pwc-archive/papers-with-abstracts huggingface dataset:
The SETimes.HR Linguistically Annotated Corpus of Croatian
Authors: {\v{Z}}eljko Agi{\'c}, Nikola Ljube{\v{s}}i{\'c}
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: All, Boundary Detection, Dependency Parsing, Lemmatization, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Part-Of-Speech Tagging

We present SETimes.HR â€• the first linguistically annotated corpus of Croatian that is freely available for all purposes. The corpus is built on top of the SETimes parallel corpus of nine Southeast European languages and English. It is manually annotated for lemmas, morphosyntactic tags, named entities and dependency syntax. We couple the corpus with domain-sensitive test sets for Croatian and Serbian to support direct model transfer evaluation between these closely related languages. We build and evaluate statistical models for lemmatization, morphosyntactic tagging, named entity recognition and dependency parsing on top of SETimes.HR and the test sets, providing the state of the art in all the tasks. We make all resources presented in the paper freely available under a very permissive licensing scheme.
ACTIV-ES: a comparable, cross-dialect corpus of `everyday' Spanish from Argentina, Mexico, and Spain
Authors: Jerid Francom, Mans Hulden, Adam Ussishkin
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Part-Of-Speech Tagging

Corpus resources for Spanish have proved invaluable for a number of applications in a wide variety of fields. However, a majority of resources are based on formal, written language and/or are not built to model language variation between varieties of the Spanish language, despite the fact that most language in everyday use is informal/ dialogue-based and shows rich regional variation. This paper outlines the development and evaluation of the ACTIV-ES corpus, a first-step to produce a comparable, cross-dialect corpus representative of the everyday language of various regions of the Spanish-speaking world.
Multiple Choice Question Corpus Analysis for Distractor Characterization
Authors: Van-Minh Pho, Thibault Andr{\'e}, Anne-Laure Ligozat, Brigitte Grau, Gabriel Illouz, Thomas Fran{\c{c}}ois
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Multiple-choice, Reading Comprehension

In this paper, we present a study of MCQ aiming to define criteria in order to automatically select distractors. We are aiming to show that distractor editing follows rules like syntactic and semantic homogeneity according to associated answer, and the possibility to automatically identify this homogeneity. Manual analysis shows that homogeneity rule is respected to edit distractors and automatic analysis shows the possibility to reproduce these criteria. These ones can be used in future works to automatically select distractors, with the combination of other criteria.
Croatian Dependency Treebank 2.0: New Annotation Guidelines for Improved Parsing
Authors: {\v{Z}}eljko Agi{\'c}, Da{\v{s}}a Berovi{\'c}, Danijela Merkler, Marko Tadi{\'c}
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Dependency Parsing

We present a new version of the Croatian Dependency Treebank. It constitutes a slight departure from the previously closely observed Prague Dependency Treebank syntactic layer annotation guidelines as we introduce a new subset of syntactic tags on top of the existing tagset. These new tags are used in explicit annotation of subordinate clauses via subordinate conjunctions. Introducing the new annotation to Croatian Dependency Treebank, we also modify head attachment rules addressing subordinate conjunctions and subordinate clause predicates. In an experiment with data-driven dependency parsing, we show that implementing these new annotation guidelines leeds to a statistically significant improvement in parsing accuracy. We also observe a substantial improvement in inter-annotator agreement, facilitating more consistent annotation in further treebank development.
Euronews: a multilingual speech corpus for ASR
Authors: Roberto Gretter
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Machine Translation, speech-recognition, Speech Recognition

In this paper we present a multilingual speech corpus, designed for Automatic Speech Recognition (ASR) purposes. Data come from the portal Euronews and were acquired both from the Web and from TV. The corpus includes data in 10 languages (Arabic, English, French, German, Italian, Polish, Portuguese, Russian, Spanish and Turkish) and was designed both to train AMs and to evaluate ASR performance. For each language, the corpus is composed of about 100 hours of speech for training (60 for Polish) and about 4 hours, manually transcribed, for testing. Training data include the audio, some reference text, the ASR output and their alignment. We plan to make public at least part of the benchmark in view of a multilingual ASR benchmark for IWSLT 2014.
Constituency Parsing of Bulgarian: Word- vs Class-based Parsing
Authors: Masood Ghayoomi, Kiril Simov, Petya Osenova
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Clustering, Constituency Parsing, Dependency Parsing, LEMMA, POS

