THE EFFECTIVENESS OF USING VIDEO IN TEACHING LISTENING OF ORAL NARRATIVE TEXT

The objective of the research is to identify the effectiveness of using video in teaching listening of oral narrative text. Related to the objective of the research, the writer uses experimental method. The research was conducted at SMP N 1 Sawit, Boyolali from 13 January to 28 February 2010, in the academic year 2009/2010. The population in this research is the eighth grade students of SMP N 1 Sawit. The total number of population is 280 students coming from seven classes. The sampling of the research is cluster random sampling. From the population, two classes were taken randomly as the sample. The samples are class VIII G as the experimental group which consists of 40 students, and class VIII F as the control group which consists of 40 students. The writer uses t-test, normality test, and homogeneity test in order to check whether the two groups have the same listening ability, homogeneity and normal distribution or not. After analyzing the pre-test data the writer finds that both groups are homogeneous and in normal distribution. Moreover, based on t-test of pre-test data analysis, the writer finds that t0=0.617 is less than tt(78,0.05)=1.980 or t0< tt. It means H0 is accepted and there is no significant difference in listening ability between the experimental group and the control group. The research design used is Quasi- Experimental Design with Pretest- Posttest and Control Group. While in collecting the data, the writer used a test in the form of multiple choice type. The data are then analyzed by using t-test formula. In this case, data which are analyzed are pre-test and post-test scores of the two groups, the experimental group and the control group. The result of t-test computation shows that t observation (t0) is 4.99 while the value of t table (tt) is 1.98. In other words, t0 is higher than tt (t observation > t table). Therefore, the Alternative Hypothesis (Ha) is accepted while Null Hypothesis (H0) is rejected. It can be concluded that there is a significant difference in listening achievement of oral narrative text between the experimental group and the control group. Besides, the writer finds that the mean of the scores of the experimental group is higher than the control group. The mean of the scores of the experimental group is 6.05, while the mean of the scores of control group is 5.48. The mean difference between them is 0.57. Thus, the result of the research study implies that video is effective to be applied in teaching listening of oral narrative text

From Verbs to Tasks: An Integrated Account of Learning Tasks from Situated Interactive Instruction.

Intelligent collaborative agents are becoming common in the human society. From virtual assistants such as Siri and Google Now to assistive robots, they contribute to human activities in a variety of ways. As they become more pervasive, the challenge of customizing them to a variety of environments and tasks becomes critical. It is infeasible for engineers to program them for each individual use. Our research aims at building interactive robots and agents that adapt to new environments autonomously by interacting with human users using natural modalities. This dissertation studies the problem of learning novel tasks from human-agent dialog. We propose a novel approach for interactive task learning, situated interactive instruction (SII), and investigate approaches to three computational challenges that arise in designing SII agents: situated comprehension, mixed-initiative interaction, and interactive task learning. We propose a novel mixed-modality grounded representation for task verbs which encompasses their lexical, semantic, and task-oriented aspects. This representation is useful in situated comprehension and can be learned through human-agent interactions. We introduce the Indexical Model of comprehension that can exploit extra-linguistic contexts for resolving semantic ambiguities in situated comprehension of task commands. The Indexical model is integrated with a mixed-initiative interaction model that facilitates a flexible task-oriented human-agent dialog. This dialog serves as the basis of interactive task learning. We propose an interactive variation of explanation-based learning that can acquire the proposed representation. We demonstrate that our learning paradigm is efficient, can transfer knowledge between structurally similar tasks, integrates agent-driven exploration with instructional learning, and can acquire several tasks. The methods proposed in this thesis are integrated in Rosie - a generally instructable agent developed in the Soar cognitive architecture and embodied on a table-top robot.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111573/1/shiwali_1.pd

