pwc-archive/papers-with-abstracts huggingface dataset:
Dealing with unknown words in statistical machine translation
Authors: Jo{\~a}o Silva, Lu{\'\i}sa Coheur, {\^A}ngela Costa, Isabel Trancoso
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Machine Translation, Translation, Transliteration

In Statistical Machine Translation, words that were not seen during training are unknown words, that is, words that the system will not know how to translate. In this paper we contribute to this research problem by profiting from orthographic cues given by words. Thus, we report a study of the impact of word distance metrics in cognates' detection and, in addition, on the possibility of obtaining possible translations of unknown words through Logical Analogy. Our approach is tested in the translation of corpora from Portuguese to English (and vice-versa).
A disambiguation resource extracted from Wikipedia for semantic annotation
Authors: Eric Charton, Michel Gagnon
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Information Retrieval, Named Entity Recognition (NER), Retrieval

The Semantic Annotation (SA) task consists in establishing the relation between a textual entity (word or group of words designating a named entity of the real world or a concept) and its corresponding entity in an ontology. The main difficulty of this task is that a textual entity might be highly polysemic and potentially related to many different ontological representations. To solve this specific problem, various Information Retrieval techniques can be used. Most of those involves contextual words to estimate wich exact textual entity have to be recognized. In this paper, we present a resource of contextual words that can be used by IR algorithms to establish a link between a named entity (NE) in a text and an entry point to its semantic description in the LinkedData Network.
PET: a Tool for Post-editing and Assessing Machine Translation
Authors: Wilker Aziz, Sheila Castilho, Lucia Specia
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Machine Translation, Sentence, Translation

Given the significant improvements in Machine Translation (MT) quality and the increasing demand for translations, post-editing of automatic translations is becoming a popular practice in the translation industry. It has been shown to allow for much larger volumes of translations to be produced, saving time and costs. In addition, the post-editing of automatic translations can help understand problems in such translations and this can be used as feedback for researchers and developers to improve MT systems. Finally, post-editing can be used as a way of evaluating the quality of translations in terms of how much post-editing effort these translations require. We describe a standalone tool that has two main purposes: facilitate the post-editing of translations from any MT system so that they reach publishable quality and collect sentence-level information from the post-editing process, e.g.: post-editing time and detailed keystroke statistics.
Enriching the ISST-TANL Corpus with Semantic Frames
Authors: Aless Lenci, ro, Simonetta Montemagni, Giulia Venturi, Maria Grazia Cutrull{\`a}
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5

The paper describes the design and the results of a manual annotation methodology devoted to enrich the ISST--TANL Corpus, derived from the Italian Syntactic--Semantic Treebank (ISST), with Semantic Frames information. The main issues encountered in applying the English FrameNet annotation criteria to a corpus of Italian language are discussed together with the choice of anchoring the semantic annotation layer to the underlying dependency syntactic structure. The results of a case study aimed at extending and specialising this methodology for the annotation of a corpus of legislative texts are also discussed.
Developing Partially-Transcribed Speech Corpus from Edited Transcriptions
Authors: Kengo Ohta, Masatoshi Tsuchiya, Seiichi Nakagawa
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Language Modelling

Large-scale spontaneous speech corpora are crucial resource for various domains of spoken language processing. However, the available corpora are usually limited because their construction cost is quite expensive especially in transcribing speech precisely. On the other hand, loosely transcribed corpora like shorthand notes, meeting records and closed captions are more widely available than precisely transcribed ones, because their imperfectness reduces their construction cost. Because these corpora contain both precisely transcribed regions and edited regions, it is difficult to use them directly as speech corpora for learning acoustic models. Under this background, we have been considering to build an efficient semi-automatic framework to convert loose transcriptions to precise ones. This paper describes an improved automatic detection method of precise regions from loosely transcribed corpora for the above framework. Our detection method consists of two steps: the first step is a force alignment between loose transcriptions and their utterances to discover the corresponding utterance for the certain loose transcription, and the second step is a detector of precise regions with a support vector machine using several features obtained from the first step. Our experimental result shows that our method achieves a high accuracy of detecting precise regions, and shows that the precise regions extracted by our method are effective as training labels of lightly supervised speaker adaptation.
Construction of the Turkish National Corpus (TNC)
Authors: Ye{\c{s}}im Aksan, Mustafa Aksan, Ahmet Koltuksuz, Taner Sezer, {\"U}mit Mersinli, Umut Ufuk Demirhan, Hakan Y{\i}lmazer, G{\"u}ls{\"u}m Atasoy, Seda {\"O}z, {\.I}pek Y{\i}ld{\i}z, {\"O}zlem Kurto{\u{g}}lu
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Management

