pwc-archive/papers-with-abstracts huggingface dataset:
The CIRDO Corpus: Comprehensive Audio/Video Database of Domestic Falls of Elderly People
Authors: Michel Vacher, Sa{\"\i}da Bouakaz, Marc-Eric Bobillier Chaumon, Fr{\'e}d{\'e}ric Aman, R. A. Khan, Slima Bekkadja, Fran{\c{c}}ois Portet, Erwan Guillou, Solange Rossato, Benjamin Lecouteux
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

Ambient Assisted Living aims at enhancing the quality of life of older and disabled people at home thanks to Smart Homes. In particular, regarding elderly living alone at home, the detection of distress situation after a fall is very important to reassure this kind of population. However, many studies do not include tests in real settings, because data collection in this domain is very expensive and challenging and because of the few available data sets. The C IRDO corpus is a dataset recorded in realistic conditions in D OMUS , a fully equipped Smart Home with microphones and home automation sensors, in which participants performed scenarios including real falls on a carpet and calls for help. These scenarios were elaborated thanks to a field study involving elderly persons. Experiments related in a first part to distress detection in real-time using audio and speech analysis and in a second part to fall detection using video analysis are presented. Results show the difficulty of the task. The database can be used as standardized database by researchers to evaluate and compare their systems for elderly person{'}s assistance.
Semi-automatically Alignment of Predicates between Speech and OntoNotes data
Authors: Niraj Shrestha, Marie-Francine Moens
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Semantic Role Labeling, Sentence

Speech data currently receives a growing attention and is an important source of information. We still lack suitable corpora of transcribed speech annotated with semantic roles that can be used for semantic role labeling (SRL), which is not the case for written data. Semantic role labeling in speech data is a challenging and complex task due to the lack of sentence boundaries and the many transcription errors such as insertion, deletion and misspellings of words. In written data, SRL evaluation is performed at the sentence level, but in speech data sentence boundaries identification is still a bottleneck which makes evaluation more complex. In this work, we semi-automatically align the predicates found in transcribed speech obtained with an automatic speech recognizer (ASR) with the predicates found in the corresponding written documents of the OntoNotes corpus and manually align the semantic roles of these predicates thus obtaining annotated semantic frames in the speech data. This data can serve as gold standard alignments for future research in semantic role labeling of speech data.
CORILSE: a Spanish Sign Language Repository for Linguistic Analysis
Authors: Mar{\'\i}a del Carmen Cabeza-Pereiro, Jos{\'e} MÂª Garcia-Miguel, Carmen Garc{\'\i}a Mateo, Jos{\'e} Luis Alba Castro
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

CORILSE is a computerized corpus of Spanish Sign Language (Lengua de Signos Espa{\~n}ola, LSE). It consists of a set of recordings from different discourse genres by Galician signers living in the city of Vigo. In this paper we describe its annotation system, developed on the basis of pre-existing ones (mostly the model of Auslan corpus). This includes primary annotation of id-glosses for manual signs, annotation of non-manual component, and secondary annotation of grammatical categories and relations, because this corpus is been built for grammatical analysis, in particular argument structures in LSE. Up until this moment the annotation has been basically made by hand, which is a slow and time-consuming task. The need to facilitate this process leads us to engage in the development of automatic or semi-automatic tools for manual and facial recognition. Finally, we also present the web repository that will make the corpus available to different types of users, and will allow its exploitation for research purposes and other applications (e.g. teaching of LSE or design of tasks for signed language assessment).
The OFAI Multi-Modal Task Description Corpus
Authors: Stephanie Schreitter, Brigitte Krenn
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

The OFAI Multimodal Task Description Corpus (OFAI-MMTD Corpus) is a collection of dyadic teacher-learner (human-human and human-robot) interactions. The corpus is multimodal and tracks the communication signals exchanged between interlocutors in task-oriented scenarios including speech, gaze and gestures. The focus of interest lies on the communicative signals conveyed by the teacher and which objects are salient at which time. Data are collected from four different task description setups which involve spatial utterances, navigation instructions and more complex descriptions of joint tasks.
A Japanese Chess Commentary Corpus
Authors: Shinsuke Mori, John Richardson, Atsushi Ushiku, Tetsuro Sasada, Hirotaka Kameko, Yoshimasa Tsuruoka
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Natural Language Queries, TAG, Text Generation

