Measuring Semantic Textual Similarity and Automatic Answer Assessment in Dialogue Based Tutoring Systems

This dissertation presents methods and resources proposed to improve onmeasuring semantic textual similarity and their applications in student responseunderstanding in dialogue based Intelligent Tutoring Systems. In order to predict the extent of similarity between given pair of sentences,we have proposed machine learning models using dozens of features, such as thescores calculated using optimal multi-level alignment, vector based compositionalsemantics, and machine translation evaluation methods. Furthermore, we haveproposed models towards adding an interpretation layer on top of similaritymeasurement systems. Our models on predicting and interpreting the semanticsimilarity have been the top performing systems in SemEval (a premier venue for thesemantic evaluation) for the last three years. The correlations between our models\u27predictions and the human judgments were above 0.80 for several datasets while ourmodels being very robust than many other top performing systems. Moreover, wehave proposed Bayesian. We have also proposed a novel Neural Network based word representationmapping approach which allows us to map the vector based representation of a wordfound in one model to the another model where the word representation is missing,effectively pooling together the vocabularies and corresponding representationsacross models. Our experiments show that the model coverage increased by few toseveral times depending on which model\u27s vocabulary is taken as a reference. Also,the transformed representations were well correlated to the native target modelvectors showing that the mapped representations can be used with condence tosubstitute the missing word representations in the target model. models to adapt similarity models across domains. Furthermore, we have proposed methods to improve open-ended answersassessment in dialogue based tutoring systems which is very challenging because ofthe variations in student answers which often are not self contained and need thecontextual information (e.g., dialogue history) in order to better assess theircorrectness. In that, we have proposed Probabilistic Soft Logic (PSL) modelsaugmenting semantic similarity information with other knowledge. To detect intra- and inter-sentential negation scope and focus in tutorialdialogs, we have developed Conditional Random Fields (CRF) models. The resultsindicate that our approach is very effective in detecting negation scope and focus intutorial dialogue context and can be further developed to augment the naturallanguage understanding systems. Additionally, we created resources (datasets, models, and tools) for fosteringresearch in semantic similarity and student response understanding inconversational tutoring systems

Distributional Semantic Models for Clinical Text Applied to Health Record Summarization

As information systems in the health sector are becoming increasingly computerized, large amounts of care-related information are being stored electronically. In hospitals clinicians continuously document treatment and care given to patients in electronic health record (EHR) systems. Much of the information being documented is in the form of clinical notes, or narratives, containing primarily unstructured free-text information. For each care episode, clinical notes are written on a regular basis, ending with a discharge summary that basically summarizes the care episode. Although EHR systems are helpful for storing and managing such information, there is an unrealized potential in utilizing this information for smarter care assistance, as well as for secondary purposes such as research and education. Advances in clinical language processing are enabling computers to assist clinicians in their interaction with the free-text information documented in EHR systems. This includes assisting in tasks like query-based search, terminology development, knowledge extraction, translation, and summarization. This thesis explores various computerized approaches and methods aimed at enabling automated semantic textual similarity assessment and information extraction based on the free-text information in EHR systems. The focus is placed on the task of (semi-)automated summarization of the clinical notes written during individual care episodes. The overall theme of the presented work is to utilize resource-light approaches and methods, circumventing the need to manually develop knowledge resources or training data. Thus, to enable computational semantic textual similarity assessment, word distribution statistics are derived from large training corpora of clinical free text and stored as vector-based representations referred to as distributional semantic models. Also resource-light methods are explored in the task of performing automatic summarization of clinical freetext information, relying on semantic textual similarity assessment. Novel and experimental methods are presented and evaluated that focus on: a) distributional semantic models trained in an unsupervised manner from statistical information derived from large unannotated clinical free-text corpora; b) representing and computing semantic similarities between linguistic items of different granularity, primarily words, sentences and clinical notes; and c) summarizing clinical free-text information from individual care episodes. Results are evaluated against gold standards that reflect human judgements. The results indicate that the use of distributional semantics is promising as a resource-light approach to automated capturing of semantic textual similarity relations from unannotated clinical text corpora. Here it is important that the semantics correlate with the clinical terminology, and with various semantic similarity assessment tasks. Improvements over classical approaches are achieved when the underlying vector-based representations allow for a broader range of semantic features to be captured and represented. These are either distributed over multiple semantic models trained with different features and training corpora, or use models that store multiple sense-vectors per word. Further, the use of structured meta-level information accompanying care episodes is explored as training features for distributional semantic models, with the aim of capturing semantic relations suitable for care episode-level information retrieval. Results indicate that such models performs well in clinical information retrieval. It is shown that a method called Random Indexing can be modified to construct distributional semantic models that capture multiple sense-vectors for each word in the training corpus. This is done in a way that retains the original training properties of the Random Indexing method, by being incremental, scalable and distributional. Distributional semantic models trained with a framework called Word2vec, which relies on the use of neural networks, outperform those trained using the classic Random Indexing method in several semantic similarity assessment tasks, when training is done using comparable parameters and the same training corpora. Finally, several statistical features in clinical text are explored in terms of their ability to indicate sentence significance in a text summary generated from the clinical notes. This includes the use of distributional semantics to enable case-based similarity assessment, where cases are other care episodes and their “solutions”, i.e., discharge summaries. A type of manual evaluation is performed, where human experts rates the different aspects of the summaries using a evaluation scheme/tool. In addition, the original clinician-written discharge summaries are explored as gold standard for the purpose of automated evaluation. Evaluation shows a high correlation between manual and automated evaluation, suggesting that such a gold standard can function as a proxy for human evaluations. --- This thesis has been published jointly with Norwegian University of Science and Technology, Norway and University of Turku, Finland.This thesis has beenpublished jointly with Norwegian University of Science and Technology, Norway.Siirretty Doriast

