Incremental probabilistic Latent Semantic Analysis for video retrieval

Recent research trends in Content-based Video Retrieval have shown topic models as an effective tool to deal with the semantic gap challenge. In this scenario, this paper has a dual target: (1) it is aimed at studying how the use of different topic models (pLSA, LDA and FSTM) affects video retrieval performance; (2) a novel incremental topic model (IpLSA) is presented in order to cope with incremental scenarios in an effective and efficient way. A comprehensive comparison among these four topic models using two different retrieval systems and two reference benchmarking video databases is provided. Experiments revealed that pLSA is the best model in sparse conditions, LDA tend to outperform the rest of the models in a dense space and IpLSA is able to work properly in both cases

Semantics-based automated essay evaluation

Automated essay evaluation (AEE) is a widely used practical solution for replacing time-consuming manual grading of student essays. Automated systems are used in combination with human graders in different high-stake assessments, as well as in classrooms. During the last 50 years, since the beginning of the development of the field, many challenges have arisen in the field, including seeking ways to evaluate the semantic content, providing automated feedback, determining reliability of grades, making the field more "exposed", and others. In this dissertation we address several of these challenges and propose novel solutions for semantic based essay evaluation. Most of the AEE research has been conducted by commercial organizations that protect their investments by releasing proprietary systems where details are not publicly available. We provide comparison (as detailed as possible) of 20 state-of-the-art approaches for automated essay evaluation and we propose a new automated essay evaluation system named SAGE (Semantic Automated Grader for Essays) with all the technological details revealed to the scientific community. Lack of consideration of text semantics is one of the main weaknesses of the existing state-of-the-art systems. We address the evaluation of essay semantics from perspectives of essay coherence and semantic error detection. Coherence describes the flow of information in an essay and allows us to evaluate the connections between the discourse. We propose two groups of coherence attributes: coherence attributes obtained in a highly dimensional semantic space and coherence attributes obtained from a sentence-similarity networks. Furthermore, we propose the Automated Error Detection (AED) system and evaluate the essay semantics from the perspective of essay consistency. The system detects semantic errors using information extraction and logic reasoning and is able to provide semantic feedback for the writer. The proposed system SAGE achieves significantly higher grading accuracy compared with other state-of-the-art automated essay evaluation systems. In the last part of the dissertation we address the question of reliability of grades. Despite the unified grading rules, human graders introduce bias into scores. Consequently, a grading model has to implement a grading logic that may be a mixture of grading logics from various graders. We propose an approach for separating a set of essays into subsets that represent different graders, which uses an explanation methodology and clustering. The results show that learning from the ensemble of separated models significantly improves the average prediction accuracy on artificial and real-world datasets

Semantics-based automated essay evaluation

Automated essay evaluation (AEE) is a widely used practical solution for replacing time-consuming manual grading of student essays. Automated systems are used in combination with human graders in different high-stake assessments, as well as in classrooms. During the last 50 years, since the beginning of the development of the field, many challenges have arisen in the field, including seeking ways to evaluate the semantic content, providing automated feedback, determining reliability of grades, making the field more "exposed", and others. In this dissertation we address several of these challenges and propose novel solutions for semantic based essay evaluation. Most of the AEE research has been conducted by commercial organizations that protect their investments by releasing proprietary systems where details are not publicly available. We provide comparison (as detailed as possible) of 20 state-of-the-art approaches for automated essay evaluation and we propose a new automated essay evaluation system named SAGE (Semantic Automated Grader for Essays) with all the technological details revealed to the scientific community. Lack of consideration of text semantics is one of the main weaknesses of the existing state-of-the-art systems. We address the evaluation of essay semantics from perspectives of essay coherence and semantic error detection. Coherence describes the flow of information in an essay and allows us to evaluate the connections between the discourse. We propose two groups of coherence attributes: coherence attributes obtained in a highly dimensional semantic space and coherence attributes obtained from a sentence-similarity networks. Furthermore, we propose the Automated Error Detection (AED) system and evaluate the essay semantics from the perspective of essay consistency. The system detects semantic errors using information extraction and logic reasoning and is able to provide semantic feedback for the writer. The proposed system SAGE achieves significantly higher grading accuracy compared with other state-of-the-art automated essay evaluation systems. In the last part of the dissertation we address the question of reliability of grades. Despite the unified grading rules, human graders introduce bias into scores. Consequently, a grading model has to implement a grading logic that may be a mixture of grading logics from various graders. We propose an approach for separating a set of essays into subsets that represent different graders, which uses an explanation methodology and clustering. The results show that learning from the ensemble of separated models significantly improves the average prediction accuracy on artificial and real-world datasets

