pwc-archive/papers-with-abstracts huggingface dataset:
Using a sledgehammer to crack a nut? Lexical diversity and event coreference resolution
Authors: Agata Cybulska, Piek Vossen
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Articles, coreference-resolution, Coreference Resolution, Diversity, Event Coreference Resolution

In this paper we examine the representativeness of the EventCorefBank (ECB, Bejan and Harabagiu, 2010) with regards to the language population of large-volume streams of news. The ECB corpus is one of the data sets used for evaluation of the task of event coreference resolution. Our analysis shows that the ECB in most cases covers one seminal event per domain, what considerably simplifies event and so language diversity that one comes across in the news. We augmented the corpus with a new corpus component, consisting of 502 texts, describing different instances of event types that were already captured by the 43 topics of the ECB, making it more representative of news articles on the web. The new {``}ECB+{''} corpus is available for further research.
caWaC -- A web corpus of Catalan and its application to language modeling and machine translation
Authors: Nikola Ljube{\v{s}}i{\'c}, Antonio Toral
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation

In this paper we present the construction process of a web corpus of Catalan built from the content of the .cat top-level domain. For collecting and processing data we use the Brno pipeline with the spiderling crawler and its accompanying tools. To the best of our knowledge the corpus represents the largest existing corpus of Catalan containing 687 million words, which is a significant increase given that until now the biggest corpus of Catalan, CuCWeb, counts 166 million words. We evaluate the resulting resource on the tasks of language modeling and statistical machine translation (SMT) by calculating LM perplexity and incorporating the LM in the SMT pipeline. We compare language models trained on different subsets of the resource with those trained on the Catalan Wikipedia and the target side of the parallel data used to train the SMT system.
Recent Developments in DeReKo
Authors: Marc Kupietz, Harald L{\"u}ngen
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

This paper gives an overview of recent developments in the German Reference Corpus DeReKo in terms of growth, maximising relevant corpus strata, metadata, legal issues, and its current and future research interface. Due to the recent acquisition of new licenses, DeReKo has grown by a factor of four in the first half of 2014, mostly in the area of newspaper text, and presently contains over 24 billion word tokens. Other strata, like fictional texts, web corpora, in particular CMC texts, and spoken but conceptually written texts have also increased significantly. We report on the newly acquired corpora that led to the major increase, on the principles and strategies behind our corpus acquisition activities, and on our solutions for the emerging legal, organisational, and technical challenges.
Why Chinese Web-as-Corpus is Wacky? Or: How Big Data is Killing Chinese Corpus Linguistics
Authors: Shu-Kai Hsieh
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Chinese Word Segmentation

This paper aims to examine and evaluate the current development of using Web-as-Corpus (WaC) paradigm in Chinese corpus linguistics. I will argue that the unstable notion of wordhood in Chinese and the resulting diverse ideas of implementing word segmentation systems have posed great challenges for those who are keen on building web-scaled corpus data. Two lexical measures are proposed to illustrate the issues and methodological discussions are provided.
Automatic acquisition of Urdu nouns (along with gender and irregular plurals)
Authors: Tafseer Ahmed Khan
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Transliteration

The paper describes a set of methods to automatically acquire the Urdu nouns (and its gender) on the basis of inflectional and contextual clues. The algorithms used are a blend of computer{'}s brute force on the corpus and careful design of distinguishing rules on the basis linguistic knowledge. As there are homograph inflections for Urdu nouns, adjectives and verbs, we compare potential inflectional forms with paradigms of inflections in strict order and gives best guess (of part of speech) for the word. We also worked on irregular plurals i.e. the plural forms that are borrowed from Arabic, Persian and English. Evaluation shows that not all the borrowed rules have same productivity in Urdu. The commonly used borrowed plural rules are shown in the result.
Re-using an Argument Corpus to Aid in the Curation of Social Media Collections
Authors: Clare Llewellyn, Claire Grover, Oberl, Jon er, Ewan Klein
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Argument Mining, BIG-bench Machine Learning

This work investigates how automated methods can be used to classify social media text into argumentation types. In particular it is shown how supervised machine learning was used to annotate a Twitter dataset (London Riots) with argumentation classes. An investigation of issues arising from a natural inconsistency within social media data found that machine learning algorithms tend to over fit to the data because Twitter contains a lot of repetition in the form of retweets. It is also noted that when learning argumentation classes we must be aware that the classes will most likely be of very different sizes and this must be kept in mind when analysing the results. Encouraging results were found in adapting a model from one domain of Twitter data (London Riots) to another (OR2012). When adapting a model to another dataset the most useful feature was punctuation. It is probable that the nature of punctuation in Twitter language, the very specific use in links, indicates argumentation class.
Billions of Parallel Words for Free: Building and Using the EU Bookshop Corpus
Authors: Raivis Skadi{\c{n}}{\v{s}}, J{\"o}rg Tiedemann, Roberts Rozis, Daiga Deksne
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Translation