This paper addresses theoretical and practical issues experienced in the construction of Turkish National Corpus (TNC). TNC is designed to be a balanced, large scale (50 million words) and general-purpose corpus for contemporary Turkish. It has benefited from previous practices and efforts for the construction of corpora. In this sense, TNC generally follows the framework of British National Corpus, yet necessary adjustments in corpus design of TNC are made whenever needed. All throughout the process, different types of open-source software are used for specific tasks, and the resulting corpus is a free resource for non-commercial use. This paper presents TNC's design features, web-based corpus management system, carefully planned workflow and its web-based user-friendly search interface.
Building a learner corpus
Authors: Jirka Hana, Alex Rosen, R, Barbora {\v{S}}tindlov{\'a}, Petr J{\"a}ger
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Language Acquisition

The paper describes a corpus of texts produced by non-native speakers of Czech. We discuss its annotation scheme, consisting of three interlinked levels to cope with a wide range of error types present in the input. Each level corrects different types of errors; links between the levels allow capturing errors in word order and complex discontinuous expressions. Errors are not only corrected, but also classified. The annotation scheme is tested on a doubly-annotated sample of approx. 10,000 words with fair inter-annotator agreement results. We also explore options of application of automated linguistic annotation tools (taggers, spell checkers and grammar checkers) on the learner text to support or even substitute manual annotation.
The ML4HMT Workshop on Optimising the Division of Labour in Hybrid Machine Translation
Authors: Christian Federmann, Eleftherios Avramidis, Marta R. Costa-juss{\`a}, Josef van Genabith, Maite Melero, Pavel Pecina
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Language Modelling, Machine Translation, Translation

We describe the Shared Task on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid Machine Translation (ML4HMT) which aims to foster research on improved system combination approaches for machine translation (MT). Participants of the challenge are requested to build hybrid translations by combining the output of several MT systems of different types. We first describe the ML4HMT corpus used in the shared task, then explain the XLIFF-based annotation format we have designed for it, and briefly summarize the participating systems. Using both automated metrics scores and extensive manual evaluation, we discuss the individual performance of the various systems. An interesting result from the shared task is the fact that we were able to observe different systems winning according to the automated metrics scores when compared to the results from the manual evaluation. We conclude by summarising the first edition of the challenge and by giving an outlook to future work.
The META-SHARE Metadata Schema for the Description of Language Resources
Authors: Maria Gavrilidou, Penny Labropoulou, Elina Desipri, Stelios Piperidis, Haris Papageorgiou, Monica Monachini, Francesca Frontini, Thierry Declerck, Gil Francopoulo, Victoria Arranz, Valerie Mapelli
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Machine Translation, Retrieval

This paper presents a metadata model for the description of language resources proposed in the framework of the META-SHARE infrastructure, aiming to cover both datasets and tools/technologies used for their processing. It places the model in the overall framework of metadata models, describes the basic principles and features of the model, elaborates on the distinction between minimal and maximal versions thereof, briefly presents the integrated environment supporting the LRs description and search and retrieval processes and concludes with work to be done in the future for the improvement of the model.
Fine-grained German Sentiment Analysis on Social Media
Authors: Saeedeh Momtazi
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Sentiment Analysis

Expressing opinions and emotions on social media becomes a frequent activity in daily life. People express their opinions about various targets via social media and they are also interested to know about other opinions on the same target. Automatically identifying the sentiment of these texts and also the strength of the opinions is an enormous help for people and organizations who are willing to use this information for their goals. In this paper, we present a rule-based approach for German sentiment analysis. The proposed model provides a fine-grained annotation for German texts, which represents the sentiment strength of the input text using two scores: positive and negative. The scores show that if the text contains any positive or negative opinion as well as the strength of each positive and negative opinions. To this aim, a German opinion dictionary of 1,864 words is prepared and compared with other opinion dictionaries for German. We also introduce a new dataset for German sentiment analysis. The dataset contains 500 short texts from social media about German celebrities and is annotated by three annotators. The results show that the proposed unsupervised model outperforms the supervised machine learning techniques. Moreover, the new dictionary performs better than other German opinion dictionaries.
Alignment-based reordering for SMT
Authors: Maria Holmqvist, Sara Stymne, Lars Ahrenberg, Magnus Merkel
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Machine Translation, Part-Of-Speech Tagging, Translation, Word Alignment

