pwc-archive/papers-with-abstracts huggingface dataset:
Machine Translationness: Machine-likeness in Machine Translation Evaluation
Authors: Joaquim Mor{\'e}, Salvador Climent
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Translation

Machine translationness (MTness) is the linguistic phenomena that make machine translations distinguishable from human translations. This paper intends to present MTness as a research object and suggests an MT evaluation method based on determining whether the translation is machine-like instead of determining its human-likeness as in evaluation current approaches. The method rates the MTness of a translation with a metric, the MTS (Machine Translationness Score). The MTS calculation is in accordance with the results of an experimental study on machine translation perception by common people. MTS proved to correlate well with human ratings on translation quality. Besides, our approach allows the performance of cheap evaluations since expensive resources (e.g. reference translations, training corpora) are not needed. The paper points out the challenge of dealing with MTness as an everyday phenomenon caused by the massive use of MT.
Towards an environment for the production and the validation of lexical semantic resources
Authors: Mika{\"e}l Morardo, {\'E}ric Villemonte de la Clergerie
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Question Answering

We present the components of a processing chain for the creation, visualization, and validation of lexical resources (formed of terms and relations between terms). The core of the chain is a component for building lexical networks relying on Harris{'} distributional hypothesis applied on the syntactic dependencies produced by the French parser FRMG on large corpora. Another important aspect concerns the use of an online interface for the visualization and collaborative validation of the resulting resources.
The Distress Analysis Interview Corpus of human and computer interviews
Authors: Jonathan Gratch, Ron artstein, Gale Lucas, Giota Stratou, Stefan Scherer, Angela Nazarian, Rachel Wood, Jill Boberg, David DeVault, Stacy Marsella, David Traum, Skip Rizzo, Louis-Philippe Morency
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

The Distress Analysis Interview Corpus (DAIC) contains clinical interviews designed to support the diagnosis of psychological distress conditions such as anxiety, depression, and post traumatic stress disorder. The interviews are conducted by humans, human controlled agents and autonomous agents, and the participants include both distressed and non-distressed individuals. Data collected include audio and video recordings and extensive questionnaire responses; parts of the corpus have been transcribed and annotated for a variety of verbal and non-verbal features. The corpus has been used to support the creation of an automated interviewer agent, and for research on the automatic identification of psychological distress.
Representing Multimodal Linguistic Annotated data
Authors: Brigitte Bigi, Tatsuya Watanabe, Laurent Pr{\'e}vot
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

The question of interoperability for linguistic annotated resources covers different aspects. First, it requires a representation framework making it possible to compare, and eventually merge, different annotation schema. In this paper, a general description level representing the multimodal linguistic annotations is proposed. It focuses on time representation and on the data content representation: This paper reconsiders and enhances the current and generalized representation of annotations. An XML schema of such annotations is proposed. A Python API is also proposed. This framework is implemented in a multi-platform software and distributed under the terms of the GNU Public License.
SWIFT Aligner, A Multifunctional Tool for Parallel Corpora: Visualization, Word Alignment, and (Morpho)-Syntactic Cross-Language Transfer
Authors: Timur Gilmanov, Olga Scrivner, S K{\"u}bler, ra
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Word Alignment, Word Sense Disambiguation

It is well known that word aligned parallel corpora are valuable linguistic resources. Since many factors affect automatic alignment quality, manual post-editing may be required in some applications. While there are several state-of-the-art word-aligners, such as GIZA++ and Berkeley, there is no simple visual tool that would enable correcting and editing aligned corpora of different formats. We have developed SWIFT Aligner, a free, portable software that allows for visual representation and editing of aligned corpora from several most commonly used formats: TALP, GIZA, and NAACL. In addition, our tool has incorporated part-of-speech and syntactic dependency transfer from an annotated source language into an unannotated target language, by means of word-alignment.
Semi-automatic annotation of the UCU accents speech corpus
Authors: Rosemary Orr, Marijn Huijbregts, Roel van Beek, Lisa Teunissen, Kate Backhouse, David van Leeuwen
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Event Detection, Speech Recognition

Annotation and labeling of speech tasks in large multitask speech corpora is a necessary part of preparing a corpus for distribution. We address three approaches to annotation and labeling: manual, semi automatic and automatic procedures for labeling the UCU Accent Project speech data, a multilingual multitask longitudinal speech corpus. Accuracy and minimal time investment are the priorities in assessing the efficacy of each procedure. While manual labeling based on aural and visual input should produce the most accurate results, this approach is error-prone because of its repetitive nature. A semi automatic event detection system requiring manual rejection of false alarms and location and labeling of misses provided the best results. A fully automatic system could not be applied to entire speech recordings because of the variety of tasks and genres. However, it could be used to annotate separate sentences within a specific task. Acoustic confidence measures can correctly detect sentences that do not match the text with an EER of 3.3{\%}
Comparative Analysis of Portuguese Named Entities Recognition Tools
Authors: Daniela Amaral, Ev Fonseca, ro, Lucelene Lopes, Renata Vieira
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: BIG-bench Machine Learning, Information Retrieval, Named Entity Recognition (NER), Part-Of-Speech Tagging

