pwc-archive/papers-with-abstracts huggingface dataset:
The D-ANS corpus: the Dublin-Autonomous Nervous System corpus of biosignal and multimodal recordings of conversational speech
Authors: Shannon Hennig, Ryad Chellali, Nick Campbell
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

Biosignals, such as electrodermal activity (EDA) and heart rate, are increasingly being considered as potential data sources to provide information about the temporal fluctuations in affective experience during human interaction. This paper describes an English-speaking, multiple session corpus of small groups of people engaged in informal, unscripted conversation while wearing wireless, wrist-based EDA sensors. Additionally, one participant per recording session wore a heart rate monitor. This corpus was collected in order to observe potential interactions between various social and communicative phenomena and the temporal dynamics of the recorded biosignals. Here we describe the communicative context, technical set-up, synchronization process, and challenges in collecting and utilizing such data. We describe the segmentation and annotations to date, including laughter annotations, and how the research community can access and collaborate on this corpus now and in the future. We believe this corpus is particularly relevant to researchers interested in unscripted social conversation as well as to researchers with a specific interest in observing the dynamics of biosignals during informal social conversation rich with examples of laughter, conversational turn-taking, and non-task-based interaction.
Annotating the MASC Corpus with BabelNet
Authors: Andrea Moro, Roberto Navigli, Francesco Maria Tucci, Rebecca J. Passonneau
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Entity Linking, Reading Comprehension, Relation Extraction, Word Sense Disambiguation

In this paper we tackle the problem of automatically annotating, with both word senses and named entities, the MASC 3.0 corpus, a large English corpus covering a wide range of genres of written and spoken text. We use BabelNet 2.0, a multilingual semantic network which integrates both lexicographic and encyclopedic knowledge, as our sense/entity inventory together with its semantic structure, to perform the aforementioned annotation task. Word sense annotated corpora have been around for more than twenty years, helping the development of Word Sense Disambiguation algorithms by providing both training and testing grounds. More recently Entity Linking has followed the same path, with the creation of huge resources containing annotated named entities. However, to date, there has been no resource that contains both kinds of annotation. In this paper we present an automatic approach for performing this annotation, together with its output on the MASC corpus. We use this corpus because its goal of integrating different types of annotations goes exactly in our same direction. Our overall aim is to stimulate research on the joint exploitation and disambiguation of word senses and named entities. Finally, we estimate the quality of our annotations using both manually-tagged named entities and word senses, obtaining an accuracy of roughly 70{\%} for both named entities and word sense annotations.
All Fragments Count in Parser Evaluation
Authors: Jasmijn Bastings, Khalil Sima{'}an
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: All, Human Parsing, Machine Translation

PARSEVAL, the default paradigm for evaluating constituency parsers, calculates parsing success (Precision/Recall) as a function of the number of matching labeled brackets across the test set. Nodes in constituency trees, however, are connected together to reflect important linguistic relations such as predicate-argument and direct-dominance relations between categories. In this paper, we present FREVAL, a generalization of PARSEVAL, where the precision and recall are calculated not only for individual brackets, but also for co-occurring, connected brackets (i.e. fragments). FREVAL fragments precision (FLP) and recall (FLR) interpolate the match across the whole spectrum of fragment sizes ranging from those consisting of individual nodes (labeled brackets) to those consisting of full parse trees. We provide evidence that FREVAL is informative for inspecting relative parser performance by comparing a range of existing parsers.
TexAFon 2.0: A text processing tool for the generation of expressive speech in TTS applications
Authors: Juan Mar{\'\i}a Garrido, Yesika Laplaza, Benjamin Kolz, Miquel Cornudella
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Sentence

This paper presents TexAfon 2.0, an improved version of the text processing tool TexAFon, specially oriented to the generation of synthetic speech with expressive content. TexAFon is a text processing module in Catalan and Spanish for TTS systems, which performs all the typical tasks needed for the generation of synthetic speech from text: sentence detection, pre-processing, phonetic transcription, syllabication, prosodic segmentation and stress prediction. These improvements include a new normalisation module for the standardisation on chat text in Spanish, a module for the detection of the expressed emotions in the input text, and a module for the automatic detection of the intended speech acts, which are briefly described in the paper. The results of the evaluations carried out for each module are also presented.
A Persian Treebank with Stanford Typed Dependencies
Authors: Mojgan Seraji, Carina Jahani, Be{\'a}ta Megyesi, Joakim Nivre
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Articles, Cultural Vocal Bursts Intensity Prediction, Sentence

