pwc-archive/papers-with-abstracts huggingface dataset:
Bridging the gap between speech technology and natural language processing: an evaluation toolbox for term discovery systems
Authors: Bogdan Ludusan, Maarten Versteegh, Aren Jansen, Guillaume Gravier, Xuan-Nga Cao, Mark Johnson, Emmanuel Dupoux
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Acquisition

The unsupervised discovery of linguistic terms from either continuous phoneme transcriptions or from raw speech has seen an increasing interest in the past years both from a theoretical and a practical standpoint. Yet, there exists no common accepted evaluation method for the systems performing term discovery. Here, we propose such an evaluation toolbox, drawing ideas from both speech technology and natural language processing. We first transform the speech-based output into a symbolic representation and compute five types of evaluation metrics on this representation: the quality of acoustic matching, the quality of the clusters found, and the quality of the alignment with real words (type, token, and boundary scores). We tested our approach on two term discovery systems taking speech as input, and one using symbolic input. The latter was run using both the gold transcription and a transcription obtained from an automatic speech recognizer, in order to simulate the case when only imperfect symbolic information is available. The results obtained are analysed through the use of the proposed evaluation metrics and the implications of these metrics are discussed.
Modeling and evaluating dialog success in the LAST MINUTE corpus
Authors: Dietmar R{\"o}sner, Rafael Friesen, Stephan G{\"u}nther, Rico Andrich
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

The LAST MINUTE corpus comprises records and transcripts of naturalistic problem solving dialogs between N = 130 subjects and a companion system simulated in a Wizard of Oz experiment. Our goal is to detect dialog situations where subjects might break up the dialog with the system which might happen when the subject is unsuccessful. We present a dialog act based representation of the dialog courses in the problem solving phase of the experiment and propose and evaluate measures for dialog success or failure derived from this representation. This dialog act representation refines our previous coarse measure as it enables the correct classification of many dialog sequences that were ambiguous before. The dialog act representation is useful for the identification of different subject groups and the exploration of interesting dialog courses in the corpus. We find young females to be most successful in the challenging last part of the problem solving phase and young subjects to have the initiative in the dialog more often than the elderly.
Comparison of Gender- and Speaker-adaptive Emotion Recognition
Authors: Maxim Sidorov, Stefan Ultes, Alex Schmitt, er
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Attribute, Emotion Classification, Emotion Recognition, Speaker Identification, Speech Recognition

Deriving the emotion of a human speaker is a hard task, especially if only the audio stream is taken into account. While state-of-the-art approaches already provide good results, adaptive methods have been proposed in order to further improve the recognition accuracy. A recent approach is to add characteristics of the speaker, e.g., the gender of the speaker. In this contribution, we argue that adding information unique for each speaker, i.e., by using speaker identification techniques, improves emotion recognition simply by adding this additional information to the feature vector of the statistical classification algorithm. Moreover, we compare this approach to emotion recognition adding only the speaker gender being a non-unique speaker attribute. We justify this by performing adaptive emotion recognition using both gender and speaker information on four different corpora of different languages containing acted and non-acted speech. The final results show that adding speaker information significantly outperforms both adding gender information and solely using a generic speaker-independent approach.
The pragmatic annotation of a corpus of academic lectures
Authors: Si{\^a}n Alsop, Hilary Nesi
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

This paper will describe a process of pragmatic annotation (c.f. Simpson-Vlach and Leicher 2006) which systematically identifies pragmatic meaning in spoken text. The annotation of stretches of text that perform particular pragmatic functions allows conclusions to be drawn across data sets at a different level than that of the individual lexical item, or structural content. The annotation of linguistic features, which cannot be identified by purely objective means, is distinguished here from structural mark-up of speaker identity, turns, pauses etc. The features annotated are explaining, housekeeping, humour, storytelling and summarising. Twenty-two subcategories are attributed to these elements. Data is from the Engineering Lecture Corpus (ELC), which includes 76 English-medium engineering lectures from the UK, New Zealand and Malaysia. The annotation allows us to compare differences in the use of these discourse features across cultural subcorpora. Results show that cultural context does impact on the linguistic realisation of commonly occurring discourse features in engineering lectures.
Using TEI, CMDI and ISOcat in CLARIN-DK
Authors: Dorte Haltrup Hansen, Lene Offersgaard, Sussi Olsen
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