This paper describes an experiment to compare four tools to recognize named entities in Portuguese texts. The experiment was made over the HAREM corpora, a golden standard for named entities recognition in Portuguese. The tools experimented are based on natural language processing techniques and also machine learning. Specifically, one of the tools is based on Conditional random fields, an unsupervised machine learning model that has being used to named entities recognition in several languages, while the other tools follow more traditional natural language approaches. The comparison results indicate advantages for different tools according to the different classes of named entities. Despite of such balance among tools, we conclude pointing out foreseeable advantages to the machine learning based tool.
A corpus of European Portuguese child and child-directed speech
Authors: Ana L{\'u}cia Santos, Michel G{\'e}n{\'e}reux, Aida Cardoso, Celina Agostinho, Silvana Abalada
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Part-Of-Speech Tagging, POS, TAG

We present a corpus of child and child-directed speech of European Portuguese. This corpus results from the expansion of an already existing database (Santos, 2006). It includes around 52 hours of child-adult interaction and now contains 27,595 child utterances and 70,736 adult utterances. The corpus was transcribed according to the CHILDES system (Child Language Data Exchange System) and using the CLAN software (MacWhinney, 2000). The corpus itself represents a valuable resource for the study of lexical, syntax and discourse acquisition. In this paper, we also show how we used an existing part-of-speech tagger trained on written material (G{\'e}n{\'e}reux, Hendrickx {\&} Mendes, 2012) to automatically lemmatize and tag child and child-directed speech and generate a line with part-of-speech information compatible with the CLAN interface. We show that a POS-tagger trained on the analysis of written language can be exploited for the treatment of spoken material with minimal effort, with only a small number of written rules assisting the statistical model.
Using C5.0 and Exhaustive Search for Boosting Frame-Semantic Parsing Accuracy
Authors: Guntis Barzdins, Didzis Gosko, Laura Rituma, Peteris Paikens
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Semantic Parsing, Semantic Role Labeling

Frame-semantic parsing is a kind of automatic semantic role labeling performed according to the FrameNet paradigm. The paper reports a novel approach for boosting frame-semantic parsing accuracy through the use of the C5.0 decision tree classifier, a commercial version of the popular C4.5 decision tree classifier, and manual rule enhancement. Additionally, the possibility to replace C5.0 by an exhaustive search based algorithm (nicknamed C6.0) is described, leading to even higher frame-semantic parsing accuracy at the expense of slightly increased training time. The described approach is particularly efficient for languages with small FrameNet annotated corpora as it is for Latvian, which is used for illustration. Frame-semantic parsing accuracy achieved for Latvian through the C6.0 algorithm is on par with the state-of-the-art English frame-semantic parsers. The paper includes also a frame-semantic parsing use-case for extracting structured information from unstructured newswire texts, sometimes referred to as bridging of the semantic gap.
`interHist' \`I\P an interactive visual interface for corpus exploration
Authors: Verena Lyding, Lionel Nicolas, Egon Stemle
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Part-Of-Speech Tagging

In this article, we present interHist, a compact visualization for the interactive exploration of results to complex corpus queries. Integrated with a search interface to the PAISA corpus of Italian web texts, interHist aims at facilitating the exploration of large results sets to linguistic corpus searches. This objective is approached by providing an interactive visual overview of the data, which supports the user-steered navigation by means of interactive filtering. It allows to dynamically switch between an overview on the data and a detailed view on results in their immediate textual context, thus helping to detect and inspect relevant hits more efficiently. We provide background information on corpus linguistics and related work on visualizations for language and linguistic data. We introduce the architecture of interHist, by detailing the data structure it relies on, describing the visualization design and providing technical details of the implementation and its integration with the corpus querying environment. Finally, we illustrate its usage by presenting a use case for the analysis of the composition of Italian noun phrases.
Identification of Multiword Expressions in the brWaC
Authors: Rodrigo Boos, Kassius Prestes, Aline Villavicencio
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Information Retrieval, Machine Translation, Spelling Correction

Although corpus size is a well known factor that affects the performance of many NLP tasks, for many languages large freely available corpora are still scarce. In this paper we describe one effort to build a very large corpus for Brazilian Portuguese, the brWaC, generated following the Web as Corpus kool initiative. To indirectly assess the quality of the resulting corpus we examined the impact of corpus origin in a specific task, the identification of Multiword Expressions with association measures, against a standard corpus. Focusing on nominal compounds, the expressions obtained from each corpus are of comparable quality and indicate that corpus origin has no impact on this task.
Collocation or Free Combination? --- Applying Machine Translation Techniques to identify collocations in Japanese
Authors: Lis Pereira, Elga Strafella, Yuji Matsumoto
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Translation