In recent years there has been a surge of interest in the natural language prosessing related to the real world, such as symbol grounding, language generation, and nonlinguistic data search by natural language queries. In order to concentrate on language ambiguities, we propose to use a well-defined {``}real world,{''} that is game states. We built a corpus consisting of pairs of sentences and a game state. The game we focus on is shogi (Japanese chess). We collected 742,286 commentary sentences in Japanese. They are spontaneously generated contrary to natural language annotations in many image datasets provided by human workers on Amazon Mechanical Turk. We defined domain specific named entities and we segmented 2,508 sentences into words manually and annotated each word with a named entity tag. We describe a detailed definition of named entities and show some statistics of our game commentary corpus. We also show the results of the experiments of word segmentation and named entity recognition. The accuracies are as high as those on general domain texts indicating that we are ready to tackle various new problems related to the real world.
The CAMOMILE Collaborative Annotation Platform for Multi-modal, Multi-lingual and Multi-media Documents
Authors: Johann Poignant, Mateusz Budnik, Herv{\'e} Bredin, Claude Barras, Mickael Stefas, Pierrick Bruneau, Gilles Adda, Laurent Besacier, Hazim Ekenel, Gil Francopoulo, Hern, Javier o, Joseph Mariani, Ramon Morros, Georges Qu{\'e}not, Sophie Rosset, Thomas Tamisier
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Active Learning, Management

In this paper, we describe the organization and the implementation of the CAMOMILE collaborative annotation framework for multimodal, multimedia, multilingual (3M) data. Given the versatile nature of the analysis which can be performed on 3M data, the structure of the server was kept intentionally simple in order to preserve its genericity, relying on standard Web technologies. Layers of annotations, defined as data associated to a media fragment from the corpus, are stored in a database and can be managed through standard interfaces with authentication. Interfaces tailored specifically to the needed task can then be developed in an agile way, relying on simple but reliable services for the management of the centralized annotations. We then present our implementation of an active learning scenario for person annotation in video, relying on the CAMOMILE server; during a dry run experiment, the manual annotation of 716 speech segments was thus propagated to 3504 labeled tracks. The code of the CAMOMILE framework is distributed in open source.
Finding Recurrent Features of Image Schema Gestures: the FIGURE corpus
Authors: Andy Luecking, Alex Mehler, er, D{\'e}sir{\'e}e Walther, Marcel Mauri, Dennis Kurf{\"u}rst
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Descriptive

The Frankfurt Image GestURE corpus (FIGURE) is introduced. The corpus data is collected in an experimental setting where 50 naive participants spontaneously produced gestures in response to five to six terms from a total of 27 stimulus terms. The stimulus terms have been compiled mainly from image schemata from psycholinguistics, since such schemata provide a panoply of abstract contents derived from natural language use. The gestures have been annotated for kinetic features. FIGURE aims at finding (sets of) stable kinetic feature configurations associated with the stimulus terms. Given such configurations, they can be used for designing HCI gestures that go beyond pre-defined gesture vocabularies or touchpad gestures. It is found, for instance, that movement trajectories are far more informative than handshapes, speaking against purely handshape-based HCI vocabularies. Furthermore, the mean temporal duration of hand and arm movements associated vary with the stimulus terms, indicating a dynamic dimension not covered by vocabulary-based approaches. Descriptive results are presented and related to findings from gesture studies and natural language dialogue.
An Interaction-Centric Dataset for Learning Automation Rules in Smart Homes
Authors: Kai Frederic Engelmann, Patrick Holthaus, Britta Wrede, Sebastian Wrede
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

The term smart home refers to a living environment that by its connected sensors and actuators is capable of providing intelligent and contextualised support to its user. This may result in automated behaviors that blends into the user{'}s daily life. However, currently most smart homes do not provide such intelligent support. A first step towards such intelligent capabilities lies in learning automation rules by observing the user{'}s behavior. We present a new type of corpus for learning such rules from user behavior as observed from the events in a smart homes sensor and actuator network. The data contains information about intended tasks by the users and synchronized events from this network. It is derived from interactions of 59 users with the smart home in order to solve five tasks. The corpus contains recordings of more than 40 different types of data streams and has been segmented and pre-processed to increase signal quality. Overall, the data shows a high noise level on specific data types that can be filtered out by a simple smoothing approach. The resulting data provides insights into event patterns resulting from task specific user behavior and thus constitutes a basis for machine learning approaches to learn automation rules.
A Web Tool for Building Parallel Corpora of Spoken and Sign Languages
Authors: Alex Becker, Fabio Kepler, C, Sara eias
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Translation

In this paper we describe our work in building an online tool for manually annotating texts in any spoken language with SignWriting in any sign language. The existence of such tool will allow the creation of parallel corpora between spoken and sign languages that can be used to bootstrap the creation of efficient tools for the Deaf community. As an example, a parallel corpus between English and American Sign Language could be used for training Machine Learning models for automatic translation between the two languages. Clearly, this kind of tool must be designed in a way that it eases the task of human annotators, not only by being easy to use, but also by giving smart suggestions as the annotation progresses, in order to save time and effort. By building a collaborative, online, easy to use annotation tool for building parallel corpora between spoken and sign languages we aim at helping the development of proper resources for sign languages that can then be used in state-of-the-art models currently used in tools for spoken languages. There are several issues and difficulties in creating this kind of resource, and our presented tool already deals with some of them, like adequate text representation of a sign and many to many alignments between words and signs.
Issues and Challenges in Annotating Urdu Action Verbs on the IMAGACT4ALL Platform
Authors: Sharmin Muzaffar, Pitambar Behera, Girish Jha
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Machine Translation