Unsupervised extraction of semantic relations using discourse information

La compréhension du langage naturel repose souvent sur des raisonnements de sens commun, pour lesquels la connaissance de relations sémantiques, en particulier entre prédicats verbaux, peut être nécessaire. Cette thèse porte sur la problématique de l'utilisation d'une méthode distributionnelle pour extraire automatiquement les informations sémantiques nécessaires à ces inférences de sens commun. Des associations typiques entre des paires de prédicats et un ensemble de relations sémantiques (causales, temporelles, de similarité, d'opposition, partie/tout) sont extraites de grands corpus, par l'exploitation de la présence de connecteurs du discours signalant typiquement ces relations. Afin d'apprécier ces associations, nous proposons plusieurs mesures de signifiance inspirées de la littérature ainsi qu'une mesure novatrice conçue spécifiquement pour évaluer la force du lien entre les deux prédicats et la relation. La pertinence de ces mesures est évaluée par le calcul de leur corrélation avec des jugements humains, obtenus par l'annotation d'un échantillon de paires de verbes en contexte discursif. L'application de cette méthodologie sur des corpus de langue française et anglaise permet la construction d'une ressource disponible librement, Lecsie (Linked Events Collection for Semantic Information Extraction). Celle-ci est constituée de triplets: des paires de prédicats associés à une relation; à chaque triplet correspondent des scores de signifiance obtenus par nos mesures.Cette ressource permet de dériver des représentations vectorielles de paires de prédicats qui peuvent être utilisées comme traits lexico-sémantiques pour la construction de modèles pour des applications externes. Nous évaluons le potentiel de ces représentations pour plusieurs applications. Concernant l'analyse du discours, les tâches de la prédiction d'attachement entre unités du discours, ainsi que la prédiction des relations discursives spécifiques les reliant, sont explorées. En utilisant uniquement les traits provenant de notre ressource, nous obtenons des améliorations significatives pour les deux tâches, par rapport à plusieurs bases de référence, notamment des modèles utilisant d'autres types de représentations lexico-sémantiques. Nous proposons également de définir des ensembles optimaux de connecteurs mieux adaptés à des applications sur de grands corpus, en opérant une réduction de dimension dans l'espace des connecteurs, au lieu d'utiliser des groupes de connecteurs composés manuellement et correspondant à des relations prédéfinies. Une autre application prometteuse explorée dans cette thèse concerne les relations entre cadres sémantiques (semantic frames, e.g. FrameNet): la ressource peut être utilisée pour enrichir cette structure par des relations potentielles entre frames verbaux à partir des associations entre leurs verbes. Ces applications diverses démontrent les contributions prometteuses amenées par notre approche permettant l'extraction non supervisée de relations sémantiques.Natural language understanding often relies on common-sense reasoning, for which knowledge about semantic relations, especially between verbal predicates, may be required. This thesis addresses the challenge of using a distibutional method to automatically extract the necessary semantic information for common-sense inference. Typical associations between pairs of predicates and a targeted set of semantic relations (causal, temporal, similarity, opposition, part/whole) are extracted from large corpora, by exploiting the presence of discourse connectives which typically signal these semantic relations. In order to appraise these associations, we provide several significance measures inspired from the literature as well as a novel measure specifically designed to evaluate the strength of the link between the two predicates and the relation. The relevance of these measures is evaluated by computing their correlations with human judgments, based on a sample of verb pairs annotated in context. The application of this methodology to French and English corpora leads to the construction of a freely available resource, Lecsie (Linked Events Collection for Semantic Information Extraction), which consists of triples: pairs of event predicates associated with a relation; each triple is assigned significance scores based on our measures. From this resource, vector-based representations of pairs of predicates can be induced and used as lexical semantic features to build models for external applications. We assess the potential of these representations for several applications. Regarding discourse analysis, the tasks of predicting attachment of discourse units, as well as predicting the specific discourse relation linking them, are investigated. Using only features from our resource, we obtain significant improvements for both tasks in comparison to several baselines, including ones using other representations of the pairs of predicates. We also propose to define optimal sets of connectives better suited for large corpus applications by performing a dimension reduction in the space of the connectives, instead of using manually composed groups of connectives corresponding to predefined relations. Another promising application pursued in this thesis concerns relations between semantic frames (e.g. FrameNet): the resource can be used to enrich this sparse structure by providing candidate relations between verbal frames, based on associations between their verbs. These diverse applications aim to demonstrate the promising contributions provided by our approach, namely allowing the unsupervised extraction of typed semantic relations

Linguistic Representation of Problem Solving Processes in Unaided Object Assembly

This thesis investigates the linguistic representation of problem solving processes in data recorded during unaided object assembly. It combines traditional approaches of analyzing verbal protocols with the recent approach of Cognitive Discourse Analysis

