pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised document zone identification using probabilistic graphical models
Authors: Andrea Varga, Daniel Preo{\c{t}}iuc-Pietro, Fabio Ciravegna
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5

Document zone identification aims to automatically classify sequences of text-spans (e.g. sentences) within a document into predefined zone categories. Current approaches to document zone identification mostly rely on supervised machine learning methods, which require a large amount of annotated data, which is often difficult and expensive to obtain. In order to overcome this bottleneck, we propose graphical models based on the popular Latent Dirichlet Allocation (LDA) model. The first model, which we call zoneLDA aims to cluster the sentences into zone classes using only unlabelled data. We also study an extension of zoneLDA called zoneLDAb, which makes distinction between common words and non-common words within the different zone types. We present results on two different domains: the scientific domain and the technical domain. For the latter one we propose a new document zone classification schema, which has been annotated over a collection of 689 documents, achieving a Kappa score of 85{\%}. Overall our experiments show promising results for both of the domains, outperforming the baseline model. Furthermore, on the technical domain the performance of the models are comparable to the supervised approach using the same feature sets. We thus believe that graphical models are a promising avenue of research for automatic document zoning.
Summarizing a multimodal set of documents in a Smart Room
Authors: Maria Fuentes, Horacio Rodr{\'\i}guez, Jordi Turmo
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Text Summarization

This article reports an intrinsic automatic summarization evaluation in the scientific lecture domain. The lecture takes place in a Smart Room that has access to different types of documents produced from different media. An evaluation framework is presented to analyze the performance of systems producing summaries answering a user need. Several ROUGE metrics are used and a manual content responsiveness evaluation was carried out in order to analyze the performance of the evaluated approaches. Various multilingual summarization approaches are analyzed showing that the use of different types of documents outperforms the use of transcripts. In fact, not using any part of the spontaneous speech transcription in the summary improves the performance of automatic summaries. Moreover, the use of semantic information represented in the different textual documents coming from different media helps to improve summary quality.
Empirical Comparisons of MASC Word Sense Annotations
Authors: Gerard de Melo, Collin F. Baker, Nancy Ide, Rebecca J. Passonneau, Christiane Fellbaum
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5

We analyze how different conceptions of lexical semantics affect sense annotations and how multiple sense inventories can be compared empirically, based on annotated text. Our study focuses on the MASC project, where data has been annotated using WordNet sense identifiers on the one hand, and FrameNet lexical units on the other. This allows us to compare the sense inventories of these lexical resources empirically rather than just theoretically, based on their glosses, leading to new insights. In particular, we compute contingency matrices and develop a novel measure, the Expected Jaccard Index, that quantifies the agreement between annotations of the same data based on two different resources even when they have different sets of categories.
Creating HAVIC: Heterogeneous Audio Visual Internet Collection
Authors: Stephanie Strassel, Am Morris, a, Jonathan Fiscus, Christopher Caruso, Haejoong Lee, Paul Over, James Fiumara, Barbara Shaw, Brian Antonishek, Martial Michel
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Event Detection

Linguistic Data Consortium and the National Institute of Standards and Technology are collaborating to create a large, heterogeneous annotated multimodal corpus to support research in multimodal event detection and related technologies. The HAVIC (Heterogeneous Audio Visual Internet Collection) Corpus will ultimately consist of several thousands of hours of unconstrained user-generated multimedia content. HAVIC has been designed with an eye toward providing increased challenges for both acoustic and video processing technologies, focusing on multi-dimensional variation inherent in user-generated multimedia content. To date the HAVIC corpus has been used to support the NIST 2010 and 2011 TRECVID Multimedia Event Detection (MED) Evaluations. Portions of the corpus are expected to be released in LDC's catalog in the coming year, with the remaining segments being published over time after their use in the ongoing MED evaluations.
Identifying bilingual Multi-Word Expressions for Statistical Machine Translation
Authors: Dhouha Bouamor, Nasredine Semmar, Pierre Zweigenbaum
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Machine Translation, Translation

MultiWord Expressions (MWEs) repesent a key issue for numerous applications in Natural Language Processing (NLP) especially for Machine Translation (MT). In this paper, we describe a strategy for detecting translation pairs of MWEs in a French-English parallel corpus. In addition we introduce three methods aiming to integrate extracted bilingual MWE S in M OSES, a phrase based Statistical Machine Translation (SMT) system. We experimentally show that these textual units can improve translation quality.
Multi-Layer Discourse Annotation of a Dutch Text Corpus
Authors: Gisela Redeker, Ildik{\'o} Berzl{\'a}novich, Nynke van der Vliet, Gosse Bouma, Markus Egg
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Segmentation, Text Segmentation