This paper presents the challenges and issues encountered in the conversion of TEI header metadata into the CMDI format. The work is carried out in the Danish research infrastructure, CLARIN-DK, in order to enable the exchange of language resources nationally as well as internationally, in particular with other partners of CLARIN ERIC. The paper describes the task of converting an existing TEI specification applied to all the text resources deposited in DK-CLARIN. During the task we have tried to reuse and share CMDI profiles and components in the CLARIN Component Registry, as well as linking the CMDI components and elements to the relevant data categories in the ISOcat Data Category Registry. The conversion of the existing metadata into the CMDI format turned out not to be a trivial task and the experience and insights gained from this work have resulted in a proposal for a work flow for future use. We also present a core TEI header metadata set.
Comparative analysis of verbal alignment in human-human and human-agent interactions
Authors: Sabrina Campano, Jessica Durand, Chloé Clavel
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

Engagement is an important feature in human-human and human-agent interaction. In this paper, we investigate lexical alignment as a cue of engagement, relying on two different corpora : CID and SEMAINE. Our final goal is to build a virtual conversational character that could use alignment strategies to maintain user{'}s engagement. To do so, we investigate two alignment processes : shared vocabulary and other-repetitions. A quantitative and qualitative approach is proposed to characterize these aspects in human-human (CID) and human-operator (SEMAINE) interactions. Our results show that these processes are observable in both corpora, indicating a stable pattern that can be further modelled in conversational agents.
Transliteration and alignment of parallel texts from Cyrillic to Latin
Authors: Mircea Petic, Daniela G{\^\i}fu
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Common Sense Reasoning, Machine Translation, Transliteration

This article describes a methodology of recovering and preservation of old Romanian texts and problems related to their recognition. Our focus is to create a gold corpus for Romanian language (the novella Sania), for both alphabets used in Transnistria â€• Cyrillic and Latin. The resource is available for similar researches. This technology is based on transliteration and semiautomatic alignment of parallel texts at the level of letter/lexem/multiwords. We have analysed every text segment present in this corpus and discovered other conventions of writing at the level of transliteration, academic norms and editorial interventions. These conventions allowed us to elaborate and implement some new heuristics that make a correct automatic transliteration process. Sometimes the words of Latin script are modified in Cyrillic script from semantic reasons (for instance, editor{'}s interpretation). Semantic transliteration is seen as a good practice in introducing multiwords from Cyrillic to Latin. Not only does it preserve how a multiwords sound in the source script, but also enables the translator to modify in the original text (here, choosing the most common sense of an expression). Such a technology could be of interest to lexicographers, but also to specialists in computational linguistics to improve the actual transliteration standards.
How to Tell a Schneemann from a Milchmann: An Annotation Scheme for Compound-Internal Relations
Authors: Corina Dima, Verena Henrich, Erhard Hinrichs, Christina Hoppermann
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Information Retrieval, Machine Translation, Text Generation

This paper presents a language-independent annotation scheme for the semantic relations that link the constituents of noun-noun compounds, such as Schneemann {`}snow man{'} or Milchmann {`}milk man{'}. The annotation scheme is hybrid in the sense that it assigns each compound a two-place label consisting of a semantic property and a prepositional paraphrase. The resulting inventory combines the insights of previous annotation schemes that rely exclusively on either semantic properties or prepositions, thus avoiding the known weaknesses that result from using only one of the two label types. The proposed annotation scheme has been used to annotate a set of 5112 German noun-noun compounds. A release of the dataset is currently being prepared and will be made available via the CLARIN Center T{\"u}bingen. In addition to the presentation of the hybrid annotation scheme, the paper also reports on an inter-annotator agreement study that has resulted in a substantial agreement among annotators.
Can Numerical Expressions Be Simpler? Implementation and Demostration of a Numerical Simplification System for Spanish
Authors: Susana Bautista, Horacio Saggion
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Articles, Text Simplification

Information in newspapers is often showed in the form of numerical expressions which present comprehension problems for many people, including people with disabilities, illiteracy or lack of access to advanced technology. The purpose of this paper is to motivate, describe, and demonstrate a rule-based lexical component that simplifies numerical expressions in Spanish texts. We propose an approach that makes news articles more accessible to certain readers by rewriting difficult numerical expressions in a simpler way. We will showcase the numerical simplification system with a live demo based on the execution of our components over different texts, and which will consider both successful and unsuccessful simplification cases.
4FX: Light Verb Constructions in a Multilingual Parallel Corpus
Authors: Anita R{\'a}cz, Istv{\'a}n Nagy T., Veronika Vincze
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation

