Diagnosing Reading strategies: Paraphrase Recognition

Paraphrase recognition is a form of natural language processing used in tutoring, question answering, and information retrieval systems. The context of the present work is an automated reading strategy trainer called iSTART (Interactive Strategy Trainer for Active Reading and Thinking). The ability to recognize the use of paraphrase—a complete, partial, or inaccurate paraphrase; with or without extra information—in the student\u27s input is essential if the trainer is to give appropriate feedback. I analyzed the most common patterns of paraphrase and developed a means of representing the semantic structure of sentences. Paraphrases are recognized by transforming sentences into this representation and comparing them. To construct a precise semantic representation, it is important to understand the meaning of prepositions. Adding preposition disambiguation to the original system improved its accuracy by 20%. The preposition sense disambiguation module itself achieves about 80% accuracy for the top 10 most frequently used prepositions. The main contributions of this work to the research community are the preposition classification and generalized preposition disambiguation processes, which are integrated into the paraphrase recognition system and are shown to be quite effective. The recognition model also forms a significant part of this contribution. The present effort includes the modeling of the paraphrase recognition process, featuring the Syntactic-Semantic Graph as a sentence representation, the implementation of a significant portion of this design demonstrating its effectiveness, the modeling of an effective preposition classification based on prepositional usage, the design of the generalized preposition disambiguation module, and the integration of the preposition disambiguation module into the paraphrase recognition system so as to gain significant improvement

Learning Components of Computational Models from Texts

The mental models of experts can be encoded in computational cognitive models that can support the functioning of intelligent agents. This paper compares human mental models to computational cognitive models, and explores the extent to which the latter can be acquired automatically from published sources via automatic learning by reading. It suggests that although model components can be automatically learned, published sources lack sufficient information for the compilation of fully specified models that can support sophisticated agent capabilities, such as physiological simulation and reasoning. Such models require hypotheses and educated guessing about unattested phenomena, which can be provided only by humans and are best recorded using knowledge engineering strategies. This work merges past work on cognitive modeling, agent simulation, learning by reading, and narrative structure, and draws examples from the domain of clinical medicine

Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning

Contains fulltext : 228326pre.pdf (preprint version ) (Open Access) Contains fulltext : 228326pub.pdf (publisher's version ) (Open Access)BNAIC/BeneLearn 202

The role of trust in proactive conversational assistants

Humans and machines harmoniously collaborating and bene ting from each other is a long lasting dream for researchers in robotics and arti cial intelligence. An important feature of ef cient and rewarding cooperation is the ability to assume possible problematic situations and act in advance to prevent negative outcomes. This concept of assistance is known under the term proactivity. In this article, we investigate the development and implementation of proactive dialogues for fostering a trustworthy human-computer relationship and providing adequate and timely assistance. Here, we make several contributions. A formalisation of proactive dialogue in conversational assistants is provided. The formalisation forms a framework for integrating proactive dialogue in conversational applications. Additionally, we present a study showing the relations between proactive dialogue actions and several aspects of the perceived trustworthiness of a system as well as effects on the user experience. The results of the experiments provide signi cant contributions to the line of proactive dialogue research. Particularly, we provide insights on the effects of proactive dialogue on the human-computer trust relationship and dependencies between proactive dialogue and user specific and situational characteristics

Selecting and Generating Computational Meaning Representations for Short Texts

Language conveys meaning, so natural language processing (NLP) requires representations of meaning. This work addresses two broad questions: (1) What meaning representation should we use? and (2) How can we transform text to our chosen meaning representation? In the first part, we explore different meaning representations (MRs) of short texts, ranging from surface forms to deep-learning-based models. We show the advantages and disadvantages of a variety of MRs for summarization, paraphrase detection, and clustering. In the second part, we use SQL as a running example for an in-depth look at how we can parse text into our chosen MR. We examine the text-to-SQL problem from three perspectives—methodology, systems, and applications—and show how each contributes to a fuller understanding of the task.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143967/1/cfdollak_1.pd

Exploring Text Mining and Analytics for Applications in Public Security: An in-depth dive into a systematic literature review