We present a method for improving word alignment quality for phrase-based statistical machine translation by reordering the source text according to the target word order suggested by an initial word alignment. The reordered text is used to create a second word alignment which can be an improvement of the first alignment, since the word order is more similar. The method requires no other pre-processing such as part-of-speech tagging or parsing. We report improved Bleu scores for English-to-German and English-to-Swedish translation. We also examined the effect on word alignment quality and found that the reordering method increased recall while lowering precision, which partly can explain the improved Bleu scores. A manual evaluation of the translation output was also performed to understand what effect our reordering method has on the translation system. We found that where the system employing reordering differed from the baseline in terms of having more words, or a different word order, this generally led to an improvement in translation quality.
Same domain different discourse style - A case study on Language Resources for data-driven Machine Translation
Authors: Monica Gavrila, Walther v. Hahn, Cristina Vertan
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Information Retrieval, Machine Translation, Translation

Data-driven machine translation (MT) approaches became very popular during last years, especially for language pairs for which it is difficult to find specialists to develop transfer rules. Statistical (SMT) or example-based (EBMT) systems can provide reasonable translation quality for assimilation purposes, as long as a large amount of training data is available. Especially SMT systems rely on parallel aligned corpora which have to be statistical relevant for the given language pair. The construction of large domain specific parallel corpora is time- and cost-consuming; the current practice relies on one or two big such corpora per language pair. Recent developed strategies ensure certain portability to other domains through specialized lexicons or small domain specific corpora. In this paper we discuss the influence of different discourse styles on statistical machine translation systems. We investigate how a pure SMT performs when training and test data belong to same domain but the discourse style varies.
Annotations for Power Relations on Email Threads
Authors: Vinodkumar Prabhakaran, Huzaifa Neralwala, Owen Rambow, Mona Diab
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5

Social relations like power and influence are difficult concepts to define, but are easily recognizable when expressed. In this paper, we describe a multi-layer annotation scheme for social power relations that are recognizable from online written interactions. We introduce a typology of four types of power relations between dialog participants: hierarchical power, situational power, influence and control of communication. We also present a corpus of Enron emails comprising of 122 threaded conversations, manually annotated with instances of these power relations between participants. Our annotations also capture attempts at exercise of power or influence and whether those attempts were successful or not. In addition, we also capture utterance level annotations for overt display of power. We describe the annotation definitions using two example email threads from our corpus illustrating each type of power relation. We also present detailed instructions given to the annotators and provide various statistics on annotations in the corpus.
Foundations of a Multilayer Annotation Framework for Twitter Communications During Crisis Events
Authors: William J. Corvey, Sudha Verma, Sarah Vieweg, Martha Palmer, James H. Martin
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5

In times of mass emergency, vast amounts of data are generated via computer-mediated communication (CMC) that are difficult to manually collect and organize into a coherent picture. Yet valuable information is broadcast, and can provide useful insight into time- and safety-critical situations if captured and analyzed efficiently and effectively. We describe a natural language processing component of the EPIC (Empowering the Public with Information in Crisis) Project infrastructure, designed to extract linguistic and behavioral information from tweet text to aid in the task of information integration. The system incorporates linguistic annotation, in the form of Named Entity Tagging, as well as behavioral annotations to capture tweets contributing to situational awareness and analyze the information type of the tweet content. We show classification results and describe future integration of these classifiers in the larger EPIC infrastructure.
Pedagogical stances and their multimodal signals.
Authors: Isabella Poggi, Francesca D{'}Errico, Giovanna Leone
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Position, Stance Detection

The paper defines the notion of pedagogical stance, viewed as the type of position taken, the role assumed, the image projected and the types of social behaviours performed by a teacher in her teaching interaction with a pupil. Two aspects of pedagogical stance, didactic and affective ― relational, are distinguished and a hypothesis is put forward about their determinant factors (the teacher's personality, idea of one's role and of the learning process, and model of the pupil). Based on a qualitative analysis of the verbal and bodily behaviour of teachers in a corpus of teacher-pupil interactions, the paper singles out two didactic stances (maieutic and efficient) and four affective-relational ones (friendly, dominating, paternalistic, and secure base). Some examples of these stances are analysed in detail and the respective patterns of verbal and behavioural signals that typically characterize the six types of stances are outlined.
An audiovisual political speech analysis incorporating eye-tracking and perception data
Authors: Stefan Scherer, Georg Layher, John Kane, Heiko Neumann, Nick Campbell
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Persuasiveness

