pwc-archive/papers-with-abstracts huggingface dataset:
Data Mining with Shallow vs. Linguistic Features to Study Diversification of Scientific Registers
Authors: Stefania Degaetano-Ortlieb, Peter Fankhauser, Hannah Kermes, Ekaterina Lapshinova-Koltunski, Noam Ordan, Elke Teich
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Text Categorization, Text Classification

We present a methodology to analyze the linguistic evolution of scientific registers with data mining techniques, comparing the insights gained from shallow vs. linguistic features. The focus is on selected scientific disciplines at the boundaries to computer science (computational linguistics, bioinformatics, digital construction, microelectronics). The data basis is the English Scientific Text Corpus (SCITEX) which covers a time range of roughly thirty years (1970/80s to early 2000s) (Degaetano-Ortlieb et al., 2013; Teich and Fankhauser, 2010). In particular, we investigate the diversification of scientific registers over time. Our theoretical basis is Systemic Functional Linguistics (SFL) and its specific incarnation of register theory (Halliday and Hasan, 1985). In terms of methods, we combine corpus-based methods of feature extraction and data mining techniques.
On Stopwords, Filtering and Data Sparsity for Sentiment Analysis of Twitter
Authors: Hassan Saif, Fern, Miriam ez, Yulan He, Harith Alani
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Classification, Document Classification, General Classification, Sentiment Analysis, Sentiment Classification, Text Classification, Twitter Sentiment Analysis

Sentiment classification over Twitter is usually affected by the noisy nature (abbreviations, irregular forms) of tweets data. A popular procedure to reduce the noise of textual data is to remove stopwords by using pre-compiled stopword lists or more sophisticated methods for dynamic stopword identification. However, the effectiveness of removing stopwords in the context of Twitter sentiment classification has been debated in the last few years. In this paper we investigate whether removing stopwords helps or hampers the effectiveness of Twitter sentiment classification methods. To this end, we apply six different stopword identification methods to Twitter data from six different datasets and observe how removing stopwords affects two well-known supervised sentiment classification methods. We assess the impact of removing stopwords by observing fluctuations on the level of data sparsity, the size of the classifier{'}s feature space and its classification performance. Our results show that using pre-compiled lists of stopwords negatively impacts the performance of Twitter sentiment classification approaches. On the other hand, the dynamic generation of stopword lists, by removing those infrequent terms appearing only once in the corpus, appears to be the optimal method to maintaining a high classification performance while reducing the data sparsity and shrinking the feature space.
Adapting Freely Available Resources to Build an Opinion Mining Pipeline in Portuguese
Authors: Patrik Lambert, Carlos Rodr{\'\i}guez-Penagos
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Binary Classification, General Classification, Named Entity Recognition (NER), Opinion Mining, Part-Of-Speech Tagging, Sentiment Analysis

We present a complete UIMA-based pipeline for sentiment analysis in Portuguese news using freely available resources and a minimal set of manually annotated training data. We obtained good precision on binary classification but concluded that news feed is a challenging environment to detect the extent of opinionated text.
The SYN-series corpora of written Czech
Authors: Milena Hn{\'a}tkov{\'a}, Michal K{\v{r}}en, Pavel Proch{\'a}zka, Hana Skoumalov{\'a}
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Lemmatization, Morphological Tagging

The paper overviews the SYN series of synchronic corpora of written Czech compiled within the framework of the Czech National Corpus project. It describes their design and processing with a focus on the annotation, i.e. lemmatization and morphological tagging. The paper also introduces SYN2013PUB, a new 935-million newspaper corpus of Czech published in 2013 as the most recent addition to the SYN series before planned revision of its architecture. SYN2013PUB can be seen as a completion of the series in terms of titles and publication dates of major Czech newspapers that are now covered by complete volumes in comparable proportions. All SYN-series corpora can be characterized as traditional, with emphasis on cleared copyright issues, well-defined composition, reliable metadata and high-quality data processing; their overall size currently exceeds 2.2 billion running words.
ParCor 1.0: A Parallel Pronoun-Coreference Corpus to Support Statistical MT
Authors: Liane Guillou, Christian Hardmeier, Aaron Smith, J{\"o}rg Tiedemann, Bonnie Webber
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Translation