In this paper, we report the obtained results of two constituency parsers trained with BulTreeBank, an HPSG-based treebank for Bulgarian. To reduce the data sparsity problem, we propose using the Brown word clustering to do an off-line clustering and map the words in the treebank to create a class-based treebank. The observations show that when the classes outnumber the POS tags, the results are better. Since this approach adds on another dimension of abstraction (in comparison to the lemma), its coarse-grained representation can be used further for training statistical parsers.
A Multimodal Corpus of Rapid Dialogue Games
Authors: Maike Paetzel, David Nicolas Racca, David DeVault
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Dialogue Management, Management, Natural Language Understanding, Question Answering, speech-recognition, Speech Recognition, Spoken Dialogue Systems, Text Generation

This paper presents a multimodal corpus of spoken human-human dialogues collected as participants played a series of Rapid Dialogue Games (RDGs). The corpus consists of a collection of about 11 hours of spoken audio, video, and Microsoft Kinect data taken from 384 game interactions (dialogues). The games used for collecting the corpus required participants to give verbal descriptions of linguistic expressions or visual images and were specifically designed to engage players in a fast-paced conversation under time pressure. As a result, the corpus contains many examples of participants attempting to communicate quickly in specific game situations, and it also includes a variety of spontaneous conversational phenomena such as hesitations, filled pauses, overlapping speech, and low-latency responses. The corpus has been created to facilitate research in incremental speech processing for spoken dialogue systems. Potentially, the corpus could be used in several areas of speech and language research, including speech recognition, natural language understanding, natural language generation, and dialogue management.
New Spanish speech corpus database for the analysis of people suffering from Parkinson's disease
Authors: Juan Rafael Orozco-Arroyave, Juli{\'a}n David Arias-Londo{\~n}o, Jes{\'u}s Francisco Vargas-Bonilla, Mar{\'\i}a Claudia Gonz{\'a}lez-R{\'a}tiva, Elmar N{\"o}th
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

ParkinsonÂ’s disease (PD) is the second most prevalent neurodegenerative disorder after Alzheimer{'}s, affecting about 1{\%} of the people older than 65 and about 89{\%} of the people with PD develop different speech disorders. Different researchers are currently working in the analysis of speech of people with PD, including the study of different dimensions in speech such as phonation, articulation,prosody and intelligibility. The study of phonation and articulation has been addressed mainly considering sustained vowels; however, the analysis of prosody and intelligibility requires the inclusion of words, sentences and monologue. In this paper we present a new database with speech recordings of 50 patients with PD and their respective healthy controls, matched by age and gender. All of the participants are Spanish native speakers and the recordings were collected following a protocol that considers both technical requirements and several recommendations given by experts in linguistics, phoniatry and neurology. This corpus includes tasks such as sustained phonations of the vowels, diadochokinetic evaluation, 45 words, 10 sentences, a reading text and a monologue. The paper also includes results of the characterization of the Spanish vowels considering different measures used in other works to characterize different speech impairments.
Thomas Aquinas in the T\"uNDRA: Integrating the Index Thomisticus Treebank into CLARIN-D
Authors: Scott Martens, Marco Passarotti
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Retrieval

This paper describes the integration of the Index Thomisticus Treebank (IT-TB) into the web-based treebank search and visualization application TueNDRA (Tuebingen aNnotated Data Retrieval {\&} Analysis). TueNDRA was originally designed to provide access via the Internet to constituency treebanks and to tools for searching and visualizing them, as well as tabulating statistics about their contents. TueNDRA has now been extended to also provide full support for dependency treebanks with non-projective dependencies, in order to integrate the IT-TB and future treebanks with similar properties. These treebanks are queried using an adapted form of the TIGERSearch query language, which can search both hierarchical and sequential information in treebanks in a single query. As a web application, making the IT-TB accessible via TueNDRA makes the treebank and the tools to use of it available to a large community without having to distribute software and show users how to install it.
Identification of Technology Terms in Patents
Authors: Peter Anick, Marc Verhagen, James Pustejovsky
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: BIG-bench Machine Learning

Natural language analysis of patents holds promise for the development of tools designed to assist analysts in the monitoring of emerging technologies. One component of such tools is the identification of technology terms. We describe an approach to the discovery of technology terms using supervised machine learning and evaluate its performance on subsets of patents in three languages: English, German, and Chinese.
Towards Linked Hypernyms Dataset 2.0: complementing DBpedia with hypernym discovery
Authors: Tom{\'a}{\v{s}} Kliegr, Ond{\v{r}}ej Zamazal
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Articles, Hypernym Discovery, Specificity, Vocal Bursts Type Prediction

