pwc-archive/papers-with-abstracts huggingface dataset:
TEG-REP: A corpus of Textual Entailment Graphs based on Relation Extraction Patterns
Authors: Kathrin Eichler, Feiyu Xu, Hans Uszkoreit, Leonhard Hennig, Sebastian Krause
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Natural Language Inference, Relation, Relation Extraction

The task of relation extraction is to recognize and extract relations between entities or concepts in texts. Dependency parse trees have become a popular source for discovering extraction patterns, which encode the grammatical relations among the phrases that jointly express relation instances. State-of-the-art weakly supervised approaches to relation extraction typically extract thousands of unique patterns only potentially expressing the target relation. Among these patterns, some are semantically equivalent, but differ in their morphological, lexical-semantic or syntactic form. Some express a relation that entails the target relation. We propose a new approach to structuring extraction patterns by utilizing entailment graphs, hierarchical structures representing entailment relations, and present a novel resource of gold-standard entailment graphs based on a set of patterns automatically acquired using distant supervision. We describe the methodology used for creating the dataset and present statistics of the resource as well as an analysis of inference types underlying the entailment decisions.
Passing a USA National Bar Exam: a First Corpus for Experimentation
Authors: Biralatei Fawei, Adam Wyner, Jeff Pan
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Natural Language Inference

Bar exams provide a key watershed by which legal professionals demonstrate their knowledge of the law and its application. Passing the bar entitles one to practice the law in a given jurisdiction. The bar provides an excellent benchmark for the performance of legal information systems since passing the bar would arguably signal that the system has acquired key aspects of legal reason on a par with a human lawyer. The paper provides a corpus and experimental results with material derived from a real bar exam, treating the problem as a form of textual entailment from the question to an answer. The providers of the bar exam material set the Gold Standard, which is the answer key. The experiments carried out using the {`}out of the box{'} the Excitement Open Platform for textual entailment. The results and evaluation show that the tool can identify wrong answers (non-entailment) with a high F1 score, but it performs poorly in identifying the correct answer (entailment). The results provide a baseline performance measure against which to evaluate future improvements. The reasons for the poor performance are examined, and proposals are made to augment the tool in the future. The corpus facilitates experimentation by other researchers.
Corpora for Learning the Mutual Relationship between Semantic Relatedness and Textual Entailment
Authors: Ngoc Phuoc An Vo, Octavian Popescu
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Natural Language Inference, RTE

In this paper we present the creation of a corpora annotated with both semantic relatedness (SR) scores and textual entailment (TE) judgments. In building this corpus we aimed at discovering, if any, the relationship between these two tasks for the mutual benefit of resolving one of them by relying on the insights gained from the other. We considered a corpora already annotated with TE judgments and we proceed to the manual annotation with SR scores. The RTE 1-4 corpora used in the PASCAL competition fit our need. The annotators worked independently of one each other and they did not have access to the TE judgment during annotation. The intuition that the two annotations are correlated received major support from this experiment and this finding led to a system that uses this information to revise the initial estimates of SR scores. As semantic relatedness is one of the most general and difficult task in natural language processing we expect that future systems will combine different sources of information in order to solve it. Our work suggests that textual entailment plays a quantifiable role in addressing it.
Can Topic Modelling benefit from Word Sense Information?
Authors: Adriana Ferrugento, Hugo Gon{\c{c}}alo Oliveira, Ana Alves, Filipe Rodrigues
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper proposes a new topic model that exploits word sense information in order to discover less redundant and more informative topics. Word sense information is obtained from WordNet and the discovered topics are groups of synsets, instead of mere surface words. A key feature is that all the known senses of a word are considered, with their probabilities. Alternative configurations of the model are described and compared to each other and to LDA, the most popular topic model. However, the obtained results suggest that there are no benefits of enriching LDA with word sense information.
Age and Gender Prediction on Health Forum Data
Authors: Prasha Shrestha, Nicolas Rey-Villamizar, Farig Sadeque, Ted Pedersen, Steven Bethard, Thamar Solorio
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Author Profiling, Gender Prediction, Prediction, valid

Health support forums have become a rich source of data that can be used to improve health care outcomes. A user profile, including information such as age and gender, can support targeted analysis of forum data. But users might not always disclose their age and gender. It is desirable then to be able to automatically extract this information from users{'} content. However, to the best of our knowledge there is no such resource for author profiling of health forum data. Here we present a large corpus, with close to 85,000 users, for profiling and also outline our approach and benchmark results to automatically detect a user{'}s age and gender from their forum posts. We use a mix of features from a user{'}s text as well as forum specific features to obtain accuracy well above the baseline, thus showing that both our dataset and our method are useful and valid.
Comparing Speech and Text Classification on ICNALE
Authors: Sergiu Nisioi
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Classification, General Classification, text-classification, Text Classification

