pwc-archive/papers-with-abstracts huggingface dataset:
Modelling Irony in Twitter: Feature Analysis and Evaluation
Authors: Francesco Barbieri, Horacio Saggion
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

Irony, a creative use of language, has received scarce attention from the computational linguistics research point of view. We propose an automatic system capable of detecting irony with good accuracy in the social network Twitter. Twitter allows users to post short messages (140 characters) which usually do not follow the expected rules of the grammar, users tend to truncate words and use particular punctuation. For these reason automatic detection of Irony in Twitter is not trivial and requires specific linguistic tools. We propose in this paper a new set of experiments to assess the relevance of the features included in our model. Our model does not include words or sequences of words as features, aiming to detect inner characteristic of Irony.
DBpedia Domains: augmenting DBpedia with domain information
Authors: Gregor Titze, Volha Bryl, C{\"a}cilia Zirn, Simone Paolo Ponzetto
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Clustering, Open-Domain Question Answering, Question Answering

We present an approach for augmenting DBpedia, a very large ontology lying at the heart of the Linked Open Data (LOD) cloud, with domain information. Our approach uses the thematic labels provided for DBpedia entities by Wikipedia categories, and groups them based on a kernel based k-means clustering algorithm. Experiments on gold-standard data show that our approach provides a first solution to the automatic annotation of DBpedia entities with domain labels, thus providing the largest LOD domain-annotated ontology to date.
Mining a multimodal corpus for non-verbal behavior sequences conveying attitudes
Authors: Mathieu Chollet, Magalie Ochs, Catherine Pelachaud
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

Interpersonal attitudes are expressed by non-verbal behaviors on a variety of different modalities. The perception of these behaviors is influenced by how they are sequenced with other behaviors from the same person and behaviors from other interactants. In this paper, we present a method for extracting and generating sequences of non-verbal signals expressing interpersonal attitudes. These sequences are used as part of a framework for non-verbal expression with Embodied Conversational Agents that considers different features of non-verbal behavior: global behavior tendencies, interpersonal reactions, sequencing of non-verbal signals, and communicative intentions. Our method uses a sequence mining technique on an annotated multimodal corpus to extract sequences characteristic of different attitudes. New sequences of non-verbal signals are generated using a probabilistic model, and evaluated using the previously mined sequences.
Biomedical entity extraction using machine-learning based approaches
Authors: Cyril Grouin
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: BIG-bench Machine Learning, Coreference Resolution, Entity Extraction using GAN, Named Entity Recognition (NER)

In this paper, we present the experiments we made to process entities from the biomedical domain. Depending on the task to process, we used two distinct supervised machine-learning techniques: Conditional Random Fields to perform both named entity identification and classification, and Maximum Entropy to classify given entities. Machine-learning approaches outperformed knowledge-based techniques on categories where sufficient annotated data was available. We showed that the use of external features (unsupervised clusters, information from ontology and taxonomy) improved the results significantly.
Parsing Heterogeneous Corpora with a Rich Dependency Grammar
Authors: Achim Stein
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Dependency Parsing

Grammar models conceived for parsing purposes are often poorer than models that are motivated linguistically. We present a grammar model which is linguistically satisfactory and based on the principles of traditional dependency grammar. We show how a state-of-the-art dependency parser (mate tools) performs with this model, trained on the Syntactic Reference Corpus of Medieval French (SRCMF), a manually annotated corpus of medieval (Old French) texts. We focus on the problems caused by small and heterogeneous training sets typical for corpora of older periods. The result is the first publicly available dependency parser for Old French. On a 90/10 training/evaluation split of eleven OF texts (206000 words), we obtained an UAS of 89.68{\%} and a LAS of 82.62{\%}. Three experiments showed how heterogeneity, typical of medieval corpora, affects the parsing results: (a) a {`}one-on-one{'} cross evaluation for individual texts, (b) a {`}leave-one-out{'} cross evaluation, and (c) a prose/verse cross evaluation.
ML-Optimization of Ported Constraint Grammars
Authors: Eckhard Bick
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Domain Adaptation, Morphological Analysis

In this paper, we describe how a Constraint Grammar with linguist-written rules can be optimized and ported to another language using a Machine Learning technique. The effects of rule movements, sorting, grammar-sectioning and systematic rule modifications are discussed and quantitatively evaluated. Statistical information is used to provide a baseline and to enhance the core of manual rules. The best-performing parameter combinations achieved part-of-speech F-scores of over 92 for a grammar ported from English to Danish, a considerable advance over both the statistical baseline (85.7), and the raw ported grammar (86.1). When the same technique was applied to an existing native Danish CG, error reduction was 10{\%} (F=96.94).
A Multi-Cultural Repository of Automatically Discovered Linguistic and Conceptual Metaphors
Authors: Samira Shaikh, Tomek Strzalkowski, Ting Liu, George Aaron Broadwell, Boris Yamrom, Sarah Taylor, Laurie Feldman, Kit Cho, Umit Boz, Ignacio Cases, Yuliya Peshkova, Ching-Sheng Lin
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