The European Union is a great source of high quality documents with translations into several languages. Parallel corpora from its publications are frequently used in various tasks, machine translation in particular. A source that has not systematically been explored yet is the EU Bookshop â€• an online service and archive of publications from various European institutions. The service contains a large body of publications in the 24 official of the EU. This paper describes our efforts in collecting those publications and converting them to a format that is useful for natural language processing in particular statistical machine translation. We report our procedure of crawling the website and various pre-processing steps that were necessary to clean up the data after the conversion from the original PDF files. Furthermore, we demonstrate the use of this dataset in training SMT models for English, French, German, Spanish, and Latvian.
Generating Polarity Lexicons with WordNet propagation in 5 languages
Authors: Isa Maks, Ruben Izquierdo, Francesca Frontini, Rodrigo Agerri, Piek Vossen, Andoni Azpeitia
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Named Entity Recognition (NER), Opinion Mining, Sentiment Analysis

In this paper we focus on the creation of general-purpose (as opposed to domain-specific) polarity lexicons in five languages: French, Italian, Dutch, English and Spanish using WordNet propagation. WordNet propagation is a commonly used method to generate these lexicons as it gives high coverage of general purpose language and the semantically rich WordNets where concepts are organised in synonym , antonym and hyperonym/hyponym structures seem to be well suited to the identification of positive and negative words. However, WordNets of different languages may vary in many ways such as the way they are compiled, the number of synsets, number of synonyms and number of semantic relations they include. In this study we investigate whether this variability translates into differences of performance when these WordNets are used for polarity propagation. Although many variants of the propagation method are developed for English, little is known about how they perform with WordNets of other languages. We implemented a propagation algorithm and designed a method to obtain seed lists similar with respect to quality and size, for each of the five languages. We evaluated the results against gold standards also developed according to a common method in order to achieve as less variance as possible between the different languages.
Online optimisation of log-linear weights in interactive machine translation
Authors: Mara Chinea Rios, Germ{\'a}n Sanchis-Trilles, Daniel Ortiz-Mart{\'\i}nez, Francisco Casacuberta
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Modelling, Machine Translation, regression, Translation

Whenever the quality provided by a machine translation system is not enough, a human expert is required to correct the sentences provided by the machine translation system. In such a setup, it is crucial that the system is able to learn from the errors that have already been corrected. In this paper, we analyse the applicability of discriminative ridge regression for learning the log-linear weights of a state-of-the-art machine translation system underlying an interactive machine translation framework, with encouraging results.
Extending HeidelTime for Temporal Expressions Referring to Historic Dates
Authors: Jannik Str{\"o}tgen, Thomas B{\"o}gel, Julian Zell, Ayser Armiti, Tran Van Canh, Michael Gertz
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Articles, Document Summarization, Machine Translation, Temporal Information Extraction, Temporal Tagging

Research on temporal tagging has achieved a lot of attention during the last years. However, most of the work focuses on processing news-style documents. Thus, references to historic dates are often not well handled by temporal taggers although they frequently occur in narrative-style documents about history, e.g., in many Wikipedia articles. In this paper, we present the AncientTimes corpus containing documents about different historic time periods in eight languages, in which we manually annotated temporal expressions. Based on this corpus, we explain the challenges of temporal tagging documents about history. Furthermore, we use the corpus to extend our multilingual, cross-domain temporal tagger HeidelTime to extract and normalize temporal expressions referring to historic dates, and to demonstrate HeidelTime{'}s new capabilities. Both, the AncientTimes corpus as well as the new HeidelTime version are made publicly available.
The USAGE review corpus for fine grained multi lingual opinion analysis
Authors: Roman Klinger, Philipp Cimiano
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Opinion Mining, Sentiment Analysis