In this paper, we describe 4FX, a quadrilingual (English-Spanish-German-Hungarian) parallel corpus annotated for light verb constructions. We present the annotation process, and report statistical data on the frequency of LVCs in each language. We also offer inter-annotator agreement rates and we highlight some interesting facts and tendencies on the basis of comparing multilingual data from the four corpora. According to the frequency of LVC categories and the calculated KendallÂ’s coefficient for the four corpora, we found that Spanish and German are very similar to each other, Hungarian is also similar to both, but German differs from all these three. The qualitative and quantitative data analysis might prove useful in theoretical linguistic research for all the four languages. Moreover, the corpus will be an excellent testbed for the development and evaluation of machine learning based methods aiming at extracting or identifying light verb constructions in these four languages.
The eIdentity Text Exploration Workbench
Authors: Fritz Kliche, Andr{\'e} Blessing, Ulrich Heid, Jonathan Sonntag
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Articles, Information Retrieval, Named Entity Recognition (NER), Retrieval

We work on tools to explore text contents and metadata of newspaper articles as provided by news archives. Our tool components are being integrated into an {``}Exploration Workbench{''} for Digital Humanities researchers. Next to the conversion of different data formats and character encodings, a prominent feature of our design is its {``}Wizard{''} function for corpus building: Researchers import raw data and define patterns to extract text contents and metadata. The Workbench also comprises different tools for data cleaning. These include filtering of off-topic articles, duplicates and near-duplicates, corrupted and empty articles. We currently work on ca. 860.000 newspaper articles from different media archives, provided in different data formats. We index the data with state-of-the-art systems to allow for large scale information retrieval. We extract metadata on publishing dates, author names, newspaper sections, etc., and split articles into segments such as headlines, subtitles, paragraphs, etc. After cleaning the data and compiling a thematically homogeneous corpus, the sample can be used for quantitative analyses which are not affected by noise. Users can retrieve sets of articles on different topics, issues or otherwise defined research questions ({``}subcorpora{''}) and investigate quantitatively their media attention on the timeline ({``}Issue Cycles{''}).
Emilya: Emotional body expression in daily actions database
Authors: Nesrine Fourati, Catherine Pelachaud
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

The studies of bodily expression of emotion have been so far mostly focused on body movement patterns associated with emotional expression. Recently, there is an increasing interest on the expression of emotion in daily actions, called also non-emblematic movements (such as walking or knocking at the door). Previous studies were based on database limited to a small range of movement tasks or emotional states. In this paper, we describe our new database of emotional body expression in daily actions, where 11 actors express 8 emotions in 7 actions. We use motion capture technology to record body movements, but we recorded as well synchronized audio-visual data to enlarge the use of the database for different research purposes. We investigate also the matching between the expressed emotions and the perceived ones through a perceptive study. The first results of this study are discussed in this paper.
Using Stem-Templates to Improve Arabic POS and Gender/Number Tagging
Authors: Kareem Darwish, Ahmed Abdelali, Hamdy Mubarak
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Part-Of-Speech Tagging, POS, POS Tagging

This paper presents an end-to-end automatic processing system for Arabic. The system performs: correction of common spelling errors pertaining to different forms of alef, ta marbouta and ha, and alef maqsoura and ya; context sensitive word segmentation into underlying clitics, POS tagging, and gender and number tagging of nouns and adjectives. We introduce the use of stem templates as a feature to improve POS tagging by 0.5{\textbackslash}{\%} and to help ascertain the gender and number of nouns and adjectives. For gender and number tagging, we report accuracies that are significantly higher on previously unseen words compared to a state-of-the-art system.
Construction of Diachronic Ontologies from People's Daily of Fifty Years
Authors: Shaoda He, Xiaojun Zou, Liumingjing Xiao, Junfeng Hu
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Clustering

This paper presents an Ontology Learning From Text (OLFT) method follows the well-known OLFT cake layer framework. Based on the distributional similarity, the proposed method generates multi-level ontologies from comparatively small corpora with the aid of HITS algorithm. Currently, this method covers terms extraction, synonyms recognition, concepts discovery and concepts hierarchical clustering. Among them, both concepts discovery and concepts hierarchical clustering are aided by the HITS authority, which is obtained from the HITS algorithm by an iteratively recommended way. With this method, a set of diachronic ontologies is constructed for each year based on People{'}s Daily corpora of fifty years (i.e., from 1947 to 1996). Preliminary experiments show that our algorithm outperforms the Google{'}s RNN and K-means based algorithm in both concepts discovery and concepts hierarchical clustering.
ROOTS: a toolkit for easy, fast and consistent processing of large sequential annotated data collections
Authors: Jonathan Chevelu, Gw{\'e}nol{\'e} Lecorv{\'e}, Damien Lolive
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Diversity, Management