We present ParCor, a parallel corpus of texts in which pronoun coreference ― reduced coreference in which pronouns are used as referring expressions ― has been annotated. The corpus is intended to be used both as a resource from which to learn systematic differences in pronoun use between languages and ultimately for developing and testing informed Statistical Machine Translation systems aimed at addressing the problem of pronoun coreference in translation. At present, the corpus consists of a collection of parallel English-German documents from two different text genres: TED Talks (transcribed planned speech), and EU Bookshop publications (written text). All documents in the corpus have been manually annotated with respect to the type and location of each pronoun and, where relevant, its antecedent. We provide details of the texts that we selected, the guidelines and tools used to support annotation and some corpus statistics. The texts in the corpus have already been translated into many languages, and we plan to expand the corpus into these other languages, as well as other genres, in the future.
A Benchmark Database of Phonetic Alignments in Historical Linguistics and Dialectology
Authors: Johann-Mattis List, Jelena Proki{\'c}
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Diversity

In the last two decades, alignment analyses have become an important technique in quantitative historical linguistics and dialectology. Phonetic alignment plays a crucial role in the identification of regular sound correspondences and deeper genealogical relations between and within languages and language families. Surprisingly, up to today, there are no easily accessible benchmark data sets for phonetic alignment analyses. Here we present a publicly available database of manually edited phonetic alignments which can serve as a platform for testing and improving the performance of automatic alignment algorithms. The database consists of a great variety of alignments drawn from a large number of different sources. The data is arranged in a such way that typical problems encountered in phonetic alignment analyses (metathesis, diversity of phonetic sequences) are represented and can be directly tested.
Extracting News Web Page Creation Time with DCTFinder
Authors: Xavier Tannier
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Document Summarization, Information Retrieval, Multi-Document Summarization, Question Answering

Web pages do not offer reliable metadata concerning their creation date and time. However, getting the document creation time is a necessary step for allowing to apply temporal normalization systems to web pages. In this paper, we present DCTFinder, a system that parses a web page and extracts from its content the title and the creation date of this web page. DCTFinder combines heuristic title detection, supervised learning with Conditional Random Fields (CRFs) for document date extraction, and rule-based creation time recognition. Using such a system allows further deep and efficient temporal analysis of web pages. Evaluation on three corpora of English and French web pages indicates that the tool can extract document creation times with reasonably high accuracy (between 87 and 92{\textbackslash}{\%}). DCTFinder is made freely available on http://sourceforge.net/projects/dctfinder/, as well as all resources (vocabulary and annotated documents) built for training and evaluating the system in English and French, and the English trained model itself.
Corpus of 19th-century Czech Texts: Problems and Solutions
Authors: Karel Ku{\v{c}}era, Martin Stluka
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Optical Character Recognition (OCR)

Although the Czech language of the 19th century represents the roots of modern Czech and many features of the 20th- and 21st-century language cannot be properly understood without this historical background, the 19th-century Czech has not been thoroughly and consistently researched so far. The long-term project of a corpus of 19th-century Czech printed texts, currently in its third year, is intended to stimulate the research as well as to provide a firm material basis for it. The reason why, in our opinion, the project is worth mentioning is that it is faced with an unusual concentration of problems following mostly from the fact that the 19th century was arguably the most tumultuous period in the history of Czech, as well as from the fact that Czech is a highly inflectional language with a long history of sound changes, orthography reforms and rather discontinuous development of its vocabulary. The paper will briefly characterize the general background of the problems and present the reasoning behind the solutions that have been implemented in the ongoing project.
Investigating the Image of Entities in Social Media: Dataset Design and First Results
Authors: Julien Velcin, Young-Min Kim, Caroline Brun, Jean-Yves Dormagen, Eric SanJuan, Leila Khouas, Anne Peradotto, Stephane Bonnevay, Claude Roux, Julien Boyadjian, Alej Molina, ro, Marie Neihouser
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Clustering, Information Retrieval, Opinion Mining, Sentiment Analysis

The objective of this paper is to describe the design of a dataset that deals with the image (i.e., representation, web reputation) of various entities populating the Internet: politicians, celebrities, companies, brands etc. Our main contribution is to build and provide an original annotated French dataset. This dataset consists of 11527 manually annotated tweets expressing the opinion on specific facets (e.g., ethic, communication, economic project) describing two French policitians over time. We believe that other researchers might benefit from this experience, since designing and implementing such a dataset has proven quite an interesting challenge. This design comprises different processes such as data selection, formal definition and instantiation of an image. We have set up a full open-source annotation platform. In addition to the dataset design, we present the first results that we obtained by applying clustering methods to the annotated dataset in order to extract the entity images.
The Norwegian Dependency Treebank
Authors: Per Erik Solberg, Arne Skj{\ae}rholt, Lilja {\O}vrelid, Kristin Hagen, Janne Bondi Johannessen
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Dependency Parsing, Machine Translation, Part-Of-Speech Tagging, Semantic Parsing, Sentiment Analysis