We investigate the influence of audiovisual features on the perception of speaking style and performance of politicians, utilizing a large publicly available dataset of German parliament recordings. We conduct a human perception experiment involving eye-tracker data to evaluate human ratings as well as behavior in two separate conditions, i.e. audiovisual and video only. The ratings are evaluated on a five dimensional scale comprising measures of insecurity, monotony, expressiveness, persuasiveness, and overall performance. Further, they are statistically analyzed and put into context in a multimodal feature analysis, involving measures of prosody, voice quality and motion energy. The analysis reveals several statistically significant features, such as pause timing, voice quality measures and motion energy, that highly positively or negatively correlate with certain human ratings of speaking style. Additionally, we compare the gaze behavior of the human subjects to evaluate saliency regions in the multimodal and visual only conditions. The eye-tracking analysis reveals significant changes in the gaze behavior of the human subjects; participants reduce their focus of attention in the audiovisual condition mainly to the region of the face of the politician and scan the upper body, including hands and arms, in the video only condition.
Tajik-Farsi Persian Transliteration Using Statistical Machine Translation
Authors: Chris Irwin Davis
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Machine Translation, Translation, Transliteration

Tajik Persian is a dialect of Persian spoken primarily in Tajikistan and written with a modified Cyrillic alphabet. Iranian Persian, or Farsi, as it is natively called, is the lingua franca of Iran and is written with the Persian alphabet, a modified Arabic script. Although the spoken versions of Tajik and Farsi are mutually intelligible to educated speakers of both languages, the difference between the writing systems constitutes a barrier to text compatibility between the two languages. This paper presents a system to transliterate text between these two different Persian dialects that use incompatible writing systems. The system also serves as a mechanism to facilitate sharing of computational linguistic resources between the two languages. This is relevant because of the disparity in resources for Tajik versus Farsi.
Corpus based Semi-Automatic Extraction of Persian Compound Verbs and their Relations
Authors: Somayeh Bagherbeygi, Mehrnoush Shamsfard
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: POS

Nowadays, Wordnet is used in natural language processing as one of the major linguistic resources. Having such a resource for Persian language helps researchers in computational linguistics and natural language processing fields to develop more accurate systems with higher performances. In this research, we propose a model for semi-automatic construction of Persian wordnet of verbs. Compound verbs are a very productive structure in Persian and number of compound verbs is much greater than simple verbs in this language This research is aimed at finding the structure of Persian compound verbs and the relations between verb components. The main idea behind developing this system is using the wordnet of other POS categories (here means noun and adjective) to extract Persian compound verbs, their synsets and their relations. This paper focuses on three main tasks: 1.extracting compound verbs 2.extracting verbal synsets and 3.extracting the relations among verbal synsets such as hypernymy, antonymy and cause.
Word Sense Inventories by Non-Experts.
Authors: Anna Rumshisky, Nick Botchan, Sophie Kushkuley, James Pustejovsky
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Word Sense Disambiguation

In this paper, we explore different strategies for implementing a crowdsourcing methodology for a single-step construction of an empirically-derived sense inventory and the corresponding sense-annotated corpus. We report on the crowdsourcing experiments using implementation strategies with different HIT costs, worker qualification testing, and other restrictions. We describe multiple adjustments required to ensure successful HIT design, given significant changes within the crowdsourcing community over the last three years.
PAMOCAT: Automatic retrieval of specified postures
Authors: Bernhard Br{\"u}ning, Christian Schnier, Karola Pitsch, Sven Wasmuth
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Data Visualization, Motion Segmentation, Retrieval, Time Series Analysis

In order to understand and model the non-verbal communicative conduct of humans, it seems fruitful to combine qualitative methods (Conversation Analysis) and quantitative techniques (motion capturing). A Tools for data visualization and annotation is important as they constitute a central interface between different research approaches and methodologies. We have developed the pre-annotation tool PAMOCAT that detects motion segments of individual joints. A sophisticated user interface easily allows the annotating person to find correlations between different joints and to export combined qualitative and quantitative annotations to standard annotation tools. Using this technique we are able to examine complex setups with three persons in tight conversion. A functionality to search for special postures of interest and display the frames in an overview makes it easy to analyze difference phenomenas in Conversation Analysis.