnosing and the following of the condition progression of patients. Such methods include different tasks such as read speech, spontaneous speech, isolated words, sustained vowels, etc. In this context, automatic speech processing tools have proven pertinence in speech quality evaluation and assistive technology-based applications. Though, a very few studies have investigated the use of automatic tools on spontaneous speech. This paper investigates the behavior of an automatic phone-based anomaly detection system when applied on read and spontaneous French dysarthric speech. The behavior of the automatic tool reveals interesting inter-pathology differences across speech styles.
TTS for Low Resource Languages: A Bangla Synthesizer
Authors: Alex Gutkin, er, Linne Ha, Martin Jansche, Knot Pipatsrisawat, Richard Sproat
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Text Normalization, text-to-speech, Text to Speech

We present a text-to-speech (TTS) system designed for the dialect of Bengali spoken in Bangladesh. This work is part of an ongoing effort to address the needs of under-resourced languages. We propose a process for streamlining the bootstrapping of TTS systems for under-resourced languages. First, we use crowdsourcing to collect the data from multiple ordinary speakers, each speaker recording small amount of sentences. Second, we leverage an existing text normalization system for a related language (Hindi) to bootstrap a linguistic front-end for Bangla. Third, we employ statistical techniques to construct multi-speaker acoustic models using Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) and Hidden Markov Model (HMM) approaches. We then describe our experiments that show that the resulting TTS voices score well in terms of their perceived quality as measured by Mean Opinion Score (MOS) evaluations.
Speech Trax: A Bottom to the Top Approach for Speaker Tracking and Indexing in an Archiving Context
Authors: F{\'e}licien Vallet, Jim Uro, J{\'e}r{\'e}my Andriamakaoly, Hakim Nabi, Mathieu Derval, Jean Carrive
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: speaker-diarization, Speaker Diarization

With the increasing amount of audiovisual and digital data deriving from televisual and radiophonic sources, professional archives such as INA, France{'}s national audiovisual institute, acknowledge a growing need for efficient indexing tools. In this paper, we describe the Speech Trax system that aims at analyzing the audio content of TV and radio documents. In particular, we focus on the speaker tracking task that is very valuable for indexing purposes. First, we detail the overall architecture of the system and show the results obtained on a large-scale experiment, the largest to our knowledge for this type of content (about 1,300 speakers). Then, we present the Speech Trax demonstrator that gathers the results of various automatic speech processing techniques on top of our speaker tracking system (speaker diarization, speech transcription, etc.). Finally, we provide insight on the obtained performances and suggest hints for future improvements.
Web Chat Conversations from Contact Centers: a Descriptive Study
Authors: G{\'e}raldine Damnati, Aleks Guerraz, ra, Delphine Charlet
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Descriptive, Semantic Parsing

In this article we propose a descriptive study of a chat conversations corpus from an assistance contact center. Conversations are described from several view points, including interaction analysis, language deviation analysis and typographic expressivity marks analysis. We provide in particular a detailed analysis of language deviations that are encountered in our corpus of 230 conversations, corresponding to 6879 messages and 76839 words. These deviations may be challenging for further syntactic and semantic parsing. Analysis is performed with a distinction between Customer messages and Agent messages. On the overall only 4{\%} of the observed words are misspelled but 26{\%} of the messages contain at least one erroneous word (rising to 40{\%} when focused on Customer messages). Transcriptions of telephone conversations from an assistance call center are also studied, allowing comparisons between these two interaction modes to be drawn. The study reveals significant differences in terms of conversation flow, with an increased efficiency for chat conversations in spite of longer temporal span.
Identification of Drug-Related Medical Conditions in Social Media
Authors: Fran{\c{c}}ois Morlane-Hond{\`e}re, Cyril Grouin, Pierre Zweigenbaum
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

Monitoring social media has been shown to be an interesting approach for the early detection of drug adverse effects. In this paper, we describe a system which extracts medical entities in French drug reviews written by users. We focus on the identification of medical conditions, which is based on the concept of post-coordination: we first extract minimal medical-related entities (pain, stomach) then we combine them to identify complex ones (It was the worst [pain I ever felt in my stomach]). These two steps are respectively performed by two classifiers, the first being based on Conditional Random Fields and the second one on Support Vector Machines. The overall results of the minimal entity classifier are the following: P=0.926; R=0.849; F1=0.886. A thourough analysis of the feature set shows that, when combined with word lemmas, clusters generated by word2vec are the most valuable features. When trained on the output of the first classifier, the second classifier{'}s performances are the following: p=0.683;r=0.956;f1=0.797. The addition of post-processing rules did not add any significant global improvement but was found to modify the precision/recall ratio.
Creating a Lexicon of Bavarian Dialect by Means of Facebook Language Data and Crowdsourcing
Authors: Manuel Burghardt, Daniel Granvogl, Christian Wolff
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