Epistemička modalnost u akademskom diskursu u hrvatskom i engleskom jeziku

The present thesis is the result of a cross-cultural, genre-based study whose main objective is to examine how writers of research articles in psychology in Croatian and English use epistemic modality devices in hedging their claims or in evaluating other scholars’ work. Based on the corpus of 60 research articles published in Croatian and English journals, the study aims to establish the patterns of similarities and differences in the use of the epistemic devices across the main rhetorical sections of a research article as well as to identify their major hedging functions. The overall results show that English writers use epistemic markers more frequently than their Croatian counterparts. This finding is generally in line with the previous cross-cultural studies, showing a more salient use of hedges and their more entrenched status in the Anglo- American writing as compared to academic writing in some other languages investigated. With respect to the individual categories of epistemic devices, the results show both similarities and differences in their uses across the two sub-corpora. In both the English and Croatian sub-corpus, epistemic modal verbs are employed most frequently, followed by epistemic verbs, while epistemic nouns are the least frequent category of epistemic devices. The major difference in the overall results concerns the distributional patterns in the use of epistemic devices. While epistemic modal verbs show a strikingly high frequency of occurrences as compared to other epistemic devices in the English corpus, the results of the frequency analysis of the Croatian corpus show that writers hedge their claims mostly by means of the modal verbs, epistemic verbs, and epistemic adverbs and particles, as attested by their overall similar frequencies. With respect to the distribution of epistemic devices across the research article sections, both English and Croatian writers hedge their claims mostly in the Discussion, followed by the Introduction section, while the use of epistemic devices in the remaining two sections is significantly lower by comparison. Generally, this complies with the major rhetorical functions of the research article sections. Thus, the highest density of hedges in the Discussion reflects its major rhetorical functions primarily concerned with writers’ interpretations and implications of the given research, which often requires a cautious and tentative use of language, shielding writers from the risks of negatibilty of the claims. By contrast, the use of hedges in the middle research article sections is less salient given their focus on the descriptive accounts of the methodological procedures and obtained findings. Drawing on Hyland’s (1998) polypragmatic model of scientific hedges, epistemic devices in both corpora are mostly concerned with the reliability type of hedges, concerned with indicating uncertainties towards the propositional content, signaling at the same time the extent to which the claims may be considered as accurate given the limited state of knowledge they are based on. In addition, epistemic markers may be used as writer-oriented hedges concerned with diminishing the writers’ presence in the text, allowing them to maintain distance from the proposed claims. Finally, the use of epistemic verbs co-occurring with the 1st person plural pronouns is interpreted in the present study as a writer’s strategic choice in foregrounding the epistemic stance. This use of epistemic devices is more frequent in the English as compared to the Croatian corpus, which is in line with some previous crosscultural research, indicating that self-mention is a more prominent feature of the Anglo- American writing as compared to that in other languages. In sum, the present findings provide an insight into the use of the epistemic language in the cross-cultural disciplinary writing and as such may be of particular use to the Croatian speaking disciplinary scholars, students and all those interested in writing research articles in English. On a more general note, it is expected that the study may incite further research on academic writing conventions in Croatian or their comparison with those in English as a lingua franca of science.Cilj je rada istražiti kako autori znanstvenih članaka iz područja psihologije na hrvatskom i engleskom jeziku koriste sredstva epistemičke modalnosti da bi izrazili različiti stupanj sigurnosti prema iznesenim tvrdnjama te iskazali stav prema tvrdnjama drugih autora. Analiza se temelji na korpusu 60 znanstvenih članaka objavljenim u znanstvenim časopisima na hrvatskom i engleskom jeziku. Cilj je analize utvrđivanje sličnosti i razlika u uporabi i učestalosti sredstava epistemičke