Characterisation and adaptive learning in interactive video retrieval

El objetivo principal de esta tesis consiste en utilizar eficazmente los modelos de tópicos latentes para afrontar el problema de la recuperación automática de vídeo. Concretamente, se pretende mejorar tanto a nivel de eficiencia como a nivel de precisión el actual estado del arte en materia de los sitemas de recuperación automática de vídeo. En general, los modelos de tópicos latentes son un conjunto de herramientas estadísticas que permiten extraer los patrones generadores de una colección de datos. Tradicionalmente, este tipo de técnicas no han sido consideradas de gran utilidad para los sistemas de recuperación automática de vídeo debido a su alto coste computacional y a la propia complejidad del espacio de tópicos en el ámbito de la información visual.In this work, we are interested in the use of latent topics to overcome the current limitations in CBVR. Despite the potential of topic models to uncover the hidden structure of a collection, they have traditionally been unable to provide a competitive advantage in CBVR because of the high computational cost of their algorithms and the complexity of the latent space in the visual domain. Throughout this thesis we focus on designing new models and tools based on topic models to take advantage of the latent space in CBVR. Specifically, we have worked in four different areas within the retrieval process: vocabulary reduction, encoding, modelling and ranking, being our most important contributions related to both modelling and ranking

Proceedings of the Workshop Semantic Content Acquisition and Representation (SCAR) 2007

This is the proceedings of the Workshop on Semantic Content Acquisition and Representation, held in conjunction with NODALIDA 2007, on May 24 2007 in Tartu, Estonia.</p

SVMAUD: Using textual information to predict the audience level of written works using support vector machines

Information retrieval systems should seek to match resources with the reading ability of the individual user; similarly, an author must choose vocabulary and sentence structures appropriate for his or her audience. Traditional readability formulas, including the popular Flesch-Kincaid Reading Age and the Dale-Chall Reading Ease Score, rely on numerical representations of text characteristics, including syllable counts and sentence lengths, to suggest audience level of resources. However, the author’s chosen vocabulary, sentence structure, and even the page formatting can alter the predicted audience level by several levels, especially in the case of digital library resources. For these reasons, the performance of readability formulas when predicting the audience level of digital library resources is very low. Rather than relying on these inputs, machine learning methods, including cosine, Naïve Bayes, and Support Vector Machines (SVM), can suggest the grade level of an essay based on the vocabulary chosen by the author. The audience level prediction and essay grading problems share the same inputs, expert-labeled documents, and outputs, a numerical score representing quality or audience level. After a human expert labels a representative sample of resources with audience level, the proposed SVM-based audience level prediction program, SVMAUD, constructs a vocabulary for each audience level; then, the text in an unlabeled resource is compared with this predefined vocabulary to suggest the most appropriate audience level. Two readability formulas and four machine learning programs are evaluated with respect to predicting human-expert entered audience levels based on the text contained in an unlabeled resource. In a collection containing 10,238 expert-labeled HTML-based digital library resources, the Flesch-Kincaid Reading Age and the Dale-Chall Reading Ease Score predict the specific audience level with F-measures of 0.10 and 0.05, respectively. Conversely, cosine, Naïve Bayes, the Collins-Thompson and Callan model, and SVMAUD improve these F-measures to 0.57, 0.61, 0.68, and 0.78, respectively. When a term’s weight is adjusted based on the HTML tag in which it occurs, the specific audience level prediction performance of cosine, Naïve Bayes, the Collins-Thompson and Callan method, and SVMAUD improves to 0.68, 0.70, 0.75, and 0.84, respectively. When title, keyword, and abstract metadata is used for training, cosine, Naïve Bayes, the Collins-Thompson and Callan model, and SVMAUD specific audience level prediction F-measures are found to be 0.61, 0.68, 0.75, and 0.86, respectively. When cosine, Naïve Bayes, the Collins-Thompson and Callan method, and SVMAUD are trained and tested using resources from a single subject category, the specific audience level prediction F- measure performance improves to 0.63, 0.70, 0.77, and 0.87, respectively. SVMAUD experiences the highest audience level prediction performance among all methods under evaluation in this study. After SVMAUD is properly trained, it can be used to predict the audience level of any written work