The Norwegian Dependency Treebank is a new syntactic treebank for Norwegian Bokm{\"a}l and Nynorsk with manual syntactic and morphological annotation, developed at the National Library of Norway in collaboration with the University of Oslo. It is the first publically available treebank for Norwegian. This paper presents the core principles behind the syntactic annotation and how these principles were employed in certain specific cases. We then present the selection of texts and distribution between genres, as well as the annotation process and an evaluation of the inter-annotator agreement. Finally, we present the first results of data-driven dependency parsing of Norwegian, contrasting four state-of-the-art dependency parsers trained on the treebank. The consistency and the parsability of this treebank is shown to be comparable to other large treebank initiatives.
Extending standoff annotation
Authors: Maik St{\"u}hrenberg
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

Textual information is sometimes accompanied by additional encodings (such as visuals). These multimodal documents may be interesting objects of investigation for linguistics. Another class of complex documents are pre-annotated documents. Classic XML inline annotation often fails for both document classes because of overlapping markup. However, standoff annotation, that is the separation of primary data and markup, is a valuable and common mechanism to annotate multiple hierarchies and/or read-only primary data. We demonstrate an extended version of the XStandoff meta markup language, that allows the definition of segments in spatial and pre-annotated primary data. Together with the ability to import already established (linguistic) serialization formats as annotation levels and layers in an XStandoff instance, we are able to annotate a variety of primary data files, including text, audio, still and moving images. Application scenarios that may benefit from using XStandoff are the analyzation of multimodal documents such as instruction manuals, or sports match analysis, or the less destructive cleaning of web pages.
An efficient language independent toolkit for complete morphological disambiguation
Authors: L{\'a}szl{\'o} Laki, Gy{\"o}rgy Orosz
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Modelling, Lemmatization, Machine Translation, Morphological Disambiguation, Part-Of-Speech Tagging, POS, POS Tagging, TAG

In this paper a Moses SMT toolkit-based language-independent complete morphological annotation tool is presented called HuLaPos2. Our system performs PoS tagging and lemmatization simultaneously. Amongst others, the algorithm used is able to handle phrases instead of unigrams, and can perform the tagging in a not strictly left-to-right order. With utilizing these gains, our system outperforms the HMM-based ones. In order to handle the unknown words, a suffix-tree based guesser was integrated into HuLaPos2. To demonstrate the performance of our system it was compared with several systems in different languages and PoS tag sets. In general, it can be concluded that the quality of HuLaPos2 is comparable with the state-of-the-art systems, and in the case of PoS tagging it outperformed many available systems.
A decade of HLT Agency activities in the Low Countries: from resource maintenance (BLARK) to service offerings (BLAISE)
Authors: Peter Spyns, Remco van Veenendaal
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

In this paper we report on the Flemish-Dutch Agency for Human Language Technologies (HLT Agency or TST-Centrale in Dutch) in the Low Countries. We present its activities in its first decade of existence. The main goal of the HLT Agency is to ensure the sustainability of linguistic resources for Dutch. 10 years after its inception, the HLT Agency faces new challenges and opportunities. An important contextual factor is the rise of the infrastructure networks and proliferation of resource centres. We summarise some lessons learnt and we propose as future work to define and build for Dutch (which by extension can apply to any national language) a set of Basic LAnguage Infrastructure SErvices (BLAISE). As a conclusion, we state that the HLT Agency, also by its peculiar institutional status, has fulfilled and still is fulfilling an important role in maintaining Dutch as a digitally fully fledged functional language.
A Corpus of Spontaneous Speech in Lectures: The KIT Lecture Corpus for Spoken Language Processing and Translation
Authors: Eunah Cho, Sarah F{\"u}nfer, Sebastian St{\"u}ker, Alex Waibel
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Speech Recognition, Translation

With the increasing number of applications handling spontaneous speech, the needs to process spoken languages become stronger. Speech disfluency is one of the most challenging tasks to deal with in automatic speech processing. As most applications are trained with well-formed, written texts, many issues arise when processing spontaneous speech due to its distinctive characteristics. Therefore, more data with annotated speech disfluencies will help the adaptation of natural language processing applications, such as machine translation systems. In order to support this, we have annotated speech disfluencies in German lectures at KIT. In this paper we describe how we annotated the disfluencies in the data and provide detailed statistics on the size of the corpus and the speakers. Moreover, machine translation performance on a source text including disfluencies is compared to the results of the translation of a source text without different sorts of disfluencies or no disfluencies at all.
Free Acoustic and Language Models for Large Vocabulary Continuous Speech Recognition in Swedish
Authors: Niklas Vanhainen, Giampiero Salvi
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Modelling, speech-recognition, Speech Recognition