modalnosti u glavnim retoričkim segmentima znanstvenog članka te istraživanje njihovih pragmatičkih funkcija kao sredstava ograđivanja u znanstvenom tekstu. Rezultati frekvencijske analize pokazuju veću zastupljenost sredstava epistemičke modalnosti u engleskom korpusu u odnosu na hrvatski, što je općenito u skladu s nalazima prethodnih međujezičnih istraživanja koja upućuju na učestaliju uporabu oznaka ograđivanja u akademskom stilu angloameričkog govornog područja u odnosu na akademske stilove pisanja u nekim drugim jezicima. Rezultati pokazuju da su modalni glagoli najčešća gramatička kategorija epistemičkih sredstava u oba korpusa, dok su epistemički glagoli sljedeća kategorija po čestotnosti. U oba korpusa najmanju zastupljenost pokazuje uporaba epistemičkih imenica. Unatoč navedenim sličnostima, rezultati analize pokazuju na istaknutu uporabu modalnih glagola u engleskom korpusu, dok učestalost ostalih sredstava epistemičke modalnosti ne pokazuje drastična odstupanja. Rezultati analize hrvatskog korpusa pokazuju da se najčešća sredstva grupiraju oko modalnih glagola, epistemičkih punoznačnih glagola te modalnih priloga i čestica, dok su ostala sredstva značajno manje zastupljena. Nalazi analize ukazuju da se u oba korpusa oznake ograđivanja najviše koriste u Raspravi, manje u Uvodu, dok je značajno manja učestalost zabilježena u Metodi i Rezultatima. Najveća zastupljenost oznaka ograđivanja u Raspravi ukazuje na autorovu potrebu iskazivanja opreza i odmaka u tumačenju nalaza istraživanja i pokušajima izvođenja zaključaka, što proizlazi iz svijesti o različitim ograničenjima istraživanja koja često ne dozvoljavaju iskazivanje visokog stupnja sigurnosti u iznošenju stavova. Manja zastupljenost oznaka ograđivanja u središnjim segmentima članka odražava njihovu primarnu usmjerenost na opise metodoloških postupaka i rezultata, što u pravilu ne zahtijeva izraženiju uporabu oznaka ograđivanja. U odnosu na Hylandov (1998) polipragmatički model ograđivanja u znanstvenom tekstu, rezultati pokazuju da se sredstva epistemičke modalnosti najčešće koriste za iskazivanje nižeg stupnja sigurnosti u odnosu na sadržaj tvrdnje, upućujući pritom da se iste mogu smatrati pouzdanim u okvirima postojećeg, često ograničenog, znanja na temelju kojeg se izvode. Osim na propozicijski sadržaj, pragmatičke funkcije epistemičkih sredstava mogu biti usmjerene i na autora, pri čemu se umanjuje njegova prisutnost u tekstu te omogućuje zadržavanje većeg odmaka od iznesenih tvrdnji. Naposlijetku, uporaba prvog lica i punoznačnih epistemičkih glagola u ovom se radu smatra autorovim izborom s ciljem isticanja osobnog epistemičkog stava. Rezultati pokazuju da je navedena uporaba epistemičkih sredstava učestalija u engleskom korpusu, što je općenito u skladu s nekim prethodnim međujezičnim istraživanjima koja ukazuju da je prisutnost autora istaknutija konvencija angloameričkog akademskog stila pisanja u odnosu na iste u nekim drugim istraživanim jezicima. Zaključno, pretpostavlja se da bi uočene specifičnosti u uporabi sredstava epistemičke modalnosti u psihologijskim člancima u engleskom i hrvatskom jeziku mogle koristiti predmetnim stručnjacima, studentima i svima onima koji počinju pisati ili već imaju iskustvo pisanja znanstvenih članaka kako na hrvatskom, tako i na engleskom jeziku. Očekuje se da bi postojeće istraživanje moglo potaknuti daljnja istraživanja konvencija akademskog pisanja, kako hrvatskog jezika, tako i njihove usporedbe s engleskim jezikom kao globalnim jezikom znanosti

Argumentation Mining in User-Generated Web Discourse

The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17

Extracting Temporal and Causal Relations between Events

Structured information resulting from temporal information processing is crucial for a variety of natural language processing tasks, for instance to generate timeline summarization of events from news documents, or to answer temporal/causal-related questions about some events. In this thesis we present a framework for an integrated temporal and causal relation extraction system. We first develop a robust extraction component for each type of relations, i.e. temporal order and causality. We then combine the two extraction components into an integrated relation extraction system, CATENA---CAusal and Temporal relation Extraction from NAtural language texts---, by utilizing the presumption about event precedence in causality, that causing events must happened BEFORE resulting events. Several resources and techniques to improve our relation extraction systems are also discussed, including word embeddings and training data expansion. Finally, we report our adaptation efforts of temporal information processing for languages other than English, namely Italian and Indonesian.Comment: PhD Thesi