Opinion mining has received wide attention in recent years. Models for this task are typically trained or evaluated with a manually annotated dataset. However, fine-grained annotation of sentiments including information about aspects and their evaluation is very labour-intensive. The data available so far is limited. Contributing to this situation, this paper describes the Bielefeld University Sentiment Analysis Corpus for German and English (USAGE), which we offer freely to the community and which contains the annotation of product reviews from Amazon with both aspects and subjective phrases. It provides information on segments in the text which denote an aspect or a subjective evaluative phrase which refers to the aspect. Relations and coreferences are explicitly annotated. This dataset contains 622 English and 611 German reviews, allowing to investigate how to port sentiment analysis systems across languages and domains. We describe the methodology how the corpus was created and provide statistics including inter-annotator agreement. We further provide figures for a baseline system and results for German and English as well as in a cross-domain setting. The results are encouraging in that they show that aspects and phrases can be extracted robustly without the need of tuning to a particular type of products.
An Exercise in Reuse of Resources: Adapting General Discourse Coreference Resolution for Detecting Lexical Chains in Patent Documentation
Authors: Nadjet Bouayad-Agha, Alicia Burga, Gerard Casamayor, Joan Codina, Rogelio Nazar, Leo Wanner
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: coreference-resolution, Coreference Resolution, Domain Adaptation, Named Entity Recognition (NER)

The Stanford Coreference Resolution System (StCR) is a multi-pass, rule-based system that scored best in the CoNLL 2011 shared task on general discourse coreference resolution. We describe how the StCR has been adapted to the specific domain of patents and give some cues on how it can be adapted to other domains. We present a linguistic analysis of the patent domain and how we were able to adapt the rules to the domain and to expand coreferences with some lexical chains. A comparative evaluation shows an improvement of the coreference resolution system, denoting that (i) StCR is a valuable tool across different text genres; (ii) specialized discourse NLP may significantly benefit from general discourse NLP research.
VOAR: A Visual and Integrated Ontology Alignment Environment
Authors: Bernardo Severo, Cassia Trojahn, Renata Vieira
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Ontology Matching

Ontology alignment is a key process for enabling interoperability between ontology-based systems in the Linked Open Data age. From two input ontologies, this process generates an alignment (set of correspondences) between them. In this paper we present VOAR, a new web-based environment for ontology alignment visualization and manipulation. Within this graphical environment, users can manually create/edit correspondences and apply a set of operations on alignments (filtering, merge, difference, etc.). VOAR allows invoking external ontology matching systems that implement a specific alignment interface, so that the generated alignments can be manipulated within the environment. Evaluating multiple alignments together against a reference one can also be carried out, using classical evaluation metrics (precision, recall and f-measure). The status of each correspondence with respect to its presence or absence in reference alignment is visually represented. Overall, the main new aspect of VOAR is the visualization and manipulation of alignments at schema level, in an integrated, visual and web-based environment.
A 500 Million Word POS-Tagged Icelandic Corpus
Authors: Thomas Eckart, Erla Hallsteinsd{\'o}ttir, Sigr{\'u}n Helgad{\'o}ttir, Uwe Quasthoff, Dirk Goldhahn
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Part-Of-Speech Tagging, POS, TAG

The new POS-tagged Icelandic corpus of the Leipzig Corpora Collection is an extensive resource for the analysis of the Icelandic language. As it contains a large share of all Web documents hosted under the .is top-level domain, it is especially valuable for investigations on modern Icelandic and non-standard language varieties. The corpus is accessible via a dedicated web portal and large shares are available for download. Focus of this paper will be the description of the tagging process and evaluation of statistical properties like word form frequencies and part of speech tag distributions. The latter will be in particular compared with values from the Icelandic Frequency Dictionary (IFD) Corpus.
A Collection of Scholarly Book Reviews from the Platforms of electronic sources in Humanities and Social Sciences OpenEdition.org
Authors: Chahinez Benkoussas, Hussam Hamdan, Patrice Bellot, Fr{\'e}d{\'e}ric B{\'e}chet, Elodie Faath
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Collaborative Filtering, Genre classification, Information Retrieval

In this paper, we present our contribution for the automatic construction of the Scholarly Book Reviews corpora from two different sources, the OpenEdition platform which is dedicated to electronic resources in the humanities and social sciences, and the Web. The main target is the collect of reviews in order to provide automatic links between each review and its potential book in the future. For these purposes, we propose different document representations and we apply some supervised approaches for binary genre classification before evaluating their impact.
Rapid Deployment of Phrase Structure Parsing for Related Languages: A Case Study of Insular Scandinavian
Authors: Anton Karl Ingason, Hrafn Loftsson, Eir{\'\i}kur R{\"o}gnvaldsson, Einar Freyr Sigur{\dh}sson, Joel C. Wallenberg
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Part-Of-Speech Tagging