In this article, we present details about our ongoing work towards building a repository of Linguistic and Conceptual Metaphors. This resource is being developed as part of our research effort into the large-scale detection of metaphors from unrestricted text. We have stored a large amount of automatically extracted metaphors in American English, Mexican Spanish, Russian and Iranian Farsi in a relational database, along with pertinent metadata associated with these metaphors. A substantial subset of the contents of our repository has been systematically validated via rigorous social science experiments. Using information stored in the repository, we are able to posit certain claims in a cross-cultural context about how peoples in these cultures (America, Mexico, Russia and Iran) view particular concepts related to Governance and Economic Inequality through the use of metaphor. Researchers in the field can use this resource as a reference of typical metaphors used across these cultures. In addition, it can be used to recognize metaphors of the same form or pattern, in other domains of research.
First approach toward Semantic Role Labeling for Basque
Authors: Haritz Salaberri, Olatz Arregi, Be{\~n}at Zapirain
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: feature selection, Machine Translation, Question Answering, Semantic Parsing, Semantic Role Labeling, Text Summarization

In this paper, we present the first Semantic Role Labeling system developed for Basque. The system is implemented using machine learning techniques and trained with the Reference Corpus for the Processing of Basque (EPEC). In our experiments the classifier that offers the best results is based on Support Vector Machines. Our system achieves 84.30 F1 score in identifying the PropBank semantic role for a given constituent and 82.90 F1 score in identifying the VerbNet role. Our study establishes a baseline for Basque SRL. Although there are no directly comparable systems for English we can state that the results we have achieved are quite good. In addition, we have performed a Leave-One-Out feature selection procedure in order to establish which features are the worthiest regarding argument classification. This will help smooth the way for future stages of Basque SRL and will help draw some of the guidelines of our research.
Generating a Lexicon of Errors in Portuguese to Support an Error Identification System for Spanish Native Learners
Authors: Lianet Sep{\'u}lveda Torres, Magali Sanches Duran, S Alu{\'\i}sio, ra
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Translation

Portuguese is a less resourced language in what concerns foreign language learning. Aiming to inform a module of a system designed to support scientific written production of Spanish native speakers learning Portuguese, we developed an approach to automatically generate a lexicon of wrong words, reproducing language transfer errors made by such foreign learners. Each item of the artificially generated lexicon contains, besides the wrong word, the respective Spanish and Portuguese correct words. The wrong word is used to identify the interlanguage error and the correct Spanish and Portuguese forms are used to generate the suggestions. Keeping control of the correct word forms, we can provide correction or, at least, useful suggestions for the learners. We propose to combine two automatic procedures to obtain the error correction: i) a similarity measure and ii) a translation algorithm based on aligned parallel corpus. The similarity-based method achieved a precision of 52{\%}, whereas the alignment-based method achieved a precision of 90{\%}. In this paper we focus only on interlanguage errors involving suffixes that have different forms in both languages. The approach, however, is very promising to tackle other types of errors, such as gender errors.
xLiD-Lexica: Cross-lingual Linked Data Lexica
Authors: Lei Zhang, Michael F{\"a}rber, Achim Rettinger
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Cross-Lingual Entity Linking, Entity Linking, Question Answering, text annotation, Text Generation

In this paper, we introduce our cross-lingual linked data lexica, called xLiD-Lexica, which are constructed by exploiting the multilingual Wikipedia and linked data resources from Linked Open Data (LOD). We provide the cross-lingual groundings of linked data resources from LOD as RDF data, which can be easily integrated into the LOD data sources. In addition, we build a SPARQL endpoint over our xLiD-Lexica to allow users to easily access them using SPARQL query language. Multilingual and cross-lingual information access can be facilitated by the availability of such lexica, e.g., allowing for an easy mapping of natural language expressions in different languages to linked data resources from LOD. Many tasks in natural language processing, such as natural language generation, cross-lingual entity linking, text annotation and question answering, can benefit from our xLiD-Lexica.
A Study on Expert Sourcing Enterprise Question Collection and Classification
Authors: Yuan Luo, Thomas Boucher, Tolga Oral, David Osofsky, Sara Weber
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: General Classification, Question Answering