We have compiled a corpus of 80 Dutch texts from expository and persuasive genres, which we annotated for rhetorical and genre-specific discourse structure, and lexical cohesion with the goal of creating a gold standard for further research. The annota{\^A}{\neg}tions are based on a segmentation of the text in elementary discourse units that takes into account cues from syntax and punctuation. During the labor-intensive discourse-structure annotation (RST analysis), we took great care to thoroughly reconcile the initial analyses. That process and the availability of two independent initial analyses for each text allows us to analyze our disagreements and to assess the confusability of RST relations, and thereby improve the annotation guidelines and gather evidence for the classification of these relations into larger groups. We are using this resource for corpus-based studies of discourse relations, discourse markers, cohesion, and genre differences, e.g., the question of how discourse structure and lexical cohesion interact for different genres in the overall organization of texts. We are also exploring automatic text segmentation and semi-automatic discourse annotation.
Identifying Word Translations from Comparable Documents Without a Seed Lexicon
Authors: Reinhard Rapp, Serge Sharoff, Bogdan Babych
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Articles, Word Alignment

The extraction of dictionaries from parallel text corpora is an established technique. However, as parallel corpora are a scarce resource, in recent years the extraction of dictionaries using comparable corpora has obtained increasing attention. In order to find a mapping between languages, almost all approaches suggested in the literature rely on a seed lexicon. The work described here achieves competitive results without requiring such a seed lexicon. Instead it presupposes mappings between comparable documents in different languages. For some common types of textual resources (e.g. encyclopedias or newspaper texts) such mappings are either readily available or can be established relatively easily. The current work is based on Wikipedias where the mappings between languages are determined by the authors of the articles. We describe a neural-network inspired algorithm which first characterizes each Wikipedia article by a number of keywords, and then considers the identification of word translations as a variant of word alignment in a noisy environment. We present results and evaluations for eight language pairs involving Germanic, Romanic, and Slavic languages as well as Chinese.
The Language Archive --- a new hub for language resources
Authors: Sebastian Drude, Daan Broeder, Paul Trilsbeek, Peter Wittenburg
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Language Acquisition, Management

This contribution presents The Language Archive (TLA), a new unit at the MPI for Psycholinguistics, discussing the current developments in management of scientific data, considering the need for new data research infrastructures. Although several initiatives worldwide in the realm of language resources aim at the integration, preservation and mobilization of research data, the state of such scientific data is still often problematic. Data are often not well organized and archived and not described by metadata ― even unique data such as field-work observational data on endangered languages is still mostly on perishable carriers. New data centres are needed that provide trusted, quality-reviewed, persistent services and suitable tools and that take legal and ethical issues seriously. The CLARIN initiative has established criteria for suitable centres. TLA is in a good position to be one of such centres. It is based on three essential pillars: (1) A data archive; (2) management, access and annotation tools; (3) archiving and software expertise for collaborative projects. The archive hosts mostly observational data on small languages worldwide and language acquisition data, but also data resulting from experiments.
A Holistic Approach to Bilingual Sentence Fragment Extraction from Comparable Corpora
Authors: Mahdi Khademian, Kaveh Taghipour, Saab Mansour, Shahram Khadivi
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Boundary Detection, Machine Translation, Sentence, Translation

Achieving accurate translation, especially in multiple domain documents with statistical machine translation systems, requires more and more bilingual texts and this need becomes more critical when training such systems for language pairs with scarce training data. In the recent years, there have been some researches on new sources of parallel texts that are documents which are not necessarily parallel but are comparable. Since these methods search for possible translation equivalences in a greedy manner, they are unable to consider all possible parallel texts in comparable documents. This paper investigates a different approach for this need by considering relationships between all words of two comparable documents, which works fairly well even in the worst case of comparability. We represent each document pair in a matrix and then transform it to a new space to find parallel fragments. Evaluations show that the system is successful in extraction of useful fragment pairs.
Automatic Term Recognition Needs Multiple Evidence
Authors: Natalia Loukachevitch
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Information Retrieval, Term Extraction

In this paper we argue that the automatic term extraction procedure is an inherently multifactor process and the term extraction models needs to be based on multiple features including a specific type of a terminological resource under development. We proposed to use three types of features for extraction of two-word terms and showed that all these types of features are useful for term extraction. The set of features includes new features such as features extracted from an existing domain-specific thesaurus and features based on Internet search results. We studied the set of features for term extraction in two different domains and showed that the combination of several types of features considerably enhances the quality of the term extraction procedure. We found that for developing term extraction models in a specific domain, it is important to take into account some properties of the domain.
Evaluation of Discourse Relation Annotation in the Hindi Discourse Relation Bank
Authors: Sudheer Kolachina, Rashmi Prasad, Dipti Misra Sharma, Aravind Joshi
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Relation