This paper presents results for large vocabulary continuous speech recognition (LVCSR) in Swedish. We trained acoustic models on the public domain NST Swedish corpus and made them freely available to the community. The training procedure corresponds to the reference recogniser (RefRec) developed for the SpeechDat databases during the COST249 action. We describe the modifications we made to the procedure in order to train on the NST database, and the language models we created based on the N-gram data available at the Norwegian Language Council. Our tests include medium vocabulary isolated word recognition and LVCSR. Because no previous results are available for LVCSR in Swedish, we use as baseline the performance of the SpeechDat models on the same tasks. We also compare our best results to the ones obtained in similar conditions on resource rich languages such as American English. We tested the acoustic models with HTK and Julius and plan to make them available in CMU Sphinx format as well in the near future. We believe that the free availability of these resources will boost research in speech and language technology in Swedish, even in research groups that do not have resources to develop ASR systems.
Turkish Resources for Visual Word Recognition
Authors: Beg{\"u}m Erten, Cem Bozsahin, Deniz Zeyrek
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Modelling, Speech Recognition

We report two tools to conduct psycholinguistic experiments on Turkish words. KelimetriK allows experimenters to choose words based on desired orthographic scores of word frequency, bigram and trigram frequency, ON, OLD20, ATL and subset/superset similarity. Turkish version of Wuggy generates pseudowords from one or more template words using an efficient method. The syllabified version of the words are used as the input, which are decomposed into their sub-syllabic components. The bigram frequency chains are constructed by the entire words{'} onset, nucleus and coda patterns. Lexical statistics of stems and their syllabification are compiled by us from BOUN corpus of 490 million words. Use of these tools in some experiments is shown.
An Arabic Twitter Corpus for Subjectivity and Sentiment Analysis
Authors: Eshrag Refaee, Verena Rieser
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Sentiment Analysis

We present a newly collected data set of 8,868 gold-standard annotated Arabic feeds. The corpus is manually labelled for subjectivity and sentiment analysis (SSA) ( = 0:816). In addition, the corpus is annotated with a variety of motivated feature-sets that have previously shown positive impact on performance. The paper highlights issues posed by twitter as a genre, such as mixture of language varieties and topic-shifts. Our next step is to extend the current corpus, using online semi-supervised learning. A first sub-corpus will be released via the ELRA repository as part of this submission.
The IMAGACT Visual Ontology. An Extendable Multilingual Infrastructure for the representation of lexical encoding of Action
Authors: Massimo Moneglia, Susan Brown, Francesca Frontini, Gloria Gagliardi, Fahad Khan, Monica Monachini, Aless Panunzi, ro
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

Action verbs have many meanings, covering actions in different ontological types. Moreover, each language categorizes action in its own way. One verb can refer to many different actions and one action can be identified by more than one verb. The range of variations within and across languages is largely unknown, causing trouble for natural language processing tasks. IMAGACT is a corpus-based ontology of action concepts, derived from English and Italian spontaneous speech corpora, which makes use of the universal language of images to identify the different action types extended by verbs referring to action in English, Italian, Chinese and Spanish. This paper presents the infrastructure and the various linguistic information the user can derive from it. IMAGACT makes explicit the variation of meaning of action verbs within one language and allows comparisons of verb variations within and across languages. Because the action concepts are represented with videos, extension into new languages beyond those presently implemented in IMAGACT is done using competence-based judgments by mother-tongue informants without intense lexicographic work involving underdetermined semantic description
Collaboration in the Production of a Massively Multilingual Lexicon
Authors: Martin Benjamin
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation

This paper discusses the multiple approaches to collaboration that the Kamusi Project is employing in the creation of a massively multilingual lexical resource. The projectÂ’s data structure enables the inclusion of large amounts of rich data within each sense-specific entry, with transitive concept-based links across languages. Data collection involves mining existing data sets, language experts using an online editing system, crowdsourcing, and games with a purpose. The paper discusses the benefits and drawbacks of each of these elements, and the steps the project is taking to account for those. Special attention is paid to guiding crowd members with targeted questions that produce results in a specific format. Collaboration is seen as an essential method for generating large amounts of linguistic data, as well as for validating the data so it can be considered trustworthy.
An Effortless Way To Create Large-Scale Datasets For Famous Speakers
Authors: Fran{\c{c}}ois Salmon, F{\'e}licien Vallet
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Person Identification, Speaker Diarization, Speaker Recognition

The creation of large-scale multimedia datasets has become a scientific matter in itself. Indeed, the fully-manual annotation of hundreds or thousands of hours of video and/or audio turns out to be practically infeasible. In this paper, we propose an extremly handy approach to automatically construct a database of famous speakers from TV broadcast news material. We then run a user experiment with a correctly designed tool that demonstrates that very reliable results can be obtained with this method. In particular, a thorough error analysis demonstrates the value of the approach and provides hints for the improvement of the quality of the dataset.