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Indonesian and Malay are underrepresented in the development of natural language processing (NLP) technologies and available resources are difficult to find. A clear picture of existing work can invigorate and inform how researchers conceptualise worthwhile projects. Using an education sector project to motivate the study, we conducted a wide-ranging overview of Indonesian and Malay human language technologies and corpus work. We charted 657 included studies according to Hirschberg and Manning's 2015 description of NLP, concluding that the field was dominated by exploratory corpus work, machine reading of text gathered from the Internet, and sentiment analysis. In this paper, we identify most published authors and research hubs, and make a number of recommendations to encourage future collaboration and efficiency within NLP in Indonesian and Malay

Short-text semantic similarity (STSS): Techniques, challenges and future perspectives

In natural language processing, short-text semantic similarity (STSS) is a very prominent field. It has a significant impact on a broad range of applications, such as question-answering systems, information retrieval, entity recognition, text analytics, sentiment classification, and so on. Despite their widespread use, many traditional machine learning techniques are incapable of identifying the semantics of short text. Traditional methods are based on ontologies, knowledge graphs, and corpus-based methods. The performance of these methods is influenced by the manually defined rules. Applying such measures is still difficult, since it poses various semantic challenges. In the existing literature, the most recent advances in short-text semantic similarity (STSS) research are not included. This study presents the systematic literature review (SLR) with the aim to (i) explain short sentence barriers in semantic similarity, (ii) identify the most appropriate standard deep learning techniques for the semantics of a short text, (iii) classify the language models that produce high-level contextual semantic information, (iv) determine appropriate datasets that are only intended for short text, and (v) highlight research challenges and proposed future improvements. To the best of our knowledge, we have provided an in-depth, comprehensive, and systematic review of short text semantic similarity trends, which will assist the researchers to reuse and enhance the semantic information.Yayasan UTP Pre-commercialization grant (YUTP-PRG) [015PBC-005]; Computer and Information Science Department of Universiti Teknologi PETRONASYayasan UTP, YUTP: 015PBC-00

Analyzing short-answer questions and their automatic scoring - studies on semantic relations in reading comprehension and the reduction of human annotation effort

