pwc-archive/papers-with-abstracts huggingface dataset:
The KiezDeutsch Korpus (KiDKo) Release 1.0
Authors: Ines Rehbein, S{\"o}ren Schalowski, Heike Wiese
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: POS, POS Tagging

This paper presents the first release of the KiezDeutsch Korpus (KiDKo), a new language resource with multiparty spoken dialogues of Kiezdeutsch, a newly emerging language variety spoken by adolescents from multiethnic urban areas in Germany. The first release of the corpus includes the transcriptions of the data as well as a normalisation layer and part-of-speech annotations. In the paper, we describe the main features of the new resource and then focus on automatic POS tagging of informal spoken language. Our tagger achieves an accuracy of nearly 97{\%} on KiDKo. While we did not succeed in further improving the tagger using ensemble tagging, we present our approach to using the tagger ensembles for identifying error patterns in the automatically tagged data.
Etymological Wordnet: Tracing The History of Words
Authors: Gerard de Melo
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

Research on the history of words has led to remarkable insights about language and also about the history of human civilization more generally. This paper presents the Etymological Wordnet, the first database that aims at making word origin information available as a large, machine-readable network of words in many languages. The information in this resource is obtained from Wiktionary. Extracting a network of etymological information from Wiktionary requires significant effort, as much of the etymological information is only given in prose. We rely on custom pattern matching techniques and mine a large network with over 500,000 word origin links as well as over 2 million derivational/compositional links.
The Interplay Between Lexical and Syntactic Resources in Incremental Parsebanking
Authors: Victoria Ros{\'e}n, Petter Haugereid, Martha Thunes, Gyri S. Losnegaard, Helge Dyvik
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Optical Character Recognition (OCR)

Automatic syntactic analysis of a corpus requires detailed lexical and morphological information that cannot always be harvested from traditional dictionaries. In building the INESS Norwegian treebank, it is often the case that necessary lexical information is missing in the morphology or lexicon. The approach used to build the treebank is incremental parsebanking; a corpus is parsed with an existing grammar, and the analyses are efficiently disambiguated by annotators. When the intended analysis is unavailable after parsing, the reason is often that necessary information is not available in the lexicon. INESS has therefore implemented a text preprocessing interface where annotators can enter unrecognized words before parsing. This may concern words that are unknown to the morphology and/or lexicon, and also words that are known, but for which important information is missing. When this information is added, either during text preprocessing or during disambiguation, the result is that after reparsing the intended analysis can be chosen and stored in the treebank. The lexical information added to the lexicon in this way may be of great interest both to lexicographers and to other language technology efforts, and the enriched lexical resource being developed will be made available at the end of the project.
Interoperability and Customisation of Annotation Schemata in Argo
Authors: Rafal Rak, Jacob Carter, Andrew Rowley, Riza Theresa Batista-Navarro, Sophia Ananiadou
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Management

The process of annotating text corpora involves establishing annotation schemata which define the scope and depth of an annotation task at hand. We demonstrate this activity in Argo, a Web-based workbench for the analysis of textual resources, which facilitates both automatic and manual annotation. Annotation tasks in the workbench are defined by building workflows consisting of a selection of available elementary analytics developed in compliance with the Unstructured Information Management Architecture specification. The architecture accommodates complex annotation types that may define primitive as well as referential attributes. Argo aids the development of custom annotation schemata and supports their interoperability by featuring a schema editor and specialised analytics for schemata alignment. The schema editor is a self-contained graphical user interface for defining annotation types. Multiple heterogeneous schemata can be aligned by including one of two type mapping analytics currently offered in Argo. One is based on a simple mapping syntax and, although limited in functionality, covers most common use cases. The other utilises a well established graph query language, SPARQL, and is superior to other state-of-the-art solutions in terms of expressiveness. We argue that the customisation of annotation schemata does not need to compromise their interoperability.
Polish Coreference Corpus in Numbers
Authors: Maciej Ogrodniczuk, Mateusz Kope{\'c}, Agata Savary
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Clustering, coreference-resolution, Coreference Resolution, Lemmatization, valid

This paper attempts a preliminary interpretation of the occurrence of different types of linguistic constructs in the manually-annotated Polish Coreference Corpus by providing analyses of various statistical properties related to mentions, clusters and near-identity links. Among others, frequency of mentions, zero subjects and singleton clusters is presented, as well as the average mention and cluster size. We also show that some coreference clustering constraints, such as gender or number agreement, are frequently not valid in case of Polish. The need for lemmatization for automatic coreference resolution is supported by an empirical study. Correlation between cluster and mention count within a text is investigated, with short characteristics of outlier cases. We also examine this correlation in each of the 14 text domains present in the corpus and show that none of them has abnormal frequency of outlier texts regarding the cluster/mention ratio. Finally, we report on our negative experiences concerning the annotation of the near-identity relation. In the conclusion we put forward some guidelines for the future research in the area.
A Gold Standard Dependency Corpus for English
Authors: Natalia Silveira, Timothy Dozat, Marie-Catherine de Marneffe, Samuel Bowman, Miriam Connor, John Bauer, Chris Manning
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Sentiment Analysis