We present the Uppsala Persian Dependency Treebank (UPDT) with a syntactic annotation scheme based on Stanford Typed Dependencies. The treebank consists of 6,000 sentences and 151,671 tokens with an average sentence length of 25 words. The data is from different genres, including newspaper articles and fiction, as well as technical descriptions and texts about culture and art, taken from the open source Uppsala Persian Corpus (UPC). The syntactic annotation scheme is extended for Persian to include all syntactic relations that could not be covered by the primary scheme developed for English. In addition, we present open source tools for automatic analysis of Persian containing a text normalizer, a sentence segmenter and tokenizer, a part-of-speech tagger, and a parser. The treebank and the parser have been developed simultaneously in a bootstrapping procedure. The result of a parsing experiment shows an overall labeled attachment score of 82.05{\%} and an unlabeled attachment score of 85.29{\%}. The treebank is freely available as an open source resource.
A Language-independent Approach to Extracting Derivational Relations from an Inflectional Lexicon
Authors: Marion Baranes, Beno{\^\i}t Sagot
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Morphological Analysis, Question Answering

In this paper, we describe and evaluate an unsupervised method for acquiring pairs of lexical entries belonging to the same morphological family, i.e., derivationally related words, starting from a purely inflectional lexicon. Our approach relies on transformation rules that relate lexical entries with the one another, and which are automatically extracted from the inflected lexicon based on surface form analogies and on part-of-speech information. It is generic enough to be applied to any language with a mainly concatenative derivational morphology. Results were obtained and evaluated on English, French, German and Spanish. Precision results are satisfying, and our French results favorably compare with another resource, although its construction relied on manually developed lexicographic information whereas our approach only requires an inflectional lexicon.
Named Entity Recognition on Turkish Tweets
Authors: Dilek K{\"u}{\c{c}}{\"u}k, Guillaume Jacquet, Ralf Steinberger
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Articles, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Various recent studies show that the performance of named entity recognition (NER) systems developed for well-formed text types drops significantly when applied to tweets. The only existing study for the highly inflected agglutinative language Turkish reports a drop in F-Measure from 91{\%} to 19{\%} when ported from news articles to tweets. In this study, we present a new named entity-annotated tweet corpus and a detailed analysis of the various tweet-specific linguistic phenomena. We perform comparative NER experiments with a rule-based multilingual NER system adapted to Turkish on three corpora: a news corpus, our new tweet corpus, and another tweet corpus. Based on the analysis and the experimentation results, we suggest system features required to improve NER results for social media like Twitter.
Rhapsodie: a Prosodic-Syntactic Treebank for Spoken French
Authors: Anne Lacheret, Sylvain Kahane, Julie Beliao, Anne Dister, Kim Gerdes, Jean-Philippe Goldman, Nicolas Obin, Pietr, Paola rea, Atanas Tchobanov
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

The main objective of the Rhapsodie project (ANR Rhapsodie 07 Corp-030-01) was to define rich, explicit, and reproducible schemes for the annotation of prosody and syntax in different genres ({\^A}{\mbox{$\pm$}} spontaneous, {\^A}{\mbox{$\pm$}} planned, face-to-face interviews vs. broadcast, etc.), in order to study the prosody/syntax/discourse interface in spoken French, and their roles in the segmentation of speech into discourse units (Lacheret, Kahane, {\&} Pietrandrea forthcoming). We here describe the deliverable, a syntactic and prosodic treebank of spoken French, composed of 57 short samples of spoken French (5 minutes long on average, amounting to 3 hours of speech and 33000 words), orthographically and phonetically transcribed. The transcriptions and the annotations are all aligned on the speech signal: phonemes, syllables, words, speakers, overlaps. This resource is freely available at www.projet-rhapsodie.fr. The sound samples (wav/mp3), the acoustic analysis (original F0 curve manually corrected and automatic stylized F0, pitch format), the orthographic transcriptions (txt), the microsyntactic annotations (tabular format), the macrosyntactic annotations (txt, tabular format), the prosodic annotations (xml, textgrid, tabular format), and the metadata (xml and html) can be freely downloaded under the terms of the Creative Commons licence Attribution - Noncommercial - Share Alike 3.0 France. The metadata are encoded in the IMDI-CMFI format and can be parsed on line.
The IULA Spanish LSP Treebank
Authors: Montserrat Marimon, N{\'u}ria Bel, Beatriz Fisas, Blanca Arias, Silvia V{\'a}zquez, Jorge Vivaldi, Carlos Morell, Merc{\`e} Lorente
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