This paper presents a statistical type inference algorithm for ontology alignment, which assigns DBpedia entities with a new type (class). To infer types for a specific entity, the algorithm first identifies types that co-occur with the type the entity already has, and subsequently prunes the set of candidates for the most confident one. The algorithm has one parameter for balancing specificity/reliability of the resulting type selection. The proposed algorithm is used to complement the types in the LHD dataset, which is RDF knowledge base populated by identifying hypernyms from the free text of Wikipedia articles. The majority of types assigned to entities in LHD 1.0 are DBpedia resources. Through the statistical type inference, the number of entities with a type from DBpedia Ontology is increased significantly: by 750 thousand entities for the English dataset, 200.000 for Dutch and 440.000 for German. The accuracy of the inferred types is at 0.65 for English (as compared to 0.86 for LHD 1.0 types). A byproduct of the mapping process is a set of 11.000 mappings from DBpedia resources to DBpedia Ontology classes with associated confidence values. The number of the resulting mappings is an order of magnitude larger than what can be achieved with standard ontology alignment algorithms (Falcon, LogMapLt and YAM++), which do not utilize the type co-occurrence information. The presented algorithm is not restricted to the LHD dataset, it can be used to address generic type inference problems in presence of class membership information for a large number of instances.
Automatic Mapping Lexical Resources: A Lexical Unit as the Keystone
Authors: Eduard Bej{\v{c}}ek, V{\'a}clava Kettnerov{\'a}, Mark{\'e}ta Lopatkov{\'a}
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

This paper presents the fully automatic linking of two valency lexicons of Czech verbs: VALLEX and PDT-VALLEX. Despite the same theoretical background adopted by these lexicons and the same linguistic phenomena they focus on, the fully automatic mapping of these resouces is not straightforward. We demonstrate that converting these lexicons into a common format represents a relatively easy part of the task whereas the automatic identification of pairs of corresponding valency frames (representing lexical units of verbs) poses difficulties. The overall achieved precision of 81{\%} can be considered satisfactory. However, the higher number of lexical units a verb has, the lower the precision of their automatic mapping usually is. Moreover, we show that especially (i) supplementing further information on lexical units and (ii) revealing and reconciling regular discrepancies in their annotations can greatly assist in the automatic merging.
TermWise: A CAT-tool with Context-Sensitive Terminological Support.
Authors: Kris Heylen, Stephen Bond, Dirk De Hertog, Ivan Vuli{\'c}, Hendrik Kockaert
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Translation

Increasingly, large bilingual document collections are being made available online, especially in the legal domain. This type of Big Data is a valuable resource that specialized translators exploit to search for informative examples of how domain-specific expressions should be translated. However, general purpose search engines are not optimized to retrieve previous translations that are maximally relevant to a translator. In this paper, we report on the TermWise project, a cooperation of terminologists, corpus linguists and computer scientists, that aims to leverage big online translation data for terminological support to legal translators at the Belgian Federal Ministry of Justice. The project developed dedicated knowledge extraction algorithms and a server-based tool to provide translators with the most relevant previous translations of domain-specific expressions relative to the current translation assignment. The functionality is implemented an extra database, a Term{\&}Phrase Memory, that is meant to be integrated with existing Computer Assisted Translation tools. In the paper, we give an overview of the system, give a demo of the user interface, we present a user-based evaluation by translators and discuss how the tool is part of the general evolution towards exploiting Big Data in translation.
Measuring the Impact of Spelling Errors on the Quality of Machine Translation
Authors: Irina Galinskaya, Valentin Gusev, Elena Mescheryakova, Mariya Shmatova
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Sentiment Analysis, Translation

In this paper we show how different types of spelling errors influence the quality of machine translation. We also propose a method to evaluate the impact of spelling errors correction on translation quality without expensive manual work of providing reference translations.
Towards Multilingual Conversations in the Medical Domain: Development of Multilingual Medical Data and A Network-based ASR System
Authors: Sakriani Sakti, Keigo Kubo, Sho Matsumiya, Graham Neubig, Tomoki Toda, Satoshi Nakamura, Fumihiro Adachi, Ryosuke Isotani
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, speech-recognition, Speech Recognition, Speech Synthesis, Speech-to-Speech Translation, Text-To-Speech Synthesis, Translation

This paper outlines the recent development on multilingual medical data and multilingual speech recognition system for network-based speech-to-speech translation in the medical domain. The overall speech-to-speech translation (S2ST) system was designed to translate spoken utterances from a given source language into a target language in order to facilitate multilingual conversations and reduce the problems caused by language barriers in medical situations. Our final system utilizes a weighted finite-state transducers with n-gram language models. Currently, the system successfully covers three languages: Japanese, English, and Chinese. The difficulties involved in connecting Japanese, English and Chinese speech recognition systems through Web servers will be discussed, and the experimental results in simulated medical conversation will also be presented.
Automatic detection of other-repetition occurrences: application to French conversational Speech
Authors: Brigitte Bigi, Roxane Bertrand, Mathilde Guardiola
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Transliteration