In this paper we explore and compare a speech and text classification approach on a corpus of native and non-native English speakers. We experiment on a subset of the International Corpus Network of Asian Learners of English containing the recorded speeches and the equivalent text transcriptions. Our results suggest a high correlation between the spoken and written classification results, showing that native accent is highly correlated with grammatical structures found in text.
Monitoring Disease Outbreak Events on the Web Using Text-mining Approach and Domain Expert Knowledge
Authors: Elena Arsevska, Mathieu Roche, Sylvain Falala, Renaud Lancelot, David Chavernac, Pascal Hendrikx, Barbara Dufour
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

Timeliness and precision for detection of infectious animal disease outbreaks from the information published on the web is crucial for prevention against their spread. We propose a generic method to enrich and extend the use of different expressions as queries in order to improve the acquisition of relevant disease related pages on the web. Our method combines a text mining approach to extract terms from corpora of relevant disease outbreak documents, and domain expert elicitation (Delphi method) to propose expressions and to select relevant combinations between terms obtained with text mining. In this paper we evaluated the performance as queries of a number of expressions obtained with text mining and validated by a domain expert and expressions proposed by a panel of 21 domain experts. We used African swine fever as an infectious animal disease model. The expressions obtained with text mining outperformed as queries the expressions proposed by domain experts. However, domain experts proposed expressions not extracted automatically. Our method is simple to conduct and flexible to adapt to any other animal infectious disease and even in the public health domain.
On Developing Resources for Patient-level Information Retrieval
Authors: Stephen Wu, Tamara Timmons, Amy Yates, Meikun Wang, Steven Bedrick, William Hersh, Hongfang Liu
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Information Retrieval, Retrieval

Privacy concerns have often served as an insurmountable barrier for the production of research and resources in clinical information retrieval (IR). We believe that both clinical IR research innovation and legitimate privacy concerns can be served by the creation of intra-institutional, fully protected resources. In this paper, we provide some principles and tools for IR resource-building in the unique problem setting of patient-level IR, following the tradition of the Cranfield paradigm.
Annotating and Detecting Medical Events in Clinical Notes
Authors: Prescott Klassen, Fei Xia, Meliha Yetisgen
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Negation

Early detection and treatment of diseases that onset after a patient is admitted to a hospital, such as pneumonia, is critical to improving and reducing costs in healthcare. Previous studies (Tepper et al., 2013) showed that change-of-state events in clinical notes could be important cues for phenotype detection. In this paper, we extend the annotation schema proposed in (Klassen et al., 2014) to mark change-of-state events, diagnosis events, coordination, and negation. After we have completed the annotation, we build NLP systems to automatically identify named entities and medical events, which yield an f-score of 94.7{\%} and 91.8{\%}, respectively.
Speech Synthesis of Code-Mixed Text
Authors: Sunayana Sitaram, Alan W. black
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Language Identification, Speech Synthesis, text-to-speech, Text to Speech

Most Text to Speech (TTS) systems today assume that the input text is in a single language and is written in the same language that the text needs to be synthesized in. However, in bilingual and multilingual communities, code mixing or code switching occurs in speech, in which speakers switch between languages in the same utterance. Due to the popularity of social media, we now see code-mixing even in text in these multilingual communities. TTS systems capable of synthesizing such text need to be able to handle text that is written in multiple languages and scripts. Code-mixed text poses many challenges to TTS systems, such as language identification, spelling normalization and pronunciation modeling. In this work, we describe a preliminary framework for synthesizing code-mixed text. We carry out experiments on synthesizing code-mixed Hindi and English text. We find that there is a significant user preference for TTS systems that can correctly identify and pronounce words in different languages.
Chatbot Technology with Synthetic Voices in the Acquisition of an Endangered Language: Motivation, Development and Evaluation of a Platform for Irish
Authors: Neasa N{\'\i} Chiar{\'a}in, Ailbhe N{\'\i} Chasaide
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Chatbot

This paper describes the development and evaluation of a chatbot platform designed for the teaching/learning of Irish. The chatbot uses synthetic voices developed for the dialects of Irish. Speech-enabled chatbot technology offers a potentially powerful tool for dealing with the challenges of teaching/learning an endangered language where learners have limited access to native speaker models of the language and limited exposure to the language in a truly communicative setting. The sociolinguistic context that motivates the present development is explained. The evaluation of the chatbot was carried out in 13 schools by 228 pupils and consisted of two parts. Firstly, learners{'} opinions of the overall chatbot platform as a learning environment were elicited. Secondly, learners evaluated the intelligibility, quality, and attractiveness of the synthetic voices used in this platform. Results were overwhelmingly positive to both the learning platform and the synthetic voices and indicate that the time may now be ripe for language learning applications which exploit speech and language technologies. It is further argued that these technologies have a particularly vital role to play in the maintenance of the endangered language.
CHATR the Corpus; a 20-year-old archive of Concatenative Speech Synthesis
Authors: Nick Campbell
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Speech Synthesis