This paper presents the IULA Spanish LSP Treebank, a dependency treebank of over 41,000 sentences of different domains (Law, Economy, Computing Science, Environment, and Medicine), developed in the framework of the European project METANET4U. Dependency annotations in the treebank were automatically derived from manually selected parses produced by an HPSG-grammar by a deterministic conversion algorithm that used the identifiers of grammar rules to identify the heads, the dependents, and some dependency types that were directly transferred onto the dependency structure (e.g., subject, specifier, and modifier), and the identifiers of the lexical entries to identify the argument-related dependency functions (e.g. direct object, indirect object, and oblique complement). The treebank is accessible with a browser that provides concordance-based search functions and delivers the results in two formats: (i) a column-based format, in the style of CoNLL-2006 shared task, and (ii) a dependency graph, where dependency relations are noted by an oriented arrow which goes from the dependent node to the head node. The IULA Spanish LSP Treebank is the first technical corpus of Spanish annotated at surface syntactic level following the dependency grammar theory. The treebank has been made publicly and freely available from the META-SHARE platform with a Creative Commons CC-by licence.
Morpho-Syntactic Study of Errors from Speech Recognition System
Authors: Maria Goryainova, Cyril Grouin, Sophie Rosset, Ioana Vasilescu
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Named Entity Recognition (NER), POS, Question Answering, speech-recognition, Speech Recognition

The study provides an original standpoint of the speech transcription errors by focusing on the morpho-syntactic features of the erroneous chunks and of the surrounding left and right context. The typology concerns the forms, the lemmas and the POS involved in erroneous chunks, and in the surrounding contexts. Comparison with error free contexts are also provided. The study is conducted on French. Morpho-syntactic analysis underlines that three main classes are particularly represented in the erroneous chunks: (i) grammatical words (to, of, the), (ii) auxiliary verbs (has, is), and (iii) modal verbs (should, must). Such items are widely encountered in the ASR outputs as frequent candidates to transcription errors. The analysis of the context points out that some left 3-grams contexts (e.g., repetitions, that is disfluencies, bracketing formulas such as {``}cÂ’est{''}, etc.) may be better predictors than others. Finally, the surface analysis conducted through a Levensthein distance analysis, highlighted that the most common distance is of 2 characters and mainly involves differences between inflected forms of a unique item.
Not an Interlingua, But Close: Comparison of English AMRs to Chinese and Czech
Authors: Nianwen Xue, Ond{\v{r}}ej Bojar, Jan Haji{\v{c}}, Martha Palmer, Zde{\v{n}}ka Ure{\v{s}}ov{\'a}, Xiuhong Zhang
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Semantic Parsing, Semantic Role Labeling, Translation

Abstract Meaning Representations (AMRs) are rooted, directional and labeled graphs that abstract away from morpho-syntactic idiosyncrasies such as word category (verbs and nouns), word order, and function words (determiners, some prepositions). Because these syntactic idiosyncrasies account for many of the cross-lingual differences, it would be interesting to see if this representation can serve, e.g., as a useful, minimally divergent transfer layer in machine translation. To answer this question, we have translated 100 English sentences that have existing AMRs into Chinese and Czech to create AMRs for them. A cross-linguistic comparison of English to Chinese and Czech AMRs reveals both cases where the AMRs for the language pairs align well structurally and cases of linguistic divergence. We found that the level of compatibility of AMR between English and Chinese is higher than between English and Czech. We believe this kind of comparison is beneficial to further refining the annotation standards for each of the three languages and will lead to more compatible annotation guidelines between the languages.
Annotating Relations in Scientific Articles
Authors: Adam Meyers, Giancarlo Lee, Angus Grieve-Smith, Yifan He, Harriet Taber
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Articles, Relation Extraction

Relations (ABBREVIATE, EXEMPLIFY, ORIGINATE, REL{\_}WORK, OPINION) between entities (citations, jargon, people, organizations) are annotated for PubMed scientific articles. We discuss our specifications, pre-processing and evaluation
Annotating Clinical Events in Text Snippets for Phenotype Detection
Authors: Prescott Klassen, Fei Xia, V, Lucy erwende, Meliha Yetisgen
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Pneumonia Detection

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. NLP systems that analyze the narrative data embedded in clinical artifacts such as x-ray reports can help support early detection. In this paper, we consider the importance of identifying the change of state for events - in particular, clinical events that measure and compare the multiple states of a patientÂ’s health across time. We propose a schema for event annotation comprised of five fields and create preliminary annotation guidelines for annotators to apply the schema. We then train annotators, measure their performance, and finalize our guidelines. With the complete guidelines, we then annotate a corpus of snippets extracted from chest x-ray reports in order to integrate the annotations as a new source of features for classification tasks.
Phoneme Similarity Matrices to Improve Long Audio Alignment for Automatic Subtitling
Authors: Pablo Ruiz, Aitor {\'A}lvarez, Haritz Arzelus
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Decoder, Language Modelling