We describe our experiments on evaluating recently proposed modifications to the discourse relation annotation scheme of the Penn Discourse Treebank (PDTB), in the context of annotating discourse relations in Hindi Discourse Relation Bank (HDRB). While the proposed modifications were driven by the desire to introduce greater conceptual clarity in the PDTB scheme and to facilitate better annotation quality, our findings indicate that overall, some of the changes render the annotation task much more difficult for the annotators, as also reflected in lower inter-annotator agreement for the relevant sub-tasks. Our study emphasizes the importance of best practices in annotation task design and guidelines, given that a major goal of an annotation effort should be to achieve maximally high agreement between annotators. Based on our study, we suggest modifications to the current version of the HDRB, to be incorporated in our future annotation work.
A methodology for the extraction of information about the usage of formulaic expressions in scientific texts
Authors: Hannah Kermes
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5

In this paper, we present a methodology for the extraction of formulaic expressions, which goes beyond the mere extraction of candidate patterns. Using a pipeline we are able to extract information about the usage of formulaic expressions automatically from text corpora. According to Biber and Barbieri (2007) formulaic expressions are important building blocks of discourse in spoken and written registers. The automatic extraction procedure can help to investigate the usage and function of these recurrent patterns in different registers and domains. Formulaic expressions are commonplace not only in every- day language but also in scientific writing. Patterns such as 'in this paper', 'the number of', 'on the basis of' are often used by scientists to convey research interests, the theoretical basis of their studies, results of experiments, sci- entific findings as well as conclusions and are used as dis- course organizers. For Hyland (2008) they help to shape meanings in specific context and contribute to our sense of coherence in a text. We are interested in: (i) which and what type of formulaic expressions are used in scientific texts? (ii) the distribution of formulaic expression across different scien- tific disciplines, (iii) where do formulaic expressions occur within a text?
CLCM - A Linguistic Resource for Effective Simplification of Instructions in the Crisis Management Domain and its Evaluations
Authors: Irina Temnikova, Constantin Orasan, Ruslan Mitkov
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Machine Translation, Management, Reading Comprehension, Text Simplification, Translation

Due to the increasing number of emergency situations which can have substantial consequences, both financially and fatally, the Crisis Management (CM) domain is developing at an exponential speed. The efficient management of emergency situations relies on clear communication between all of the participants in a crisis situation. For these reasons the Text Complexity (TC) of the CM domain needed to be investigated and showed that CM domain texts exhibit high TC levels. This article presents a new linguistic resource in the form of Controlled Language (CL) guidelines for manual text simplification in the CM domain which aims to address high TC in the CM domain and produce clear messages to be used in crisis situations. The effectiveness of the resource has been tested via evaluation from several different perspectives important for the domain. The overall results show that the CLCM simplification has a positive impact on TC, reading comprehension, manual translation and machine translation. Additionally, an investigation of the cognitive difficulty in applying manual simplification operations led to interesting discoveries. This article provides details of the evaluation methods, the conducted experiments, their results and indications about future work.
From Grammar Rule Extraction to Treebanking: A Bootstrapping Approach
Authors: Masood Ghayoomi
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5

Most of the reliable language resources are developed via human supervision. Developing supervised annotated data is hard and tedious, and it will be very time consuming when it is done totally manually; as a result, various types of annotated data, including treebanks, are not available for many languages. Considering that a portion of the language is regular, we can define regular expressions as grammar rules to recognize the strings which match the regular expressions, and reduce the human effort to annotate further unseen data. In this paper, we propose an incremental bootstrapping approach via extracting grammar rules when no treebank is available in the first step. Since Persian suffers from lack of available data sources, we have applied our method to develop a treebank for this language. Our experiment shows that this approach significantly decreases the amount of manual effort in the annotation process while enlarging the treebank.
Detecting Japanese Compound Functional Expressions using Canonical/Derivational Relation
Authors: Takafumi Suzuki, Yusuke Abe, Itsuki Toyota, Takehito Utsuro, Suguru Matsuyoshi, Masatoshi Tsuchiya
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Chunking, Machine Translation, Relation

