pwc-archive/papers-with-abstracts huggingface dataset:
Corpus-based Referring Expressions Generation
Authors: Hilder Pereira, Eder Novais, Andr{\'e} Mariotti, Iv Paraboni, r{\'e}
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Attribute, Referring Expression, Text Generation

In Natural Language Generation, the task of attribute selection (AS) consists of determining the appropriate attribute-value pairs (or semantic properties) that represent the contents of a referring expression. Existing work on AS includes a wide range of algorithmic solutions to the problem, but the recent availability of corpora annotated with referring expressions data suggests that corpus-based AS strategies become possible as well. In this work we tentatively discuss a number of AS strategies using both semantic and surface information obtained from a corpus of this kind. Relying on semantic information, we attempt to learn both global and individual AS strategies that could be applied to a standard AS algorithm in order to generate descriptions found in the corpus. As an alternative, and perhaps less traditional approach, we also use surface information to build statistical language models of the referring expressions that are most likely to occur in the corpus, and let the model probabilities guide attribute selection.
Portuguese Text Generation from Large Corpora
Authors: Eder Novais, Iv Paraboni, r{\'e}, Douglas Silva
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Articles, Language Modelling, Text Generation

In the implementation of a surface realisation engine, many of the computational techniques seen in other AI fields have been widely applied. Among these, the use of statistical methods has been particularly successful, as in the so-called 'generate-and-select', or 2-stages architectures. Systems of this kind produce output strings from possibly underspecified input data by over-generating a large number of alternative realisations (often including ungrammatical candidate sentences.) These are subsequently ranked with the aid of a statistical language model, and the most likely candidate is selected as the output string. Statistical approaches may however face a number of difficulties. Among these, there is the issue of data sparseness, a problem that is particularly evident in cases such as our target language - Brazilian Portuguese - which is not only morphologically-rich, but relatively poor in NLP resources such as large, publicly available corpora. In this work we describe a first implementation of a shallow surface realisation system for this language that deals with the issue of data sparseness by making use of factored language models built from a (relatively) large corpus of Brazilian newspapers articles.
SUMAT: Data Collection and Parallel Corpus Compilation for Machine Translation of Subtitles
Authors: Volha Petukhova, Rodrigo Agerri, Mark Fishel, Sergio Penkale, Arantza del Pozo, Mirjam Sepesy Mau{\v{c}}ec, Andy Way, Panayota Georgakopoulou, Martin Volk
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Machine Translation, Translation

Subtitling and audiovisual translation have been recognized as areas that could greatly benefit from the introduction of Statistical Machine Translation (SMT) followed by post-editing, in order to increase efficiency of subtitle production process. The FP7 European project SUMAT (An Online Service for SUbtitling by MAchine Translation: http://www.sumat-project.eu) aims to develop an online subtitle translation service for nine European languages, combined into 14 different language pairs, in order to semi-automate the subtitle translation processes of both freelance translators and subtitling companies on a large scale. In this paper we discuss the data collection and parallel corpus compilation for training SMT systems, which includes several procedures such as data partition, conversion, formatting, normalization and alignment. We discuss in detail each data pre-processing step using various approaches. Apart from the quantity (around 1 million subtitles per language pair), the SUMAT corpus has a number of very important characteristics. First of all, high quality both in terms of translation and in terms of high-precision alignment of parallel documents and their contents has been achieved. Secondly, the contents are provided in one consistent format and encoding. Finally, additional information such as type of content in terms of genres and domain is available.
Affective Common Sense Knowledge Acquisition for Sentiment Analysis
Authors: Erik Cambria, Yunqing Xia, Amir Hussain
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Common Sense Reasoning, Decision Making, Information Retrieval, Natural Language Inference, Opinion Mining, Reading Comprehension, Sentiment Analysis

Thanks to the advent of Web 2.0, the potential for opinion sharing today is unmatched in history. Making meaning out of the huge amount of unstructured information available online, however, is extremely difficult as web-contents, despite being perfectly suitable for human consumption, still remain hardly accessible to machines. To bridge the cognitive and affective gap between word-level natural language data and the concept-level sentiments conveyed by them, affective common sense knowledge is needed. In sentic computing, the general common sense knowledge contained in ConceptNet is usually exploited to spread affective information from selected affect seeds to other concepts. In this work, besides exploiting the emotional content of the Open Mind corpus, we also collect new affective common sense knowledge through label sequential rules, crowd sourcing, and games-with-a-purpose techniques. In particular, we develop Open Mind Common Sentics, an emotion-sensitive IUI that serves both as a platform for affective common sense acquisition and as a publicly available NLP tool for extracting the cognitive and affective information associated with short texts.
Body-conductive acoustic sensors in human-robot communication
Authors: Panikos Heracleous, Carlos Ishi, Takahiro Miyashita, Norihiro Hagita
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5