Short-answer questions are a wide-spread exercise type in many educational areas. Answers given by learners to such questions are scored by teachers based on their content alone ignoring their linguistic correctness as far as possible. They typically have a length of up to a few sentences. Manual scoring is a time-consuming task, so that automatic scoring of short-answer questions using natural language processing techniques has become an important task. This thesis focuses on two aspects of short-answer questions and their scoring: First, we concentrate on a reading comprehension scenario for learners of German as a foreign language, where students answer questions about a reading text. Within this scenario, we examine the multiple relations between reading texts, learner answers and teacher-specified target answers. Second, we investigate how to reduce human scoring workload by both fully automatic and computer-assisted scoring. The latter is a scenario where scoring is not done entirely automatically, but where a teacher receives scoring support, for example, by means of clustering similar answers together. Addressing the first aspect, we conduct a series of corpus annotation studies which highlight the relations between pairs of learner answers and target answers, as well as between both types of answers and the reading text they refer to. We annotate sentences from the reading text that were potentially used by learners or teachers for constructing answers and observe that, unsurprisingly, most correct answers can easily be linked to the text; incorrect answers often link to the text as well, but are often backed up by a part of the text not relevant to answer the question. Based on these findings, we create a new baseline scoring model which considers for correctness whether learners looked for an answer in the right place or not. After identifying those links into the text, we label the relation between learner answers and target answers as well as between reading texts and answers by annotating entailment relations. In contrast to the widespread assumption that scoring can be fully mapped to the task of recognizing textual entailment, we find the two tasks to be only closely related and not completely equivalent. Correct answers do often, but not always, entail the target answer, as well as part of the related text, and incorrect answers do most of the time not stand in an entailment relation to the target answer, but often have some overlap with the text. This close relatedness allows us to use gold-standard entailment information to improve the performance of automatic scoring. We also use links between learner answers and both reading texts and target answers in a statistical alignment-based scoring approach using methods from machine translation and reach a performance comparable to an existing knowledge-based alignment approach. Our investigations into how human scoring effort can be reduced when learner answers are manually scored by teachers are based on two methods: active learning and clustering. In the active learning approach, we score particularly informative items first, i.e., items from which a classifier can learn most, identifying them using uncertainty-based sample selection. In this way, we reach a higher performance with a given number of annotation steps compared to randomly selected answers. In the second research strand, we use clustering methods to group similar answers together, such that groups of answers can be scored in one scoring step. In doing so, the number of necessary labeling steps can be substantially reduced. When comparing clustering-based scoring to classical supervised machine learning setups, where the human annotations are used to train a classifier, supervised machine learning is still in the lead in terms of performance, whereas clusters provide the advantage of structured output. However, we are able to close part of the performance gap by means of supervised feature selection and semi-supervised clustering. In an additional study, we investigate the automatic processing of learner language with respect to the performance of part-of-speech (POS) tagging tools. We manually annotate a German reading comprehension corpus both with spelling normalization and POS information and find that the performance of automatic POS tagging can be improved by spell-checking the data using the reading text as additional evidence for lexical material intended in a learner answer.Short-Answer-Fragen sind ein weit verbreiteter Aufgabentyp in vielen Bildungsbereichen. Die Antworten, die Lerner zu solchen Aufgaben geben, werden von Lehrenden allein auf Grundlage ihres Inhalts bewertet; linguistische Korrektheit wird soweit möglich ignoriert. Diese Doktorarbeit legt ihren Schwerpunkt auf zwei Aspekte