The Japanese language has various types of functional expressions. In order to organize Japanese functional expressions with various surface forms, a lexicon of Japanese functional expressions with hierarchical organization was compiled. This paper proposes how to design the framework of identifying more than 16,000 functional expressions in Japanese texts by utilizing hierarchical organization of the lexicon. In our framework, more than 16,000 functional expressions are roughly divided into canonical / derived functional expressions. Each derived functional expression is intended to be identified by referring to the most similar occurrence of its canonical expression. In our framework, contextual occurrence information of much fewer canonical expressions are expanded into the whole forms of derived expressions, to be utilized when identifying those derived expressions. We also empirically show that the proposed method can correctly identify more than 80{\%} of the functional / content usages only with less than 38,000 training instances of manually identified canonical expressions.
Improving K-Nearest Neighbor Efficacy for Farsi Text Classification
Authors: Mohammad Hossein Elahimanesh, Behrouz Minaei, Hossein Malekinezhad
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Articles, Classification, General Classification, Information Retrieval, Quantization, Text Categorization, text-classification, Text Classification

One of the common processes in the field of text mining is text classification. Because of the complex nature of Farsi language, words with separate parts and combined verbs, the most of text classification systems are not applicable to Farsi texts. K-Nearest Neighbors (KNN) is one of the most popular used methods for text classification and presents good performance in experiments on different datasets. A method to improve the classification performance of KNN is proposed in this paper. Effects of removing or maintaining stop words, applying N-Grams with different lengths are also studied. For this study, a portion of a standard Farsi corpus called Hamshahri1 and articles of some archived newspapers are used. As the results indicate, classification efficiency improves by applying this approach especially when eight-grams indexing method and removing stop words are applied. Using N-grams with lengths more than 3 characters, presented very encouraging results for Farsi text classification. The Results of classification using our method are compared with the results obtained by mentioned related works.
ULex: new data models and a mobile environment for corpus enrichment.
Authors: Dafydd Gibbon
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Speech Synthesis

The Ubiquitous Lexicon concept (ULex) has two sides. In the first kind of ubiquity, ULex combines prelexical corpus based lexicon extraction and formatting techniques from speech technology and corpus linguistics for both language documentation and basic speech technology (e.g. speech synthesis), and proposes new XML models for the basic datatypes concerned, in order to enable standardisastion and data interchange in these areas. The prelexical data types range from basic wordlists through diphone tables to concordance and interlinear glossing structures. While several proposals for standardising XML models of lexicon types are available, these more basic pre-lexical, data types, which are important in lexical acquisition, have received little attention. In the second area of ubiquity, ULex is implemented in a novel mobile environment to enable collaborative cross-platform use via a web application, either on the internet or, via a local hotspot, on an intranet, which runs not only on standard PC types but also on tablet computers and smartphones and is thereby also rendered truly ubiquitous in a geographical sense.
UniDic for Early Middle Japanese: a Dictionary for Morphological Analysis of Classical Japanese
Authors: Toshinobu Ogiso, Mamoru Komachi, Yasuharu Den, Yuji Matsumoto
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Morphological Analysis

In order to construct an annotated diachronic corpus of Japanese, we propose to create a new dictionary for morphological analysis of Early Middle Japanese (Classical Japanese) based on UniDic, a dictionary for Contemporary Japanese. Differences between the Early Middle Japanese and Contemporary Japanese, which prevent a na{\"\i}ve adaptation of UniDic to Early Middle Japanese, are found at the levels of lexicon, morphology, grammar, orthography and pronunciation. In order to overcome these problems, we extended dictionary entries and created a training corpus of Early Middle Japanese to adapt UniDic for Contemporary Japanese to Early Middle Japanese. Experimental results show that the proposed UniDic-EMJ, a new dictionary for Early Middle Japanese, achieves as high accuracy (97{\%}) as needed for the linguistic research on lexicon and grammar in Japanese classical text analysis.
A platform-independent user-friendly dictionary from Italian to LIS
Authors: Umar Shoaib, Nadeem Ahmad, Paolo Prinetto, Gabriele Tiotto
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Gesture Recognition, Sign Language Recognition, Sign Language Translation

The Lack of written representation for Italian Sign Language (LIS) makes it difficult to do perform tasks like looking up a new word in a dictionary. Most of the paper dictionaries show LIS signs in drawings or pictures. It's not a simple proposition to understand the meaning of sign from paper dictionaries unless one already knows the meanings. This paper presents the LIS dictionary which provides the facility to translate Italian text into sign language. LIS signs are shown as video animations performed by a virtual character. The LIS dictionary provides the integration with MultiWordNet database. The integration with MultiWordNet allows a rich extension with the meanings and senses of the words existing in MultiWordNet. The dictionary allows users to acquire information about lemmas, synonyms and synsets in the Sign Language (SL). The application is platform independent and can be used on any operating system. The results of input lemmas are displayed in groups of grammatical categories.