Large enterprises, such as IBM, accumulate petabytes of free-text data within their organizations. To mine this big data, a critical ability is to enable meaningful question answering beyond keywords search. In this paper, we present a study on the characteristics and classification of IBM sales questions. The characteristics are analyzed both semantically and syntactically, from where a question classification guideline evolves. We adopted an enterprise level expert sourcing approach to gather questions, annotate questions based on the guideline and manage the quality of annotations via enhanced inter-annotator agreement analysis. We developed a question feature extraction system and experimented with rule-based, statistical and hybrid question classifiers. We share our annotated corpus of questions and report our experimental results. Statistical classifiers separately based on n-grams and hand-crafted rule features give reasonable macro-f1 scores at 61.7{\%} and 63.1{\%} respectively. Rule based classifier gives a macro-f1 at 77.1{\%}. The hybrid classifier with n-gram and rule features using a second guess model further improves the macro-f1 to 83.9{\%}.
Annotating Relation Mentions in Tabloid Press
Authors: Hong Li, Sebastian Krause, Feiyu Xu, Hans Uszkoreit, Robert Hummel, Veselina Mironova
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Articles, Relation, Relation Extraction, Sentence

This paper presents a new resource for the training and evaluation needed by relation extraction experiments. The corpus consists of annotations of mentions for three semantic relations: marriage, parentâ€•child, siblings, selected from the domain of biographic facts about persons and their social relationships. The corpus contains more than one hundred news articles from Tabloid Press. In the current corpus, we only consider the relation mentions occurring in the individual sentences. We provide multi-level annotations which specify the marked facts from relation, argument, entity, down to the token level, thus allowing for detailed analysis of linguistic phenomena and their interactions. A generic markup tool Recon developed at the DFKI LT lab has been utilised for the annotation task. The corpus has been annotated by two human experts, supported by additional conflict resolution conducted by a third expert. As shown in the evaluation, the annotation is of high quality as proved by the stated inter-annotator agreements both on sentence level and on relationmention level. The current corpus is already in active use in our research for evaluation of the relation extraction performance of our automatically learned extraction patterns.
Efficient Reuse of Structured and Unstructured Resources for Ontology Population
Authors: Chetana Gavankar, Ashish Kulkarni, Ganesh Ramakrishnan
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Information Retrieval, Retrieval, text annotation

We study the problem of ontology population for a domain ontology and present solutions based on semi-automatic techniques. A domain ontology for an organization, often consists of classes whose instances are either specific to, or independent of the organization. E.g. in an academic domain ontology, classes like Professor, Department could be organization (university) specific, while Conference, Programming languages are organization independent. This distinction allows us to leverage data sources both―within the organization and those in the Internet ― to extract entities and populate an ontology. We propose techniques that build on those for open domain IE. Together with user input, we show through comprehensive evaluation, how these semi-automatic techniques achieve high precision. We experimented with the academic domain and built an ontology comprising of over 220 classes. Intranet documents from five universities formed our organization specific corpora and we used open domain knowledge bases like Wikipedia, Linked Open Data, and web pages from the Internet as the organization independent data sources. The populated ontology that we built for one of the universities comprised of over 75,000 instances. We adhere to the semantic web standards and tools and make the resources available in the OWL format. These could be useful for applications such as information extraction, text annotation, and information retrieval.
Mapping Diatopic and Diachronic Variation in Spoken Czech: The ORTOFON and DIALEKT Corpora
Authors: Marie Kop{\v{r}}ivov{\'a}, Hana Gol{\'a}{\v{n}}ov{\'a}, Petra Klime{\v{s}}ov{\'a}, David Luke{\v{s}}
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Speech Recognition

ORTOFON and DIALEKT are two corpora of spoken Czech (recordings + transcripts) which are currently being built at the Institute of the Czech National Corpus. The first one (ORTOFON) continues the tradition of the CNC{'}s ORAL series of spoken corpora by focusing on collecting recordings of unscripted informal spoken interactions ({``}prototypically spoken texts{''}), but also provides new features, most notably an annotation scheme with multiple tiers per speaker, including orthographic and phonetic transcripts and allowing for a more precise treatment of overlapping speech. Rich speaker- and situation-related metadata are also collected for possible use as factors in sociolinguistic analyses. One of the stated goals is to make the data in the corpus balanced with respect to a subset of these. The second project, DIALEKT, consists in annotating (in a way partially compatible with the ORTOFON corpus) and providing electronic access to historical (1960s--80s) dialect recordings, mainly of a monological nature, from all over the Czech Republic. The goal is to integrate both corpora into one map-based browsing interface, allowing an intuitive and informative spatial visualization of query results or dialect feature maps, confrontation with isoglosses previously established through the effort of dialectologists etc.
Corpus and Evaluation of Handwriting Recognition of Historical Genealogical Records
Authors: Patrick Schone, Heath Nielson, Mark Ward
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Handwriting Recognition