In South-Asian languages such as Hindi and Urdu, action verbs having compound constructions and serial verbs constructions pose serious problems for natural language processing and other linguistic tasks. Urdu is an Indo-Aryan language spoken by 51, 500, 0001 speakers in India. Action verbs that occur spontaneously in day-to-day communication are highly ambiguous in nature semantically and as a consequence cause disambiguation issues that are relevant and applicable to Language Technologies (LT) like Machine Translation (MT) and Natural Language Processing (NLP). IMAGACT4ALL is an ontology-driven web-based platform developed by the University of Florence for storing action verbs and their inter-relations. This group is currently collaborating with Jawaharlal Nehru University (JNU) in India to connect Indian languages on this platform. Action verbs are frequently used in both written and spoken discourses and refer to various meanings because of their polysemic nature. The IMAGACT4ALL platform stores each 3d animation image, each one of them referring to a variety of possible ontological types, which in turn makes the annotation task for the annotator quite challenging with regard to selecting verb argument structure having a range of probability distribution. The authors, in this paper, discuss the issues and challenges such as complex predicates (compound and conjunct verbs), ambiguously animated video illustrations, semantic discrepancies, and the factors of verb-selection preferences that have produced significant problems in annotating Urdu verbs on the IMAGACT ontology.
Domain Ontology Learning Enhanced by Optimized Relation Instance in DBpedia
Authors: Liumingjing Xiao, Chong Ruan, An Yang, Junhao Zhang, Junfeng Hu
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Relation

Ontologies are powerful to support semantic based applications and intelligent systems. While ontology learning are challenging due to its bottleneck in handcrafting structured knowledge sources and training data. To address this difficulty, many researchers turn to ontology enrichment and population using external knowledge sources such as DBpedia. In this paper, we propose a method using DBpedia in a different manner. We utilize relation instances in DBpedia to supervise the ontology learning procedure from unstructured text, rather than populate the ontology structure as a post-processing step. We construct three language resources in areas of computer science: enriched Wikipedia concept tree, domain ontology, and gold standard from NSFC taxonomy. Experiment shows that the result of ontology learning from corpus of computer science can be improved via the relation instances extracted from DBpedia in the same field. Furthermore, making distinction between the relation instances and applying a proper weighting scheme in the learning procedure lead to even better result.
Constructing a Norwegian Academic Wordlist
Authors: Janne M Johannessen, Arash Saidi, Kristin Hagen
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

We present the development of a Norwegian Academic Wordlist (AKA list) for the Norwegian Bokm{\"a}l variety. To identify specific academic vocabulary we developed a 100-million-word academic corpus based on the University of Oslo archive of digital publications. Other corpora were used for testing and developing general word lists. We tried two different methods, those of Carlund et al. (2012) and Gardner {\&} Davies (2013), and compared them. The resulting list is presented on a web site, where the words can be inspected in different ways, and freely downloaded.
The Event and Implied Situation Ontology (ESO): Application and Evaluation
Authors: Roxane Segers, Marco Rospocher, Piek Vossen, Egoitz Laparra, German Rigau, Anne-Lyse Minard
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper presents the Event and Implied Situation Ontology (ESO), a manually constructed resource which formalizes the pre and post situations of events and the roles of the entities affected by an event. The ontology is built on top of existing resources such as WordNet, SUMO and FrameNet. The ontology is injected to the Predicate Matrix, a resource that integrates predicate and role information from amongst others FrameNet, VerbNet, PropBank, NomBank and WordNet. We illustrate how these resources are used on large document collections to detect information that otherwise would have remained implicit. The ontology is evaluated on two aspects: recall and precision based on a manually annotated corpus and secondly, on the quality of the knowledge inferred by the situation assertions in the ontology. Evaluation results on the quality of the system show that 50{\%} of the events typed and enriched with ESO assertions are correct.
Combining Ontologies and Neural Networks for Analyzing Historical Language Varieties. A Case Study in Middle Low German
Authors: Maria Sukhareva, Christian Chiarcos
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: POS