We present a gold standard annotation of syntactic dependencies in the English Web Treebank corpus using the Stanford Dependencies formalism. This resource addresses the lack of a gold standard dependency treebank for English, as well as the limited availability of gold standard syntactic annotations for English informal text genres. We also present experiments on the use of this resource, both for training dependency parsers and for evaluating the quality of different versions of the Stanford Parser, which includes a converter tool to produce dependency annotation from constituency trees. We show that training a dependency parser on a mix of newswire and web data leads to better performance on that type of data without hurting performance on newswire text, and therefore gold standard annotations for non-canonical text can be a valuable resource for parsing. Furthermore, the systematic annotation effort has informed both the SD formalism and its implementation in the Stanford Parser{'}s dependency converter. In response to the challenges encountered by annotators in the EWT corpus, the formalism has been revised and extended, and the converter has been improved.
DerivBase.hr: A High-Coverage Derivational Morphology Resource for Croatian
Authors: Jan {\v{S}}najder
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Clustering, Lemmatization, Natural Language Inference, Part-Of-Speech Tagging, Semantic Role Labeling, Semantic Textual Similarity, Vocal Bursts Intensity Prediction

Knowledge about derivational morphology has been proven useful for a number of natural language processing (NLP) tasks. We describe the construction and evaluation of DerivBase.hr, a large-coverage morphological resource for Croatian. DerivBase.hr groups 100k lemmas from web corpus hrWaC into 56k clusters of derivationally related lemmas, so-called derivational families. We focus on suffixal derivation between and within nouns, verbs, and adjectives. We propose two approaches: an unsupervised approach and a knowledge-based approach based on a hand-crafted morphology model but without using any additional lexico-semantic resources The resource acquisition procedure consists of three steps: corpus preprocessing, acquisition of an inflectional lexicon, and the induction of derivational families. We describe an evaluation methodology based on manually constructed derivational families from which we sample and annotate pairs of lemmas. We evaluate DerivBase.hr on the so-obtained sample, and show that the knowledge-based version attains good clustering quality of 81.2{\%} precision, 76.5{\%} recall, and 78.8{\%} F1 -score. As with similar resources for other languages, we expect DerivBase.hr to be useful for a number of NLP tasks.
Extracting Information for Context-aware Meeting Preparation
Authors: Simon Scerri, Behrang Q. Zadeh, Maciej Dabrowski, Ismael Rivera
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

People working in an office environment suffer from large volumes of information that they need to manage and access. Frequently, the problem is due to machines not being able to recognise the many implicit relationships between office artefacts, and also due to them not being aware of the context surrounding them. In order to expose these relationships and enrich artefact context, text analytics can be employed over semi-structured and unstructured content, including free text. In this paper, we explain how this strategy is applied and partly evaluated for a specific use-case: supporting the attendees of a calendar event to prepare for the meeting.
A Repository of State of the Art and Competitive Baseline Summaries for Generic News Summarization
Authors: Kai Hong, John Conroy, Benoit Favre, Alex Kulesza, Hui Lin, Ani Nenkova
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Document Summarization, Extractive Summarization, Multi-Document Summarization, News Summarization

In the period since 2004, many novel sophisticated approaches for generic multi-document summarization have been developed. Intuitive simple approaches have also been shown to perform unexpectedly well for the task. Yet it is practically impossible to compare the existing approaches directly, because systems have been evaluated on different datasets, with different evaluation measures, against different sets of comparison systems. Here we present a corpus of summaries produced by several state-of-the-art extractive summarization systems or by popular baseline systems. The inputs come from the 2004 DUC evaluation, the latest year in which generic summarization was addressed in a shared task. We use the same settings for ROUGE automatic evaluation to compare the systems directly and analyze the statistical significance of the differences in performance. We show that in terms of average scores the state-of-the-art systems appear similar but that in fact they produce very different summaries. Our corpus will facilitate future research on generic summarization and motivates the need for development of more sensitive evaluation measures and for approaches to system combination in summarization.
Collecting Natural SMS and Chat Conversations in Multiple Languages: The BOLT Phase 2 Corpus
Authors: Zhiyi Song, Stephanie Strassel, Haejoong Lee, Kevin Walker, Jonathan Wright, Jennifer Garland, Dana Fore, Brian Gainor, Preston Cabe, Thomas Thomas, Brendan Callahan, Ann Sawyer
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Translation