Over the last few decades, significant strides have been made in handwriting recognition (HR), which is the automatic transcription of handwritten documents. HR often focuses on modern handwritten material, but in the electronic age, the volume of handwritten material is rapidly declining. However, we believe HR is on the verge of having major application to historical record collections. In recent years, archives and genealogical organizations have conducted huge campaigns to transcribe valuable historical record content with such transcription being largely done through human-intensive labor. HR has the potential of revolutionizing these transcription endeavors. To test the hypothesis that this technology is close to applicability, and to provide a testbed for reducing any accuracy gaps, we have developed an evaluation paradigm for historical record handwriting recognition. We created a huge test corpus consisting of four historical data collections of four differing genres and three languages. In this paper, we provide the details of these extensive resources which we intend to release to the research community for further study. Since several research organizations have already participated in this evaluation, we also show initial results and comparisons to human levels of performance.
Reusing Swedish FrameNet for training semantic roles
Authors: Ildik{\'o} Pil{\'a}n, Elena Volodina
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Multiple-choice

In this article we present the first experiences of reusing the Swedish FrameNet (SweFN) as a resource for training semantic roles. We give an account of the procedure we used to adapt SweFN to the needs of students of Linguistics in the form of an automatically generated exercise. During this adaptation, the mapping of the fine-grained distinction of roles from SweFN into learner-friendlier coarse-grained roles presented a major challenge. Besides discussing the details of this mapping, we describe the resulting multiple-choice exercise and its graphical user interface. The exercise was made available through L{\"a}rka, an online platform for students of Linguistics and learners of Swedish as a second language. We outline also aspects underlying the selection of the incorrect answer options which include semantic as well as frequency-based criteria. Finally, we present our own observations and initial user feedback about the applicability of such a resource in the pedagogical domain. Students{'} answers indicated an overall positive experience, the majority found the exercise useful for learning semantic roles.
A Database of Freely Written Texts of German School Students for the Purpose of Automatic Spelling Error Classification
Authors: Kay Berkling, Johanna Fay, Masood Ghayoomi, Katrin Hein, R{\'e}mi Lavalley, Ludwig Linhuber, Sebastian St{\"u}ker
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: General Classification

The spelling competence of school students is best measured on freely written texts, instead of pre-determined, dictated texts. Since the analysis of the error categories in these kinds of texts is very labor intensive and costly, we are working on an automatic systems to perform this task. The modules of the systems are derived from techniques from the area of natural language processing, and are learning systems that need large amounts of training data. To obtain the data necessary for training and evaluating the resulting system, we conducted data collection of freely written, German texts by school children. 1,730 students from grade 1 through 8 participated in this data collection. The data was transcribed electronically and annotated with their corrected version. This resulted in a total of 14,563 sentences that can now be used for research regarding spelling diagnostics. Additional meta-data was collected regarding writers{'} language biography, teaching methodology, age, gender, and school year. In order to do a detailed manual annotation of the categories of the spelling errors committed by the students we developed a tool specifically tailored to the task.
PACE Corpus: a multilingual corpus of Polarity-annotated textual data from the domains Automotive and CEllphone
Authors: Christian Haenig, Andreas Niekler, Carsten Wuensch
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Sentiment Analysis

In this paper, we describe a publicly available multilingual evaluation corpus for phrase-level Sentiment Analysis that can be used to evaluate real world applications in an industrial context. This corpus contains data from English and German Internet forums (1000 posts each) focusing on the automotive domain. The major topic of the corpus is connecting and using cellphones to/in cars. The presented corpus contains different types of annotations: objects (e.g. my car, my new cellphone), features (e.g. address book, sound quality) and phrase-level polarities (e.g. the best possible automobile, big problem). Each of the posts has been annotated by at least four different annotators â€• these annotations are retained in their original form. The reliability of the annotations is evaluated by inter-annotator agreement scores. Besides the corpus data and format, we provide comprehensive corpus statistics. This corpus is one of the first lexical resources focusing on real world applications that analyze the voice of the customer which is crucial for various industrial use cases.
Szeged Corpus 2.5: Morphological Modifications in a Manually POS-tagged Hungarian Corpus
Authors: Veronika Vincze, Viktor Varga, Katalin Ilona Simk{\'o}, J{\'a}nos Zsibrita, {\'A}goston Nagy, Rich{\'a}rd Farkas, J{\'a}nos Csirik
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: POS

The Szeged Corpus is the largest manually annotated database containing the possible morphological analyses and lemmas for each word form. In this work, we present its latest version, Szeged Corpus 2.5, in which the new harmonized morphological coding system of Hungarian has been employed and, on the other hand, the majority of misspelled words have been corrected and tagged with the proper morphological code. New morphological codes are introduced for participles, causative / modal / frequentative verbs, adverbial pronouns and punctuation marks, moreover, the distinction between common and proper nouns is eliminated. We also report some statistical data on the frequency of the new morphological codes. The new version of the corpus made it possible to train magyarlanc, a data-driven POS-tagger of Hungarian on a dataset with the new harmonized codes. According to the results, magyarlanc is able to achieve a state-of-the-art accuracy score on the 2.5 version as well.