Long audio alignment systems for Spanish and English are presented, within an automatic subtitling application. Language-specific phone decoders automatically recognize audio contents at phoneme level. At the same time, language-dependent grapheme-to-phoneme modules perform a transcription of the script for the audio. A dynamic programming algorithm (Hirschberg{'}s algorithm) finds matches between the phonemes automatically recognized by the phone decoder and the phonemes in the scriptÂ’s transcription. Alignment accuracy is evaluated when scoring alignment operations with a baseline binary matrix, and when scoring alignment operations with several continuous-score matrices, based on phoneme similarity as assessed through comparing multivalued phonological features. Alignment accuracy results are reported at phoneme, word and subtitle level. Alignment accuracy when using the continuous scoring matrices based on phonological similarity was clearly higher than when using the baseline binary matrix.
Use of unsupervised word classes for entity recognition: Application to the detection of disorders in clinical reports
Authors: Maria Evangelia Chatzimina, Cyril Grouin, Pierre Zweigenbaum
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Chunking, Clustering, Named Entity Recognition (NER), Word Embeddings

Unsupervised word classes induced from unannotated text corpora are increasingly used to help tasks addressed by supervised classification, such as standard named entity detection. This paper studies the contribution of unsupervised word classes to a medical entity detection task with two specific objectives: How do unsupervised word classes compare to available knowledge-based semantic classes? Does syntactic information help produce unsupervised word classes with better properties? We design and test two syntax-based methods to produce word classes: one applies the Brown clustering algorithm to syntactic dependencies, the other collects latent categories created by a PCFG-LA parser. When added to non-semantic features, knowledge-based semantic classes gain 7.28 points of F-measure. In the same context, basic unsupervised word classes gain 4.16pt, reaching 60{\%} of the contribution of knowledge-based semantic classes and outperforming Wikipedia, and adding PCFG-LA unsupervised word classes gain one more point at 5.11pt, reaching 70{\%}. Unsupervised word classes could therefore provide a useful semantic back-off in domains where no knowledge-based semantic classes are available. The combination of both knowledge-based and basic unsupervised classes gains 8.33pt. Therefore, unsupervised classes are still useful even when rich knowledge-based classes exist.
Three dimensions of the so-called ``interoperability'' of annotation schemes''
Authors: Eva Haji{\v{c}}ov{\'a}
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Relation

Interoperability of annotation schemes is one of the key words in the discussions about annotation of corpora. In the present contribution, we propose to look at the so-called interoperability from (at least) three angles, namely (i) as a relation (and possible interaction or cooperation) of different annotation schemes for different layers or phenomena of a single language, (ii) the possibility to annotate different languages by a single (modified or not) annotation scheme, and (iii) the relation between different annotation schemes for a single language, or for a single phenomenon or layer of the same language. The pros and cons of each of these aspects are discussed as well as their contribution to linguistic studies and natural language processing. It is stressed that a communication and collaboration between different annotation schemes requires an explicit specification and consistency of each of the schemes.
On Complex Word Alignment Configurations
Authors: Miriam Kaeshammer, Anika Westburg
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Translation, Word Alignment

Resources of manual word alignments contain configurations that are beyond the alignment capacity of current translation models, hence the term complex alignment configuration. They have been the matter of some debate in the machine translation community, as they call for more powerful translation models that come with further complications. In this work we investigate instances of complex alignment configurations in data sets of four different language pairs to shed more light on the nature and cause of those configurations. For the English-German alignments from Pad{\'o} and Lapata (2006), for instance, we find that only a small fraction of the complex configurations are due to real annotation errors. While a third of the complex configurations in this data set could be simplified when annotating according to a different style guide, the remaining ones are phenomena that one would like to be able to generate during translation. Those instances are mainly caused by the different word order of English and German. Our findings thus motivate further research in the area of translation beyond phrase-based and context-free translation modeling.
HFST-SweNER --- A New NER Resource for Swedish
Authors: Dimitrios Kokkinakis, Jyrki Niemi, Sam Hardwick, Krister Lind{\'e}n, Lars Borin
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Question Answering, Relation Extraction, Text Classification

Named entity recognition (NER) is a knowledge-intensive information extraction task that is used for recognizing textual mentions of entities that belong to a predefined set of categories, such as locations, organizations and time expressions. NER is a challenging, difficult, yet essential preprocessing technology for many natural language processing applications, and particularly crucial for language understanding. NER has been actively explored in academia and in industry especially during the last years due to the advent of social media data. This paper describes the conversion, modeling and adaptation of a Swedish NER system from a hybrid environment, with integrated functionality from various processing components, to the Helsinki Finite-State Transducer Technology (HFST) platform. This new HFST-based NER (HFST-SweNER) is a full-fledged open source implementation that supports a variety of generic named entity types and consists of multiple, reusable resource layers, e.g., various n-gram-based named entity lists (gazetteers).