In this study, the use of alternative acoustic sensors in human-robot communication is investigated. In particular, a Non-Audible Murmur (NAM) microphone was applied in teleoperating Geminoid HI-1 robot in noisy environments. The current study introduces the methodology and the results of speech intelligibility subjective tests when a NAM microphone was used in comparison with using a standard microphone. The results show the advantage of using NAM microphone when the operation takes place in adverse environmental conditions. In addition, the effect of Geminoid's lip movements on speech intelligibility is also investigated. Subjective speech intelligibility tests show that the operator's speech can be perceived with higher intelligibility scores when operator's audio speech is perceived along with the lip movements of robots.
From keystrokes to annotated process data: Enriching the output of Inputlog with linguistic information
Authors: Lieve Macken, Veronique Hoste, Mari{\"e}lle Leijten, Luuk Van Waes
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Speech Recognition

Keystroke logging tools are a valuable aid to monitor written language production. These tools record all keystrokes, including backspaces and deletions together with timing information. In this paper we report on an extension to the keystroke logging program Inputlog in which we aggregate the logged process data from the keystroke (character) level to the word level. The logged process data are further enriched with different kinds of linguistic information: part-of-speech tags, lemmata, chunk boundaries, syllable boundaries and word frequency. A dedicated parser has been developed that distils from the logged process data word-level revisions, deleted fragments and final product data. The linguistically-annotated output will facilitate the linguistic analysis of the logged data and will provide a valuable basis for more linguistically-oriented writing process research. The set-up of the extension to Inputlog is largely language-independent. As proof-of-concept, the extension has been developed for English and Dutch. Inputlog is freely available for research purposes.
A Comparative Evaluation of Word Sense Disambiguation Algorithms for German
Authors: Verena Henrich, Erhard Hinrichs
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Word Sense Disambiguation

The present paper explores a wide range of word sense disambiguation (WSD) algorithms for German. These WSD algorithms are based on a suite of semantic relatedness measures, including path-based, information-content-based, and gloss-based methods. Since the individual algorithms produce diverse results in terms of precision and thus complement each other well in terms of coverage, a set of combined algorithms is investigated and compared in performance to the individual algorithms. Among the single algorithms considered, a word overlap method derived from the Lesk algorithm that uses Wiktionary glosses and GermaNet lexical fields yields the best F-score of 56.36. This result is outperformed by a combined WSD algorithm that uses weighted majority voting and obtains an F-score of 63.59. The WSD experiments utilize the German wordnet GermaNet as a sense inventory as well as WebCAGe (short for: Web-Harvested Corpus Annotated with GermaNet Senses), a newly constructed, sense-annotated corpus for this language. The WSD experiments also confirm that WSD performance is lower for words with fine-grained sense distinctions compared to words with coarse-grained senses.
SemScribe: Natural Language Generation for Medical Reports
Authors: Sebastian Varges, Heike Bieler, Manfred Stede, Lukas C. Faulstich, Kristin Irsig, Malik Atalla
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Referring Expression, Referring expression generation, Text Generation

Natural language generation in the medical domain is heavily influenced by domain knowledge and genre-specific text characteristics. We present SemScribe, an implemented natural language generation system that produces doctor's letters, in particular descriptions of cardiological findings. Texts in this domain are characterized by a high density of information and a relatively telegraphic style. Domain knowledge is encoded in a medical ontology of about 80,000 concepts. The ontology is used in particular for concept generalizations during referring expression generation. Architecturally, the system is a generation pipeline that uses a corpus-informed syntactic frame approach for realizing sentences appropriate to the domain. The system reads XML documents conforming to the HL7 Clinical Document Architecture (CDA) Standard and enhances them with generated text and references to the used data elements. We conducted a first clinical trial evaluation with medical staff and report on the findings.
Automatic Annotation and Manual Evaluation of the Diachronic German Corpus T\"uBa-D/DC
Authors: Erhard Hinrichs, Thomas Zastrow
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5

This paper presents the Tu{\`I}bingen Baumbank des Deutschen Diachron (Tu{\`I}Ba-D/DC), a linguistically annotated corpus of selected diachronic materials from the German Gutenberg Project. It was automatically annotated by a suite of NLP tools integrated into WebLicht, the linguistic chaining tool used in CLARIN-D. The annotation quality has been evaluated manually for a subcorpus ranging from Middle High German to Modern High German. The integration of the Tu{\`I}Ba-D/DC into the CLARIN-D infrastructure includes metadata provision and harvesting as well as sustainable data storage in the Tu{\`I}bingen CLARIN-D center. The paper further provides an overview of the possibilities of accessing the Tu{\`I}Ba-D/DC data. Methods for full-text search of the metadata and object data and for annotation-based search of the object data are described in detail. The WebLicht Service Oriented Architecture is used as an integrated environment for annotation based search of the Tu{\`I}Ba-D/DC. WebLicht thus not only serves as the annotation platform for the Tu{\`I}Ba-D/DC, but also as a generic user interface for accessing and visualizing it.
A new dynamic approach for lexical networks evaluation
Authors: Alain Joubert, Mathieu Lafourcade
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5