This paper investigates the discursive phenomenon called other-repetitions (OR), particularly in the context of spontaneous French dialogues. It focuses on their automatic detection and characterization. A method is proposed to retrieve automatically OR: this detection is based on rules that are applied on the lexical material only. This automatic detection process has been used to label other-repetitions on 8 dialogues of CID - Corpus of Interactional Data. Evaluations performed on one speaker are good with a F1-measure of 0.85. Retrieved OR occurrences are then statistically described: number of words, distance, etc.
The Slovene BNSI Broadcast News database and reference speech corpus GOS: Towards the uniform guidelines for future work
Authors: Andrej {\v{Z}}gank, Ana Zwitter Vitez, Darinka Verdonik
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

The aim of the paper is to search for common guidelines for the future development of speech databases for less resourced languages in order to make them the most useful for both main fields of their use, linguistic research and speech technologies. We compare two standards for creating speech databases, one followed when developing the Slovene speech database for automatic speech recognition â€• BNSI Broadcast News, the other followed when developing the Slovene reference speech corpus GOS, and outline possible common guidelines for future work. We also present an add-on for the GOS corpus, which enables its usage for automatic speech recognition.
From Synsets to Videos: Enriching ItalWordNet Multimodally
Authors: Roberto Bartolini, Valeria Quochi, Irene De Felice, Irene Russo, Monica Monachini
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Action Recognition, Language Acquisition, Ontology Matching, Temporal Action Localization

The paper describes the multimodal enrichment of ItalWordNet action verbsÂ’ entries by means of an automatic mapping with an ontology of action types instantiated by video scenes (ImagAct). The two resources present important differences as well as interesting complementary features, such that a mapping of these two resources can lead to a an enrichment of IWN, through the connection between synsets and videos apt to illustrate the meaning described by glosses. Here, we describe an approach inspired by ontology matching methods for the automatic mapping of ImagAct video scened onto ItalWordNet sense. The experiments described in the paper are conducted on Italian, but the same methodology can be extended to other languages for which WordNets have been created, since ImagAct is done also for English, Chinese and Spanish. This source of multimodal information can be exploited to design second language learning tools, as well as for language grounding in video action recognition and potentially for robotics.
Language Editing Dataset of Academic Texts
Authors: Vidas Daudaravi{\v{c}}ius
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Grammatical Error Correction

We describe the VTeX Language Editing Dataset of Academic Texts (LEDAT), a dataset of text extracts from scientific papers that were edited by professional native English language editors at VTeX. The goal of the LEDAT is to provide a large data resource for the development of language evaluation and grammar error correction systems for the scientific community. We describe the data collection and the compilation process of the LEDAT. The new dataset can be used in many NLP studies and applications where deeper knowledge of the academic language and language editing is required. The dataset can be used also as a knowledge base of English academic language to support many writers of scientific papers.
A Toolkit for Efficient Learning of Lexical Units for Speech Recognition
Authors: Matti Varjokallio, Mikko Kurimo
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Information Retrieval, Language Modeling, Language Modelling, Machine Translation, Segmentation, speech-recognition, Speech Recognition

String segmentation is an important and recurring problem in natural language processing and other domains. For morphologically rich languages, the amount of different word forms caused by morphological processes like agglutination, compounding and inflection, may be huge and causes problems for traditional word-based language modeling approach. Segmenting text into better modelable units is thus an important part of the modeling task. This work presents methods and a toolkit for learning segmentation models from text. The methods may be applied to lexical unit selection for speech recognition and also other segmentation tasks.
Towards Automatic Transformation between Different Transcription Conventions: Prediction of Intonation Markers from Linguistic and Acoustic Features
Authors: Yuichi Ishimoto, Tomoyuki Tsuchiya, Hanae Koiso, Yasuharu Den
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

Because of the tremendous effort required for recording and transcription, large-scale spoken language corpora have been hardly developed in Japanese, with a notable exception of the Corpus of Spontaneous Japanese (CSJ). Various research groups have individually developed conversation corpora in Japanese, but these corpora are transcribed by different conventions and have few annotations in common, and some of them lack fundamental annotations, which are prerequisites for conversation research. To solve this situation by sharing existing conversation corpora that cover diverse styles and settings, we have tried to automatically transform a transcription made by one convention into that made by another convention. Using a conversation corpus transcribed in both the Conversation-Analysis-style (CA-style) and CSJ-style, we analyzed the correspondence between CA{'}s `intonation markers{'} and CSJ{'}s `tone labels,{'} and constructed a statistical model that converts tone labels into intonation markers with reference to linguistic and acoustic features of the speech. The result showed that there is considerable variance in intonation marking even between trained transcribers. The model predicted with 85{\%} accuracy the presence of the intonation markers, and classified the types of the markers with 72{\%} accuracy.