Text mining and related analytics emerge as a technological approach to support human activities in extracting useful knowledge through texts in several formats. From a managerial point of view, it can help organizations in planning and decision-making processes, providing information that was not previously evident through textual materials produced internally or even externally. In this context, within the public/governmental scope, public security agencies are great beneficiaries of the tools associated with text mining, in several aspects, from applications in the criminal area to the collection of people's opinions and sentiments about the actions taken to promote their welfare. This article reports details of a systematic literature review focused on identifying the main areas of text mining application in public security, the most recurrent technological tools, and future research directions. The searches covered four major article bases (Scopus, Web of Science, IEEE Xplore, and ACM Digital Library), selecting 194 materials published between 2014 and the first half of 2021, among journals, conferences, and book chapters. There were several findings concerning the targets of the literature review, as presented in the results of this article

Knowledge base integration in biomedical natural language processing applications

With the progress of natural language processing in the biomedical field, the lack of annotated data due to regulations and expensive labor remains an issue. In this work, we study the potential of knowledge bases for biomedical language processing to compensate for the shortage of annotated data. Accordingly, we experiment with the integration of a rigorous biomedical knowledge base, the Unified Medical Language System, in three different biomedical natural language processing applications: text simplification, conversational agents for medication adherence, and automatic evaluation of medical students' chart notes. In the first task, we take as a use case simplifying medication instructions to enhance medication adherence among patients. Given the lack of an appropriate parallel corpus, the Unified Medical Language System provided simpler synonyms for an unsupervised system we devise, and we show a positive impact on comprehension through a human subjects study. As for the second task, we devise an unsupervised system to automatically evaluate chart notes written by medical students. The purpose of the system is to speed up the feedback process and enhance the educational experience. With the lack of training corpora, utilizing the Unified Medical Language System proved to enhance the accuracy of evaluation after integration into the baseline system. For the final task, the Unified Medical Language System was used to augment the training data of a conversational agent that educates patients on their medications. As part of the educational procedure, the agent needed to assess the comprehension of the patients by evaluating their answers to predefined questions. Starting with a small seed set of paraphrases of acceptable answers, the Unified Medical Language System was used to artificially augment the original small seed set via synonymy. Results did not show an increase in quality of system output after knowledge base integration due to the majority of errors resulting from mishandling of counts and negations. We later demonstrate the importance of a (lacking) entity linking system to perform optimal integration of biomedical knowledge bases, and we offer a first stride towards solving that problem, along with conclusions on proper training setup and processes for automatic collection of an annotated dataset for biomedical word sense disambiguation

Evaluating the impact of variation in automatically generated embodied object descriptions

Institute for Communicating and Collaborative SystemsThe primary task for any system that aims to automatically generate human-readable output is choice: the input to the system is usually well-specified, but there can be a wide range of options for creating a presentation based on that input. When designing such a system, an important decision is to select which aspects of the output are hard-wired and which allow for dynamic variation. Supporting dynamic choice requires additional representation and processing effort in the system, so it is important to ensure that incorporating variation has a positive effect on the generated output. In this thesis, we concentrate on two types of output generated by a multimodal dialogue system: linguistic descriptions of objects drawn from a database, and conversational facial displays of an embodied talking head. In a series of experiments, we add different types of variation to one of these types of output. The impact of each implementation is then assessed through a user evaluation in which human judges compare outputs generated by the basic version of the system to those generated by the modified version; in some cases, we also use automated metrics to compare the versions of the generated output. This series of implementations and evaluations allows us to address three related issues. First, we explore the circumstances under which users perceive and appreciate variation in generated output. Second, we compare two methods of including variation into the output of a corpus-based generation system. Third, we compare human judgements of output quality to the predictions of a range of automated metrics. The results of the thesis are as follows. The judges generally preferred output that incorporated variation, except for a small number of cases where other aspects of the output obscured it or the variation was not marked. In general, the output of systems that chose the majority option was judged worse than that of systems that chose from a wider range of outputs. However, the results for non-verbal displays were mixed: users mildly preferred agent outputs where the facial displays were generated using stochastic techniques to those where a simple rule was used, but the stochastic facial displays decreased users’ ability to identify contextual tailoring in speech while the rule-based displays did not. Finally, automated metrics based on simple corpus similarity favour generation strategies that do not diverge far from the average corpus examples, which are exactly the strategies that human judges tend to dislike. Automated metrics that measure other properties of the generated output correspond more closely to users’ preferences

Proceedings of QG2010: The Third Workshop on Question Generation

These are the peer-reviewed proceedings of "QG2010, The Third Workshop on Question Generation". The workshop included a special track for "QGSTEC2010: The First Question Generation Shared Task and Evaluation Challenge". QG2010 was held as part of The Tenth International Conference on Intelligent Tutoring Systems (ITS2010)