Since September 2007, a large scale lexical network for French is under construction with methods based on popular consensus by means of games (under the JeuxDeMots project). To assess the resource quality, we decided to adopt an approach similar to its construction, that is to say an evaluation by laymen on open class vocabulary with a Tip of the Tongue tool.
Grammatical Error Annotation for Korean Learners of Spoken English
Authors: Hongsuck Seo, Kyusong Lee, Gary Geunbae Lee, Soo-Ok Kweon, Hae-Ri Kim
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Grammatical Error Detection

The goal of our research is to build a grammatical error-tagged corpus for Korean learners of Spoken English dubbed Postech Learner Corpus. We collected raw story-telling speech from Korean university students. Transcription and annotation using the Cambridge Learner Corpus tagset were performed by six Korean annotators fluent in English. For the annotation of the corpus, we developed an annotation tool and a validation tool. After comparing human annotation with machine-recommended error tags, unmatched errors were rechecked by a native annotator. We observed different characteristics between the spoken language corpus built in this study and an existing written language corpus.
Adaptive Speech Understanding for Intuitive Model-based Spoken Dialogues
Authors: Tobias Heinroth, Maximilian Grotz, Florian Nothdurft, Wolfgang Minker
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Dialogue Management, Information Retrieval, Keyword Spotting

In this paper we present three approaches towards adaptive speech understanding. The target system is a model-based Adaptive Spoken Dialogue Manager, the OwlSpeak ASDM. We enhanced this system in order to properly react on non-understandings in real-life situations where intuitive communication is required. OwlSpeak provides a model-based spoken interface to an Intelligent Environment depending on and adapting to the current context. It utilises a set of ontologies used as dialogue models that can be combined dynamically during runtime. Besides the benefits the system showed in practice, real-life evaluations also conveyed some limitations of the model-based approach. Since it is unfeasible to model all variations of the communication between the user and the system beforehand, various situations where the system did not correctly understand the user input have been observed. Thus we present three enhancements towards a more sophisticated use of the ontology-based dialogue models and show how grammars may dynamically be adapted in order to understand intuitive user utterances. The evaluation of our approaches revealed the incorporation of a lexical-semantic knowledgebase into the recognition process to be the most promising approach.
First Steps towards the Semi-automatic Development of a Wordformation-based Lexicon of Latin
Authors: Marco Passarotti, Francesco Mambrini
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Information Retrieval, Lemmatization, Morphological Tagging, POS, POS Tagging, Retrieval

Although lexicography of Latin has a long tradition dating back to ancient grammarians, and almost all Latin grammars devote to wordformation at least one part of the section(s) concerning morphology, none of the today available lexical resources and NLP tools of Latin feature a wordformation-based organization of the Latin lexicon. In this paper, we describe the first steps towards the semi-automatic development of a wordformation-based lexicon of Latin, by detailing several problems occurring while building the lexicon and presenting our solutions. Developing a wordformation-based lexicon of Latin is nowadays of outmost importance, as the last years have seen a large growth of annotated corpora of Latin texts of different eras. While these corpora include lemmatization, morphological tagging and syntactic analysis, none of them features segmentation of the word forms and wordformation relations between the lexemes. This restricts the browsing and the exploitation of the annotated data for linguistic research and NLP tasks, such as information retrieval and heuristics in PoS tagging of unknown words.
The Use of Parallel and Comparable Data for Analysis of Abstract Anaphora in German and English
Authors: Stefanie Dipper, Melanie Seiss, Heike Zinsmeister
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Abstract Anaphora Resolution, Sentence, Translation

Parallel corpora ― original texts aligned with their translations ― are a widely used resource in computational linguistics. Translation studies have shown that translated texts often differ systematically from comparable original texts. Translators tend to be faithful to structures of the original texts, resulting in a ''''''``shining through'''''''' of the original language preferences in the translated text. Translators also tend to make their translations most comprehensible with the effect that translated texts can be more explicit than their source texts. Motivated by the need to use a parallel resource for cross-linguistic feature induction in abstract anaphora resolution, this paper investigates properties of English and German texts in the Europarl corpus, taking into account both general features such as sentence length as well as task-dependent features such as the distribution of demonstrative noun phrases. The investigation is based on the entire Europarl corpus as well as on a small subset thereof, which has been manually annotated. The results indicate English translated texts are sufficiently ''''''``authentic'''''''' to be used as training data for anaphora resolution; results for German texts are less conclusive, though.
A Tool for Extracting Conversational Implicatures
Authors: Marta Tatu, Dan Moldovan
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Common Sense Reasoning, Implicatures, World Knowledge