The DARPA BOLT Program develops systems capable of allowing English speakers to retrieve and understand information from informal foreign language genres. Phase 2 of the program required large volumes of naturally occurring informal text (SMS) and chat messages from individual users in multiple languages to support evaluation of machine translation systems. We describe the design and implementation of a robust collection system capable of capturing both live and archived SMS and chat conversations from willing participants. We also discuss the challenges recruitment at a time when potential participants have acute and growing concerns about their personal privacy in the realm of digital communication, and we outline the techniques adopted to confront those challenges. Finally, we review the properties of the resulting BOLT Phase 2 Corpus, which comprises over 6.5 million words of naturally-occurring chat and SMS in English, Chinese and Egyptian Arabic.
Comparing the Quality of Focused Crawlers and of the Translation Resources Obtained from them
Authors: Bruno Laranjeira, Viviane Moreira, Aline Villavicencio, Carlos Ramisch, Maria Jos{\'e} Finatto
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Translation

Comparable corpora have been used as an alternative for parallel corpora as resources for computational tasks that involve domain-specific natural language processing. One way to gather documents related to a specific topic of interest is to traverse a portion of the web graph in a targeted way, using focused crawling algorithms. In this paper, we compare several focused crawling algorithms using them to collect comparable corpora on a specific domain. Then, we compare the evaluation of the focused crawling algorithms to the performance of linguistic processes executed after training with the corresponding generated corpora. Also, we propose a novel approach for focused crawling, exploiting the expressive power of multiword expressions.
Augmenting English Adjective Senses with Supersenses
Authors: Yulia Tsvetkov, Nathan Schneider, Dirk Hovy, Archna Bhatia, Manaal Faruqui, Chris Dyer
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Classification, General Classification

We develop a supersense taxonomy for adjectives, based on that of GermaNet, and apply it to English adjectives in WordNet using human annotation and supervised classification. Results show that accuracy for automatic adjective type classification is high, but synsets are considerably more difficult to classify, even for trained human annotators. We release the manually annotated data, the classifier, and the induced supersense labeling of 12,304 WordNet adjective synsets.
N-gram Counts and Language Models from the Common Crawl
Authors: Christian Buck, Kenneth Heafield, Bas van Ooyen
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Modeling, Language Modelling, Machine Translation, Speech Recognition

We contribute 5-gram counts and language models trained on the Common Crawl corpus, a collection over 9 billion web pages. This release improves upon the Google n-gram counts in two key ways: the inclusion of low-count entries and deduplication to reduce boilerplate. By preserving singletons, we were able to use Kneser-Ney smoothing to build large language models. This paper describes how the corpus was processed with emphasis on the problems that arise in working with data at this scale. Our unpruned Kneser-Ney English {\$}5{\$}-gram language model, built on 975 billion deduplicated tokens, contains over 500 billion unique n-grams. We show gains of 0.5-1.4 BLEU by using large language models to translate into various languages.
ColLex.en: Automatically Generating and Evaluating a Full-form Lexicon for English
Authors: Tim vor der Br{\"u}ck, Alex Mehler, er, Zahurul Islam
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Form, Text Categorization, Text Classification

The paper describes a procedure for the automatic generation of a large full-form lexicon of English. We put emphasis on two statistical methods to lexicon extension and adjustment: in terms of a letter-based HMM and in terms of a detector of spelling variants and misspellings. The resulting resource, {\textbackslash}collexen, is evaluated with respect to two tasks: text categorization and lexical coverage by example of the SUSANNE corpus and the {\textbackslash}openanc.
Evaluation of Simple Distributional Compositional Operations on Longer Texts
Authors: Tamara Polajnar, Laura Rimell, Stephen Clark
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Semantic Textual Similarity, Sentence, Sentence segmentation

Distributional semantic models have been effective at representing linguistic semantics at the word level, and more recently research has moved on to the construction of distributional representations for larger segments of text. However, it is not well understood how the composition operators that work well on short phrase-based models scale up to full-length sentences. In this paper we test several simple compositional methods on a sentence-length similarity task and discover that their performance peaks at fewer than ten operations. We also introduce a novel sentence segmentation method that reduces the number of compositional operations.
Creating Summarization Systems with SUMMA
Authors: Horacio Saggion
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Sentence, Text Summarization