The development of new methods for given speech and natural language processing tasks usually consists in annotating large corpora of data before applying machine learning techniques to train models or to extract information. Beyond scientific aspects, creating and managing such annotated data sets is a recurrent problem. While using human annotators is obviously expensive in time and money, relying on automatic annotation processes is not a simple solution neither. Typically, the high diversity of annotation tools and of data formats, as well as the lack of efficient middleware to interface them all together, make such processes very complex and painful to design. To circumvent this problem, this paper presents the toolkit ROOTS, a freshly released open source toolkit (http://roots-toolkit.gforge.inria.fr) for easy, fast and consistent management of heterogeneously annotated data. ROOTS is designed to efficiently handle massive complex sequential data and to allow quick and light prototyping, as this is often required for research purposes. To illustrate these properties, three sample applications are presented in the field of speech and language processing, though ROOTS can more generally be easily extended to other application domains.
Computer-Aided Quality Assurance of an Icelandic Pronunciation Dictionary
Authors: Martin Jansche
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: speech-recognition, Speech Recognition, Speech Synthesis

We propose a model-driven method for ensuring the quality of pronunciation dictionaries. The key ingredient is computing an alignment between letter strings and phoneme strings, a standard technique in pronunciation modeling. The novel aspect of our method is the use of informative, parametric alignment models which are refined iteratively as they are tested against the data. We discuss the use of alignment failures as a signal for detecting and correcting problematic dictionary entries. We illustrate this method using an existing pronunciation dictionary for Icelandic. Our method is completely general and has been applied in the construction of pronunciation dictionaries for commercially deployed speech recognition systems in several languages.
Disambiguating Verbs by Collocation: Corpus Lexicography meets Natural Language Processing
Authors: Ismail El Maarouf, Jane Bradbury, V{\'\i}t Baisa, Patrick Hanks
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Semantic Parsing, Word Sense Disambiguation

This paper reports the results of Natural Language Processing (NLP) experiments in semantic parsing, based on a new semantic resource, the Pattern Dictionary of English Verbs (PDEV) (Hanks, 2013). This work is set in the DVC (Disambiguating Verbs by Collocation) project , a project in Corpus Lexicography aimed at expanding PDEV to a large scale. This project springs from a long-term collaboration of lexicographers with computer scientists which has given rise to the design and maintenance of specific, adapted, and user-friendly editing and exploration tools. Particular attention is drawn on the use of NLP deep semantic methods to help in data processing. Possible contributions of NLP include pattern disambiguation, the focus of this article. The present article explains how PDEV differs from other lexical resources and describes its structure in detail. It also presents new classification experiments on a subset of 25 verbs. The SVM model obtained a micro-average F1 score of 0.81.
Automatic Error Detection concerning the Definite and Indefinite Conjugation in the HunLearner Corpus
Authors: Veronika Vincze, J{\'a}nos Zsibrita, P{\'e}ter Durst, Martina Katalin Szab{\'o}
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

In this paper we present the results of automatic error detection, concerning the definite and indefinite conjugation in the extended version of the HunLearner corpus, the learners corpus of the Hungarian language. We present the most typical structures that trigger definite or indefinite conjugation in Hungarian and we also discuss the most frequent types of errors made by language learners in the corpus texts. We also illustrate the error types with sentences taken from the corpus. Our results highlight grammatical structures that might pose problems for learners of Hungarian, which can be fruitfully applied in the teaching and practicing of such constructions from the language teachers or learners point of view. On the other hand, these results may be exploited in extending the functionalities of a grammar checker, concerning the definiteness of the verb. Our automatic system was able to achieve perfect recall, i.e. it could find all the mismatches between the type of the object and the conjugation of the verb, which is promising for future studies in this area.
Speech-Based Emotion Recognition: Feature Selection by Self-Adaptive Multi-Criteria Genetic Algorithm
Authors: Maxim Sidorov, Christina Brester, Wolfgang Minker, Eugene Semenkin
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Emotion Recognition, feature selection, Open-Ended Question Answering, Spoken Dialogue Systems

Automated emotion recognition has a number of applications in Interactive Voice Response systems, call centers, etc. While employing existing feature sets and methods for automated emotion recognition has already achieved reasonable results, there is still a lot to do for improvement. Meanwhile, an optimal feature set, which should be used to represent speech signals for performing speech-based emotion recognition techniques, is still an open question. In our research, we tried to figure out the most essential features with self-adaptive multi-objective genetic algorithm as a feature selection technique and a probabilistic neural network as a classifier. The proposed approach was evaluated using a number of multi-languages databases (English, German), which were represented by 37- and 384-dimensional feature sets. According to the obtained results, the developed technique allows to increase the emotion recognition performance by up to 26.08 relative improvement in accuracy. Moreover, emotion recognition performance scores for all applied databases are improved.