In this paper, we describe experiments on the morphosyntactic annotation of historical language varieties for the example of Middle Low German (MLG), the official language of the German Hanse during the Middle Ages and a dominant language around the Baltic Sea by the time. To our best knowledge, this is the first experiment in automatically producing morphosyntactic annotations for Middle Low German, and accordingly, no part-of-speech (POS) tagset is currently agreed upon. In our experiment, we illustrate how ontology-based specifications of projected annotations can be employed to circumvent this issue: Instead of training and evaluating against a given tagset, we decomponse it into independent features which are predicted independently by a neural network. Using consistency constraints (axioms) from an ontology, then, the predicted feature probabilities are decoded into a sound ontological representation. Using these representations, we can finally bootstrap a POS tagset capturing only morphosyntactic features which could be reliably predicted. In this way, our approach is capable to optimize precision and recall of morphosyntactic annotations simultaneously with bootstrapping a tagset rather than performing iterative cycles.
Ecological Gestures for HRI: the GEE Corpus
Authors: Maxence Girard-Rivier, Romain Magnani, V{\'e}ronique Auberg{\'e}, Yuko Sasa, Liliya Tsvetanova, Fr{\'e}d{\'e}ric Aman, Clarisse Bayol
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Gesture Recognition

As part of a human-robot interaction project, we are interested by gestural modality as one of many ways to communicate. In order to develop a relevant gesture recognition system associated to a smart home butler robot. Our methodology is based on an IQ game-like Wizard of Oz experiment to collect spontaneous and implicitly produced gestures in an ecological context. During the experiment, the subject has to use non-verbal cues (i.e. gestures) to interact with a robot that is the referee. The subject is unaware that his gestures will be the focus of our study. In the second part of the experiment, we asked the subjects to do the gestures he had produced in the experiment, those are the explicit gestures. The implicit gestures are compared with explicitly produced ones to determine a relevant ontology. This preliminary qualitative analysis will be the base to build a big data corpus in order to optimize acceptance of the gesture dictionary in coherence with the {``}socio-affective glue{''} dynamics.
A Taxonomy of Spanish Nouns, a Statistical Algorithm to Generate it and its Implementation in Open Source Code
Authors: Rogelio Nazar, Irene Renau
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Decision Making

In this paper we describe our work in progress in the automatic development of a taxonomy of Spanish nouns, we offer the Perl implementation we have so far, and we discuss the different problems that still need to be addressed. We designed a statistically-based taxonomy induction algorithm consisting of a combination of different strategies not involving explicit linguistic knowledge. Being all quantitative, the strategies we present are however of different nature. Some of them are based on the computation of distributional similarity coefficients which identify pairs of sibling words or co-hyponyms, while others are based on asymmetric co-occurrence and identify pairs of parent-child words or hypernym-hyponym relations. A decision making process is then applied to combine the results of the previous steps, and finally connect lexical units to a basic structure containing the most general categories of the language. We evaluate the quality of the taxonomy both manually and also using Spanish Wordnet as a gold-standard. We estimate an average of 89.07{\%} precision and 25.49{\%} recall considering only the results which the algorithm presents with high degree of certainty, or 77.86{\%} precision and 33.72{\%} recall considering all results.
FOLK-Gold ― A Gold Standard for Part-of-Speech-Tagging of Spoken German
Authors: Swantje Westpfahl, Thomas Schmidt
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Lemmatization, Part-Of-Speech Tagging, POS

In this paper, we present a GOLD standard of part-of-speech tagged transcripts of spoken German. The GOLD standard data consists of four annotation layers ― transcription (modified orthography), normalization (standard orthography), lemmatization and POS tags ― all of which have undergone careful manual quality control. It comes with guidelines for the manual POS annotation of transcripts of German spoken data and an extended version of the STTS (Stuttgart T{\"u}bingen Tagset) which accounts for phenomena typically found in spontaneous spoken German. The GOLD standard was developed on the basis of the Research and Teaching Corpus of Spoken German, FOLK, and is, to our knowledge, the first such dataset based on a wide variety of spontaneous and authentic interaction types. It can be used as a basis for further development of language technology and corpus linguistic applications for German spoken language.
Fast and Robust POS tagger for Arabic Tweets Using Agreement-based Bootstrapping
Authors: Fahad Albogamy, Allan Ramsay
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Part-Of-Speech Tagging, POS, POS Tagging, TAG

Part-of-Speech(POS) tagging is a key step in many NLP algorithms. However, tweets are difficult to POS tag because they are short, are not always written maintaining formal grammar and proper spelling, and abbreviations are often used to overcome their restricted lengths. Arabic tweets also show a further range of linguistic phenomena such as usage of different dialects, romanised Arabic and borrowing foreign words. In this paper, we present an evaluation and a detailed error analysis of state-of-the-art POS taggers for Arabic when applied to Arabic tweets. On the basis of this analysis, we combine normalisation and external knowledge to handle the domain noisiness and exploit bootstrapping to construct extra training data in order to improve POS tagging for Arabic tweets. Our results show significant improvements over the performance of a number of well-known taggers for Arabic.