This paper reports the preservation of an old speech synthesis website as a corpus. CHATR was a revolutionary technique developed in the mid nineties for concatenative speech synthesis. The method has since become the standard for high quality speech output by computer although much of the current research is devoted to parametric or hybrid methods that employ smaller amounts of data and can be more easily tunable to individual voices. The system was first reported in 1994 and the website was functional in 1996. The ATR labs where this system was invented no longer exist, but the website has been preserved as a corpus containing 1537 samples of synthesised speech from that period (118 MB in aiff format) in 211 pages under various finely interrelated themes The corpus can be accessed from www.speech-data.jp as well as www.tcd-fastnet.com, where the original code and samples are now being maintained.
How to Address Smart Homes with a Social Robot? A Multi-modal Corpus of User Interactions with an Intelligent Environment
Authors: Patrick Holthaus, Christian Leichsenring, Jasmin Bernotat, Viktor Richter, Marian Pohling, Birte Carlmeyer, Norman K{\"o}ster, Sebastian Meyer zu Borgsen, Ren{\'e} Zorn, Birte Schiffhauer, Kai Frederic Engelmann, Florian Lier, Simon Schulz, Philipp Cimiano, Friederike Eyssel, Thomas Hermann, Franz Kummert, David Schlangen, Sven Wachsmuth, Petra Wagner, Britta Wrede, Sebastian Wrede
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

In order to explore intuitive verbal and non-verbal interfaces in smart environments we recorded user interactions with an intelligent apartment. Besides offering various interactive capabilities itself, the apartment is also inhabited by a social robot that is available as a humanoid interface. This paper presents a multi-modal corpus that contains goal-directed actions of naive users in attempts to solve a number of predefined tasks. Alongside audio and video recordings, our data-set consists of large amount of temporally aligned sensory data and system behavior provided by the environment and its interactive components. Non-verbal system responses such as changes in light or display contents, as well as robot and apartment utterances and gestures serve as a rich basis for later in-depth analysis. Manual annotations provide further information about meta data like the current course of study and user behavior including the incorporated modality, all literal utterances, language features, emotional expressions, foci of attention, and addressees.
A Corpus of Gesture-Annotated Dialogues for Monologue-to-Dialogue Generation from Personal Narratives
Authors: Zhichao Hu, Michelle Dick, Chung-Ning Chang, Kevin Bowden, Michael Neff, Jean Fox Tree, Marilyn Walker
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Dialogue Generation, Text Generation

Story-telling is a fundamental and prevalent aspect of human social behavior. In the wild, stories are told conversationally in social settings, often as a dialogue and with accompanying gestures and other nonverbal behavior. This paper presents a new corpus, the Story Dialogue with Gestures (SDG) corpus, consisting of 50 personal narratives regenerated as dialogues, complete with annotations of gesture placement and accompanying gesture forms. The corpus includes dialogues generated by human annotators, gesture annotations on the human generated dialogues, videos of story dialogues generated from this representation, video clips of each gesture used in the gesture annotations, and annotations of the original personal narratives with a deep representation of story called a Story Intention Graph. Our long term goal is the automatic generation of story co-tellings as animated dialogues from the Story Intention Graph. We expect this corpus to be a useful resource for researchers interested in natural language generation, intelligent virtual agents, generation of nonverbal behavior, and story and narrative representations.
Multimodal Resources for Human-Robot Communication Modelling
Authors: Stavroula{--}Evita Fotinea, Eleni Efthimiou, Maria Koutsombogera, Athanasia-Lida Dimou, Theodore Goulas, Kyriaki Vasilaki
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper reports on work related to the modelling of Human-Robot Communication on the basis of multimodal and multisensory human behaviour analysis. A primary focus in this framework of analysis is the definition of semantics of human actions in interaction, their capture and their representation in terms of behavioural patterns that, in turn, feed a multimodal human-robot communication system. Semantic analysis encompasses both oral and sign languages, as well as both verbal and non-verbal communicative signals to achieve an effective, natural interaction between elderly users with slight walking and cognitive inability and an assistive robotic platform.
A Verbal and Gestural Corpus of Story Retellings to an Expressive Embodied Virtual Character
Authors: Jackson Tolins, Kris Liu, Michael Neff, Marilyn Walker, Jean Fox Tree
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