Data acquisition in dialectology is typically a tedious task, as dialect samples of spoken language have to be collected via questionnaires or interviews. In this article, we suggest to use the {``}web as a corpus{''} approach for dialectology. We present a case study that demonstrates how authentic language data for the Bavarian dialect (ISO 639-3:bar) can be collected automatically from the social network Facebook. We also show that Facebook can be used effectively as a crowdsourcing platform, where users are willing to translate dialect words collaboratively in order to create a common lexicon of their Bavarian dialect. Key insights from the case study are summarized as {``}lessons learned{''}, together with suggestions for future enhancements of the lexicon creation approach.
A Corpus of Wikipedia Discussions: Over the Years, with Topic, Power and Gender Labels
Authors: Vinodkumar Prabhakaran, Owen Rambow
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Articles

In order to gain a deep understanding of how social context manifests in interactions, we need data that represents interactions from a large community of people over a long period of time, capturing different aspects of social context. In this paper, we present a large corpus of Wikipedia Talk page discussions that are collected from a broad range of topics, containing discussions that happened over a period of 15 years. The dataset contains 166,322 discussion threads, across 1236 articles/topics that span 15 different topic categories or domains. The dataset also captures whether the post is made by an registered user or not, and whether he/she was an administrator at the time of making the post. It also captures the Wikipedia age of editors in terms of number of months spent as an editor, as well as their gender. This corpus will be a valuable resource to investigate a variety of computational sociolinguistics research questions regarding online social interactions.
Phrase Detectives Corpus 1.0 Crowdsourced Anaphoric Coreference.
Authors: Jon Chamberlain, Massimo Poesio, Udo Kruschwitz
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: text annotation

Natural Language Engineering tasks require large and complex annotated datasets to build more advanced models of language. Corpora are typically annotated by several experts to create a gold standard; however, there are now compelling reasons to use a non-expert crowd to annotate text, driven by cost, speed and scalability. Phrase Detectives Corpus 1.0 is an anaphorically-annotated corpus of encyclopedic and narrative text that contains a gold standard created by multiple experts, as well as a set of annotations created by a large non-expert crowd. Analysis shows very good inter-expert agreement (kappa=.88-.93) but a more variable baseline crowd agreement (kappa=.52-.96). Encyclopedic texts show less agreement (and by implication are harder to annotate) than narrative texts. The release of this corpus is intended to encourage research into the use of crowds for text annotation and the development of more advanced, probabilistic language models, in particular for anaphoric coreference.
Summ-it++: an Enriched Version of the Summ-it Corpus
Authors: Ev Fonseca, ro, Andr{\'e} Antonitsch, S Collovini, ra, Daniela Amaral, Renata Vieira, Anny Figueira
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper presents Summ-it++, an enriched version the Summ-it corpus. In this new version, the corpus has received new semantic layers, named entity categories and relations between named entities, adding to the previous coreference annotation. In addition, we change the original Summ-it format to SemEval
Towards Multiple Antecedent Coreference Resolution in Specialized Discourse
Authors: Alicia Burga, Sergio Cajal, Joan Codina-Filb{\`a}, Leo Wanner
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Abstractive Text Summarization, coreference-resolution, Coreference Resolution

Despite the popularity of coreference resolution as a research topic, the overwhelming majority of the work in this area focused so far on single antecedence coreference only. Multiple antecedent coreference (MAC) has been largely neglected. This can be explained by the scarcity of the phenomenon of MAC in generic discourse. However, in specialized discourse such as patents, MAC is very dominant. It seems thus unavoidable to address the problem of MAC resolution in the context of tasks related to automatic patent material processing, among them abstractive summarization, deep parsing of patents, construction of concept maps of the inventions, etc. We present the first version of an operational rule-based MAC resolution strategy for patent material that covers the three major types of MAC: (i) nominal MAC, (ii) MAC with personal / relative pronouns, and MAC with reflexive / reciprocal pronouns. The evaluation shows that our strategy performs well in terms of precision and recall.