Automatic text summarization, the reduction of a text to its essential content is fundamental for an on-line information society. Although many summarization algorithms exist, there are few tools or infrastructures providing capabilities for developing summarization applications. This paper presents a new version of SUMMA, a text summarization toolkit for the development of adaptive summarization applications. SUMMA includes algorithms for computation of various sentence relevance features and functionality for single and multidocument summarization in various languages. It also offers methods for content-based evaluation of summaries.
BiographyNet: Methodological Issues when NLP supports historical research
Authors: Antske Fokkens, Serge ter Braake, Niels Ockeloen, Piek Vossen, Susan Leg{\^e}ne, Guus Schreiber
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Information Retrieval

When NLP is used to support research in the humanities, new methodological issues come into play. NLP methods may introduce a bias in their analysis that can influence the results of the hypothesis a humanities scholar is testing. This paper addresses this issue in the context of BiographyNet a multi-disciplinary project involving NLP, Linked Data and history. We introduce the project to the NLP community. We argue that it is essential for historians to get insight into the provenance of information, including how information was extracted from text by NLP tools.
Enhancing the TED-LIUM Corpus with Selected Data for Language Modeling and More TED Talks
Authors: Anthony Rousseau, Paul Del{\'e}glise, Yannick Est{\`e}ve
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Modeling, Language Modelling, Machine Translation, Speech Recognition

In this paper, we present improvements made to the TED-LIUM corpus we released in 2012. These enhancements fall into two categories. First, we describe how we filtered publicly available monolingual data and used it to estimate well-suited language models (LMs), using open-source tools. Then, we describe the process of selection we applied to new acoustic data from TED talks, providing additions to our previously released corpus. Finally, we report some experiments we made around these improvements.
Enrichment of Bilingual Dictionary through News Stream Data
Authors: Ajay Dubey, Parth Gupta, Vasudeva Varma, Paolo Rosso
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Information Retrieval

Bilingual dictionaries are the key component of the cross-lingual similarity estimation methods. Usually such dictionary generation is accomplished by manual or automatic means. Automatic generation approaches include to exploit parallel or comparable data to derive dictionary entries. Such approaches require large amount of bilingual data in order to produce good quality dictionary. Many time the language pair does not have large bilingual comparable corpora and in such cases the best automatic dictionary is upper bounded by the quality and coverage of such corpora. In this work we propose a method which exploits continuous quasi-comparable corpora to derive term level associations for enrichment of such limited dictionary. Though we propose our experiments for English and Hindi, our approach can be easily extendable to other languages. We evaluated dictionary by manually computing the precision. In experiments we show our approach is able to derive interesting term level associations across languages.
sloWCrowd: A crowdsourcing tool for lexicographic tasks
Authors: Darja Fi{\v{s}}er, Ale{\v{s}} Tav{\v{c}}ar, Toma{\v{z}} Erjavec
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

The paper presents sloWCrowd, a simple tool developed to facilitate crowdsourcing lexicographic tasks, such as error correction in automatically generated wordnets and semantic annotation of corpora. The tool is open-source, language-independent and can be adapted to a broad range of crowdsourcing tasks. Since volunteers who participate in our crowdsourcing tasks are not trained lexicographers, the tool has been designed to obtain multiple answers to the same question and compute the majority vote, making sure individual unreliable answers are discarded. We also make sure unreliable volunteers, who systematically provide unreliable answers, are not taken into account. This is achieved by measuring their accuracy against a gold standard, the questions from which are posed to the annotators on a regular basis in between the real question. We tested the tool in an extensive crowdsourcing task, i.e. error correction of the Slovene wordnet, the results of which are encouraging, motivating us to use the tool in other annotation tasks in the future as well.
A Large Scale Database of Strongly-related Events in Japanese
Authors: Tomohide Shibata, Shotaro Kohama, Sadao Kurohashi
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Common Sense Reasoning, coreference-resolution, Coreference Resolution, Event Extraction

The knowledge about the relation between events is quite useful for coreference resolution, anaphora resolution, and several NLP applications such as dialogue system. This paper presents a large scale database of strongly-related events in Japanese, which has been acquired with our proposed method (Shibata and Kurohashi, 2011). In languages, where omitted arguments or zero anaphora are often utilized, such as Japanese, the coreference-based event extraction methods are hard to be applied, and so our method extracts strongly-related events in a two-phrase construct. This method first calculates the co-occurrence measure between predicate-arguments (events), and regards an event pair, whose mutual information is high, as strongly-related events. To calculate the co-occurrence measure efficiently, we adopt an association rule mining method. Then, we identify the remaining arguments by using case frames. The database contains approximately 100,000 unique events, with approximately 340,000 strongly-related event pairs, which is much larger than an existing automatically-constructed event database. We evaluated randomly-chosen 100 event pairs, and the accuracy was approximately 68{\%}.