We present a corpus of 44 human-agent verbal and gestural story retellings designed to explore whether humans would gesturally entrain to an embodied intelligent virtual agent. We used a novel data collection method where an agent presented story components in installments, which the human would then retell to the agent. At the end of the installments, the human would then retell the embodied animated agent the story as a whole. This method was designed to allow us to observe whether changes in the agent{'}s gestural behavior would result in human gestural changes. The agent modified its gestures over the course of the story, by starting out the first installment with gestural behaviors designed to manifest extraversion, and slowly modifying gestures to express introversion over time, or the reverse. The corpus contains the verbal and gestural transcripts of the human story retellings. The gestures were coded for type, handedness, temporal structure, spatial extent, and the degree to which the participants{'} gestures match those produced by the agent. The corpus illustrates the variation in expressive behaviors produced by users interacting with embodied virtual characters, and the degree to which their gestures were influenced by the agent{'}s dynamic changes in personality-based expressive style.
A Multimodal Motion-Captured Corpus of Matched and Mismatched Extravert-Introvert Conversational Pairs
Authors: Jackson Tolins, Kris Liu, Yingying Wang, Jean E. Fox Tree, Marilyn Walker, Michael Neff
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper presents a new corpus, the Personality Dyads Corpus, consisting of multimodal data for three conversations between three personality-matched, two-person dyads (a total of 9 separate dialogues). Participants were selected from a larger sample to be 0.8 of a standard deviation above or below the mean on the Big-Five Personality extraversion scale, to produce an Extravert-Extravert dyad, an Introvert-Introvert dyad, and an Extravert-Introvert dyad. Each pair carried out conversations for three different tasks. The conversations were recorded using optical motion capture for the body and data gloves for the hands. Dyads{'} speech was transcribed and the gestural and postural behavior was annotated with ANVIL. The released corpus includes personality profiles, ANVIL files containing speech transcriptions and the gestural annotations, and BVH files containing body and hand motion in 3D.
Crowdsourcing Ontology Lexicons
Authors: Bettina Lanser, Christina Unger, Philipp Cimiano
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Translation

In order to make the growing amount of conceptual knowledge available through ontologies and datasets accessible to humans, NLP applications need access to information on how this knowledge can be verbalized in natural language. One way to provide this kind of information are ontology lexicons, which apart from the actual verbalizations in a given target language can provide further, rich linguistic information about them. Compiling such lexicons manually is a very time-consuming task and requires expertise both in Semantic Web technologies and lexicon engineering, as well as a very good knowledge of the target language at hand. In this paper we present an alternative approach to generating ontology lexicons by means of crowdsourcing: We use CrowdFlower to generate a small Japanese ontology lexicon for ten exemplary ontology elements from the DBpedia ontology according to a two-stage workflow, the main underlying idea of which is to turn the task of generating lexicon entries into a translation task; the starting point of this translation task is a manually created English lexicon for DBpedia. Comparison of the results to a manually created Japanese lexicon shows that the presented workflow is a viable option if an English seed lexicon is already available.
A Crowdsourced Database of Event Sequence Descriptions for the Acquisition of High-quality Script Knowledge
Authors: Lilian D. A. Wanzare, Aless Zarcone, ra, Stefan Thater, Manfred Pinkal
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Clustering

Scripts are standardized event sequences describing typical everyday activities, which play an important role in the computational modeling of cognitive abilities (in particular for natural language processing). We present a large-scale crowdsourced collection of explicit linguistic descriptions of script-specific event sequences (40 scenarios with 100 sequences each). The corpus is enriched with crowdsourced alignment annotation on a subset of the event descriptions, to be used in future work as seed data for automatic alignment of event descriptions (for example via clustering). The event descriptions to be aligned were chosen among those expected to have the strongest corrective effect on the clustering algorithm. The alignment annotation was evaluated against a gold standard of expert annotators. The resulting database of partially-aligned script-event descriptions provides a sound empirical basis for inducing high-quality script knowledge, as well as for any task involving alignment and paraphrase detection of events.
Temporal Information Annotation: Crowd vs. Experts
Authors: Tommaso Caselli, Rachele Sprugnoli, Oana Inel
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: General Classification

This paper describes two sets of crowdsourcing experiments on temporal information annotation conducted on two languages, i.e., English and Italian. The first experiment, launched on the CrowdFlower platform, was aimed at classifying temporal relations given target entities. The second one, relying on the CrowdTruth metric, consisted in two subtasks: one devoted to the recognition of events and temporal expressions and one to the detection and classification of temporal relations. The outcomes of the experiments suggest a valuable use of crowdsourcing annotations also for a complex task like Temporal Processing.