Explicitly conveyed knowledge represents only a portion of the information communicated by a text snippet. Automated mechanisms for deriving explicit information exist; however, the implicit assumptions and default inferences that capture our intuitions about a normal interpretation of a communication remain hidden for automated systems, despite the communication participants' ease of grasping the complete meaning of the communication. In this paper, we describe a reasoning framework for the automatic identification of conversational implicatures conveyed by real-world English and Arabic conversations carried via twitter.com. Our system transforms given utterances into deep semantic logical forms. It produces a variety of axioms that identify lexical connections between concepts, define rules of combining semantic relations, capture common-sense world knowledge, and encode Grice's Conversational Maxims. By exploiting this rich body of knowledge and reasoning within the context of the conversation, our system produces entailments and implicatures conveyed by analyzed utterances with an F-measure of 70.42{\%} for English conversations.
Polaris: Lymba's Semantic Parser
Authors: Dan Moldovan, Eduardo Blanco
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Question Answering, Relation Extraction, Sentence

Semantic representation of text is key to text understanding and reasoning. In this paper, we present Polaris, Lymba's semantic parser. Polaris is a supervised semantic parser that given text extracts semantic relations. It extracts relations from a wide variety of lexico-syntactic patterns, including verb-argument structures, noun compounds and others. The output can be provided in several formats: XML, RDF triples, logic forms or plain text, facilitating interoperability with other tools. Polaris is implemented using eight separate modules. Each module is explained and a detailed example of processing using a sample sentence is provided. Overall results using a benchmark are discussed. Per module performance, including errors made and pruned by each module are also analyzed.
Light Verb Constructions in the SzegedParalellFX English--Hungarian Parallel Corpus
Authors: Veronika Vincze
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Machine Translation, Retrieval, Sentence, Translation

In this paper, we describe the first English-Hungarian parallel corpus annotated for light verb constructions, which contains 14,261 sentence alignment units. Annotation principles and statistical data on the corpus are also provided, and English and Hungarian data are contrasted. On the basis of corpus data, a database containing pairs of English-Hungarian light verb constructions has been created as well. The corpus and the database can contribute to the automatic detection of light verb constructions and it is also shown how they can enhance performance in several fields of NLP (e.g. parsing, information extraction/retrieval and machine translation).
CoALT: A Software for Comparing Automatic Labelling Tools
Authors: Dominique Fohr, Odile Mella
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Speech Recognition, Speech Synthesis

Speech-text alignment tools are frequently used in speech technology and research. In this paper, we propose a GPL software CoALT (Comparing Automatic Labelling Tools) for comparing two automatic labellers or two speech-text alignment tools, ranking them and displaying statistics about their differences. The main feature of CoALT is that a user can define its own criteria for evaluating and comparing the speech-text alignment tools since the required quality for labelling depends on the targeted application. Beyond ranking, our tool provides useful statistics for each labeller and above all about their differences and can emphasize the drawbacks and advantages of each labeller. We have applied our software for the French and English languages but it can be used for another language by simply defining the list of the phonetic symbols and optionally a set of phonetic rules. In this paper we present the usage of the software for comparing two automatic labellers on the corpus TIMIT. Moreover, as automatic labelling tools are configurable (number of GMMs, phonetic lexicon, acoustic parameterisation), we then present how CoALT allows to determine the best parameters for our automatic labelling tool.
Balanced data repository of spontaneous spoken Czech
Authors: Lucie V{\'a}lkov{\'a}, Martina Waclawi{\v{c}}ov{\'a}, Michal K{\v{r}}en
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5

The paper presents data repository that will be used as a source of data for ORAL2013, a new corpus of spontaneous spoken Czech. The corpus is planned to be published in 2013 within the framework of the Czech National Corpus and it will contain both the audio recordings and their transcriptions manually aligned with time stamps. The corpus will be designed as a representation of contemporary spontaneous spoken language used in informal, real-life situations on the area of the whole Czech Republic and thus balanced in the main sociolinguistic categories of speakers. Therefore, the data repository features broad regional coverage with large variety of speakers, as well as precise and uniform processing. The repository is already built, basically balanced and sized 3 million words proper (i.e. tokens not including punctuation). Before the publication, another set of overall consistency checks will be carried out, as well as final selection of the transcriptions to be included into ORAL2013 as the final product.