im Zusammenhang mit Short- Answer-Fragen und ihrer Bewertung: Zum einen betrachten wir ein Leseverständnisszenario, bei dem Studenten Fragen zu Lesetexten beantworten. Dabei untersuchen wir insbesondere die verschiedenen Beziehungen, die es zwischen Lesetexten, Lernerantworten und vom Lehrer erstellten Musterantworten gibt. Zum anderen untersuchen wir, wie der menschliche Bewertungsaufwand durch voll-automatisches und computergestütztes Bewerten reduziert werden kann. Bei letzterem handelt es sich um ein Szenario, in dem Lehrer bei der Bewertung unterstützt werden, z.B. indem ähnliche Antworten automatisch gruppiert werden. Zur Untersuchung des ersten Aspekts unternehmen wir eine Reihe von Korpusannotationsstudien, die sowohl die Beziehungen zwischen Lerner- und Musterantworten beleuchten, als auch die Beziehung zwischen diesen Antworten und dem Lesetext, auf den sie sich beziehen. Wir annotieren Sätze aus dem Lesetext, die vermutlich bei der Formulierung einer Antwort benutzt wurden und machen die zu erwartende Beobachtung, dass die meisten korrekten Antworten problemlos mit bestimmten Textpassagen in Verbindung gebracht werden können. Inkorrekte Antworten haben ebenfalls oft eine Verbindung zu bestimmten Textpassagen, die aber oft für die jeweilige Frage nicht relevant sind. Auf Grundlage dieser Erkenntnisse entwerfen wir ein neues Baseline-Bewertungsmodell, das für die Korrektheit einer Antwort nur in Betracht zieht, ob der Lerner die Antwort an der richtigen Stelle im Lesetext gesucht hat oder nicht. Nachdem wir diese Verbindungen in den Text identifiziert haben, annotieren wir die Relation zwischen Lerner- und Musterantworten und zwischen Texten und Antworten mit Entailment- Relationen. Im Gegensatz zur der weitverbreiteten Annahme, dass das Bewerten von Short- Answer-Fragen und das Erkennen von Textual-Entailment-Relationen zwischen Lerner und Musterantworten sich direkt entsprechen, finden wir heraus, dass die beiden Aufgaben nur nahe verwandt aber nicht vollständig äquivalent sind. Korrekte Antworten entailen meistens, aber nicht immer, die Musterantwort und auch den entsprechenden Satz im Lesetext. Inkorrekte Antworten stehen meist in keiner Entailmentrelation mit der Musterantwort, haben aber oft zumindest teilweisen Overlap mit dem Text. Diese nahe Verwandtschaft erlaubt es uns, Goldstandard-Entailmentinformation zu benutzen, um die Performanz beim automatischen Bewerten zu verbessern. Wir benutzen die annotierten Verbindungen zwischen Lesetexten und Antworten auch in einem Scoringansatz, der auf statistischem Alignment basiert und Methoden aus dem Bereich der maschinellen Übersetzung nutzt. Dabei erreichen wir eine Scoringgenauigkeit, die mit Ansätzen, die ein existierendes wissensbasiertes Alignment nutzen, vergleichbar ist. Unsere Untersuchungen, wie der Bewertungsaufwand beim Menschen verringert werden kann, wenn Antworten vom Lehrer manuell bewertet werden, basieren auf zwei Methoden: Active Learning und Clustering. Beim Active-Learning-Ansatz werden besonders informative Antworten vorrangig zur Bewertung ausgewählt, d.h. solche Antworten, von denen ein Klassifikator besonders viel lernen kann. Wir identifizieren solche Antworten durch Uncertainty-Sampling- Methoden und erreichen dadurch mit einer gegebenen Anzahl von Annotationsschritten eine höhere Klassifikationsgenauigkeit als mit zufällig ausgewählten Antworten. In unserem zweiten Forschungszweig nutzen wir Clusteringmethoden um ähnliche Antworten zu gruppieren, so dass Gruppen von Antworten in einem Annotationsschritt bewertet werden können. Dadurch kann die Anzahl der insgesamt nötigen Bewertungsschritte drastisch reduziert werden. Beim Vergleich zwischen clusteringbasierten Bewertungsverfahren und klassischem überwachten maschinellen Lernen, bei dem menschliche Annotationen dazu genutzt werden, einen Klassifikator zu trainieren, erbringen überwachte maschinelle Lernverfahren immer noch eine höhere Bewertungsgenauigkeit. Demgegenüber bringen Cluster den Vorteil eines strukturierten Outputs mit sich. Wir sind jedoch in der Lage, einen Teil diese Genauigkeitslücke zu schließen, in dem wir überwachte Featureauswahl und halbüberwachtes Clustering anwenden. In einer zusätzlichen Studie untersuchen wir die automatische Verarbeitung von Lernersprache im Hinblick auf die Performanz vonWerkzeugen für dasWortarten-Tagging. Wir annotieren ein deutsches Leseverstehenskorpus manuell sowohl mit Normalisierungsinformation in Bezug auf Rechtschreibung als auch mit Wortartinformation. Als Ergebnis der Studie finden wir, dass die Performanz bei der automatischen Wortartenzuweisung durch Rechtschreibkorrektur verbessert werden kann, insbesondere wenn wir den Lesetext als zusätzliche Evidenz dafür verwenden, welche Wörter der Leser in einer Antwort vermutlich benutzen wollte