This work presents an initial investigation on how to distinguish collocations from free combinations. The assumption is that, while free combinations can be literally translated, the overall meaning of collocations is different from the sum of the translation of its parts. Based on that, we verify whether a machine translation system can help us perform such distinction. Results show that it improves the precision compared with standard methods of collocation identification through statistical association measures.
Extrinsic Corpus Evaluation with a Collocation Dictionary Task
Authors: Adam Kilgarriff, Pavel Rychl{\'y}, Milo{\v{s}} Jakub{\'\i}{\v{c}}ek, Vojt{\v{e}}ch Kov{\'a}{\v{r}}, V{\'\i}t Baisa, Lucia Kocincov{\'a}
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

The NLP researcher or application-builder often wonders {``}what corpus should I use, or should I build one of my own? If I build one of my own, how will I know if I have done a good job?{''} Currently there is very little help available for them. They are in need of a framework for evaluating corpora. We develop such a framework, in relation to corpora which aim for good coverage of `general language{'}. The task we set is automatic creation of a publication-quality collocations dictionary. For a sample of 100 headwords of Czech and 100 of English, we identify a gold standard dataset of (ideally) all the collocations that should appear for these headwords in such a dictionary. The datasets are being made available alongside this paper. We then use them to determine precision and recall for a range of corpora, with a range of parameters.
AusTalk: an audio-visual corpus of Australian English
Authors: Dominique Estival, Steve Cassidy, Felicity Cox, Denis Burnham
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

This paper describes the AusTalk corpus, which was designed and created through the Big ASC, a collaborative project with the two main goals of providing a standardised infrastructure for audio-visual recordings in Australia and of producing a large audio-visual corpus of Australian English, with 3 hours of AV recordings for 1000 speakers. We first present the overall project, then describe the corpus itself and its components, the strict data collection protocol with high levels of standardisation and automation, and the processes put in place for quality control. We also discuss the annotation phase of the project, along with its goals and challenges; a major contribution of the project has been to explore procedures for automating annotations and we present our solutions. We conclude with the current status of the corpus and with some examples of research already conducted with this new resource. AusTalk is one of the corpora included in the HCS vLab, which is briefly sketched in the conclusion.
Comprehensive Annotation of Multiword Expressions in a Social Web Corpus
Authors: Nathan Schneider, Spencer Onuffer, Nora Kazour, Emily Danchik, Michael T. Mordowanec, Henrietta Conrad, Noah A. Smith
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Diversity, Language Acquisition, Machine Translation, Sentence

Multiword expressions (MWEs) are quite frequent in languages such as English, but their diversity, the scarcity of individual MWE types, and contextual ambiguity have presented obstacles to corpus-based studies and NLP systems addressing them as a class. Here we advocate for a comprehensive annotation approach: proceeding sentence by sentence, our annotators manually group tokens into MWEs according to guidelines that cover a broad range of multiword phenomena. Under this scheme, we have fully annotated an English web corpus for multiword expressions, including those containing gaps.
Automatic semantic relation extraction from Portuguese texts
Authors: Leonardo Sameshima Taba, Helena Caseli
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: BIG-bench Machine Learning, Common Sense Reasoning, Relation, Relation Extraction

Nowadays we are facing a growing demand for semantic knowledge in computational applications, particularly in Natural Language Processing (NLP). However, there aren{'}t sufficient human resources to produce that knowledge at the same rate of its demand. Considering the Portuguese language, which has few resources in the semantic area, the situation is even more alarming. Aiming to solve that problem, this work investigates how some semantic relations can be automatically extracted from Portuguese texts. The two main approaches investigated here are based on (i) textual patterns and (ii) machine learning algorithms. Thus, this work investigates how and to which extent these two approaches can be applied to the automatic extraction of seven binary semantic relations (is-a, part-of, location-of, effect-of, property-of, made-of and used-for) in Portuguese texts. The results indicate that machine learning, in particular Support Vector Machines, is a promising technique for the task, although textual patterns presented better results for the used-for relation.
A Multidialectal Parallel Corpus of Arabic
Authors: Houda Bouamor, Nizar Habash, Kemal Oflazer
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Dialect Identification, Machine Translation, Translation