This paper presents ongoing work that aims to improve machine parsing of Faroese using a combination of Faroese and Icelandic training data. We show that even if we only have a relatively small parsed corpus of one language, namely 53,000 words of Faroese, we can obtain better results by adding information about phrase structure from a closely related language which has a similar syntax. Our experiment uses the Berkeley parser. We demonstrate that the addition of Icelandic data without any other modification to the experimental setup results in an f-measure improvement from 75.44{\%} to 78.05{\%} in Faroese and an improvement in part-of-speech tagging accuracy from 88.86{\%} to 90.40{\%}.
N\mbox$^3$ - A Collection of Datasets for Named Entity Recognition and Disambiguation in the NLP Interchange Format
Authors: Michael R{\"o}der, Ricardo Usbeck, Sebastian Hellmann, Daniel Gerber, Andreas Both
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Coreference Resolution, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

Extracting Linked Data following the Semantic Web principle from unstructured sources has become a key challenge for scientific research. Named Entity Recognition and Disambiguation are two basic operations in this extraction process. One step towards the realization of the Semantic Web vision and the development of highly accurate tools is the availability of data for validating the quality of processes for Named Entity Recognition and Disambiguation as well as for algorithm tuning. This article presents three novel, manually curated and annotated corpora (N3). All of them are based on a free license and stored in the NLP Interchange Format to leverage the Linked Data character of our datasets.
Assessment of Non-native Prosody for Spanish as L2 using quantitative scores and perceptual evaluation
Authors: Valent{\'\i}n Carde{\~n}oso-Payo, C{\'e}sar Gonz{\'a}lez-Ferreras, David Escudero
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Acquisition, Speech Recognition

In this work we present SAMPLE, a new pronunciation database of Spanish as L2, and first results on the automatic assessment of Non-native prosody. Listen and repeat and read tasks are carried out by native and foreign speakers of Spanish. The corpus has been designed to support comparative studies and evaluation of automatic pronunciation error assessment both at phonetic and prosodic level. Four expert evaluators have annotated utterances with perceptual scores related to prosodic aspects of speech, intelligibility, phonetic quality and global proficiency level in Spanish. From each utterance, we computed several prosodic features and ASR scores. A correlation study over subjective and quantitative measures is carried out. An estimation of the prediction of perceptual scores from speech features is shown.
Exploiting the large-scale German Broadcast Corpus to boost the Fraunhofer IAIS Speech Recognition System
Authors: Michael Stadtschnitzer, Jochen Schwenninger, Daniel Stein, Joachim Koehler
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Acoustic Modelling, Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modelling, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), speech-recognition, Speech Recognition

In this paper we describe the large-scale German broadcast corpus (GER-TV1000h) containing more than 1,000 hours of transcribed speech data. This corpus is unique in the German language corpora domain and enables significant progress in tuning the acoustic modelling of German large vocabulary continuous speech recognition (LVCSR) systems. The exploitation of this huge broadcast corpus is demonstrated by optimizing and improving the Fraunhofer IAIS speech recognition system. Due to the availability of huge amount of acoustic training data new training strategies are investigated. The performance of the automatic speech recognition (ASR) system is evaluated on several datasets and compared to previously published results. It can be shown that the word error rate (WER) using a larger corpus can be reduced by up to 9.1 {\textbackslash}{\%} relative. By using both larger corpus and recent training paradigms the WER was reduced by up to 35.8 {\textbackslash}{\%} relative and below 40 {\textbackslash}{\%} absolute even for spontaneous dialectal speech in noisy conditions, making the ASR output a useful resource for subsequent tasks like named entity recognition also in difficult acoustic situations.
Summarizing News Clusters on the Basis of Thematic Chains
Authors: Natalia Loukachevitch, Aleksey Alekseev
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Document Summarization, Multi-Document Summarization, Text Summarization, Topic Models, Word Sense Disambiguation

In this paper we consider a method for extraction of sets of semantically similar language expressions representing different partici-pants of the text story â€• thematic chains. The method is based on the structural organization of news clusters and exploits comparison of various contexts of words. The word contexts are used as a basis for extracting multiword expressions and constructing thematic chains. The main difference of thematic chains in comparison with lexical chains is the basic principle of their construction: thematic chains are intended to model different participants (concrete or abstract) of the situation described in the analyzed texts, what means that elements of the same thematic chain cannot often co-occur in the same sentences of the texts under consideration. We evaluate our method on the multi-document summarization task
Because Size Does Matter: The Hamburg Dependency Treebank
Authors: Kilian A. Foth, Arne K{\"o}hn, Niels Beuck, Wolfgang Menzel
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

We present the Hamburg Dependency Treebank (HDT), which to our knowledge is the largest dependency treebank currently available. It consists of genuine dependency annotations, i. e. they have not been transformed from phrase structures. We explore characteristics of the treebank and compare it against others. To exemplify the benefit of large dependency treebanks, we evaluate different parsers on the HDT. In addition, a set of tools will be described which help working with and searching in the treebank.