The daily spoken variety of Arabic is often termed the colloquial or dialect form of Arabic. There are many Arabic dialects across the Arab World and within other Arabic speaking communities. These dialects vary widely from region to region and to a lesser extent from city to city in each region. The dialects are not standardized, they are not taught, and they do not have official status. However they are the primary vehicles of communication (face-to-face and recently, online) and have a large presence in the arts as well. In this paper, we present the first multidialectal Arabic parallel corpus, a collection of 2,000 sentences in Standard Arabic, Egyptian, Tunisian, Jordanian, Palestinian and Syrian Arabic, in addition to English. Such parallel data does not exist naturally, which makes this corpus a very valuable resource that has many potential applications such as Arabic dialect identification and machine translation.
Transfer learning of feedback head expressions in Danish and Polish comparable multimodal corpora
Authors: Costanza Navarretta, Magdalena Lis
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Transfer Learning

The paper is an investigation of the reusability of the annotations of head movements in a corpus in a language to predict the feedback functions of head movements in a comparable corpus in another language. The two corpora consist of naturally occurring triadic conversations in Danish and Polish, which were annotated according to the same scheme. The intersection of common annotation features was used in the experiments. A Na{\"\i}ve Bayes classifier was trained on the annotations of a corpus and tested on the annotations of the other corpus. Training and test datasets were then reversed and the experiments repeated. The results show that the classifier identifies more feedback behaviours than the majority baseline in both cases and the improvements are significant. The performance of the classifier decreases significantly compared with the results obtained when training and test data belong to the same corpus. Annotating multimodal data is resource consuming, thus the results are promising. However, they also confirm preceding studies that have identified both similarities and differences in the use of feedback head movements in different languages. Since our datasets are small and only regard a communicative behaviour in two languages, the experiments should be tested on more data types.
Comparing two acquisition systems for automatically building an English---Croatian parallel corpus from multilingual websites
Authors: Miquel Espl{\`a}-Gomis, Filip Klubi{\v{c}}ka, Nikola Ljube{\v{s}}i{\'c}, Sergio Ortiz-Rojas, Vassilis Papavassiliou, Prokopis Prokopidis
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Information Retrieval, Machine Translation, Natural Language Inference

In this paper we compare two tools for automatically harvesting bitexts from multilingual websites: bitextor and ILSP-FC. We used both tools for crawling 21 multilingual websites from the tourism domain to build a domain-specific Englishâ€•Croatian parallel corpus. Different settings were tried for both tools and 10,662 unique document pairs were obtained. A sample of about 10{\%} of them was manually examined and the success rate was computed on the collection of pairs of documents detected by each setting. We compare the performance of the settings and the amount of different corpora detected by each setting. In addition, we describe the resource obtained, both by the settings and through the human evaluation, which has been released as a high-quality parallel corpus.
Hashtag Occurrences, Layout and Translation: A Corpus-driven Analysis of Tweets Published by the Canadian Government
Authors: Fabrizio Gotti, Phillippe Langlais, Atefeh Farzindar
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Information Retrieval, Machine Translation, Opinion Mining, Translation

We present an aligned bilingual corpus of 8758 tweet pairs in French and English, derived from Canadian government agencies. Hashtags appear in a tweet{'}s prologue, announcing its topic, or in the tweet{'}s text in lieu of traditional words, or in an epilogue. Hashtags are words prefixed with a pound sign in 80{\%} of the cases. The rest is mostly multiword hashtags, for which we describe a segmentation algorithm. A manual analysis of the bilingual alignment of 5000 hashtags shows that 5{\%} (French) to 18{\%} (English) of them don{'}t have a counterpart in their containing tweet{'}s translation. This analysis shows that 80{\%} of multiword hashtags are correctly translated by humans, and that the mistranslation of the rest may be due to incomplete translation directives regarding social media. We show how these resources and their analysis can guide the design of a machine translation pipeline, and its evaluation. A baseline system implementing a tweet-specific tokenizer yields promising results. The system is improved by translating epilogues, prologues, and text separately. We attempt to feed the SMT engine with the original hashtag and some alternatives ({``}dehashed{''} version or a segmented version of multiword hashtags), but translation quality improves at the cost of hashtag recall.
Towards an Integration of Syntactic and Temporal Annotations in Estonian
Authors: Siim Orasmaa
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Question Answering, Text Summarization

We investigate the question how manually created syntactic annotations can be used to analyse and improve consistency in manually created temporal annotations. Our work introduces an annotation project for Estonian, where temporal annotations in TimeML framework were manually added to a corpus containing gold standard morphological and dependency syntactic annotations. In the first part of our work, we evaluate the consistency of manual temporal annotations, focusing on event annotations. We use syntactic annotations to distinguish different event annotation models, and we observe highest inter-annotator agreements on models representing prototypical events (event verbs and events being part of the syntactic predicate of clause). In the second part of our work, we investigate how to improve consistency between syntactic and temporal annotations. We test on whether syntactic annotations can be used to validate temporal annotations: to find missing or partial annotations. Although the initial results indicate that such validation is promising, we also note that a better bridging between temporal (semantic) and syntactic annotations is needed for a complete automatic validation.