Econometrics meets sentiment : an overview of methodology and applications

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

Genre and Domain Dependencies in Sentiment Analysis

Genre and domain influence an author\''s style of writing and therefore a text\''s characteristics. Natural language processing is prone to such variations in textual characteristics: it is said to be genre and domain dependent. This thesis investigates genre and domain dependencies in sentiment analysis. Its goal is to support the development of robust sentiment analysis approaches that work well and in a predictable manner under different conditions, i.e. for different genres and domains. Initially, we show that a prototypical approach to sentiment analysis -- viz. a supervised machine learning model based on word n-gram features -- performs differently on gold standards that originate from differing genres and domains, but performs similarly on gold standards that originate from resembling genres and domains. We show that these gold standards differ in certain textual characteristics, viz. their domain complexity. We find a strong linear relation between our approach\''s accuracy on a particular gold standard and its domain complexity, which we then use to estimate our approach\''s accuracy. Subsequently, we use certain textual characteristics -- viz. domain complexity, domain similarity, and readability -- in a variety of applications. Domain complexity and domain similarity measures are used to determine parameter settings in two tasks. Domain complexity guides us in model selection for in-domain polarity classification, viz. in decisions regarding word n-gram model order and word n-gram feature selection. Domain complexity and domain similarity guide us in domain adaptation. We propose a novel domain adaptation scheme and apply it to cross-domain polarity classification in semi- and unsupervised domain adaptation scenarios. Readability is used for feature engineering. We propose to adopt readability gradings, readability indicators as well as word and syntax distributions as features for subjectivity classification. Moreover, we generalize a framework for modeling and representing negation in machine learning-based sentiment analysis. This framework is applied to in-domain and cross-domain polarity classification. We investigate the relation between implicit and explicit negation modeling, the influence of negation scope detection methods, and the efficiency of the framework in different domains. Finally, we carry out a case study in which we transfer the core methods of our thesis -- viz. domain complexity-based accuracy estimation, domain complexity-based model selection, and negation modeling -- to a gold standard that originates from a genre and domain hitherto not used in this thesis

Measuring Short Text Semantic Similarity with Deep Learning Models

Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken, which is a subfield of artificial intelligence (AI). The development of NLP applications is challenging because computers traditionally require humans to speak" to them in a programming language that is precise, unambiguous and highly structured, or through a limited number of clearly enunciated voice commands. We study the use of deep learning models, the state-of-the-art artificial intelligence (AI) method, for the problem of measuring short text semantic similarity in NLP area. In particular, we propose a novel deep neural network architecture to identify semantic similarity for pairs of question sentence. In the proposed network, multiple channels of knowledge for pairs of question text can be utilized to improve the representation of text. Then a dense layer is used to learn a classifier for classifying duplicated question pairs. Through extensive experiments on the Quora test collection, our proposed approach has shown remarkable and significant improvement over strong baselines, which verifies the effectiveness of the deep models as well as the proposed deep multi-channel framework

Linguistic Competence and New Empiricism in Philosophy and Science

The topic of this dissertation is the nature of linguistic competence, the capacity to understand and produce sentences of natural language. I defend the empiricist account of linguistic competence embedded in the connectionist cognitive science. This strand of cognitive science has been opposed to the traditional symbolic cognitive science, coupled with transformational-generative grammar, which was committed to nativism due to the view that human cognition, including language capacity, should be construed in terms of symbolic representations and hardwired rules. Similarly, linguistic competence in this framework was regarded as being innate, rule-governed, domain-specific, and fundamentally different from performance, i.e., idiosyncrasies and factors governing linguistic behavior. I analyze state-of-the-art connectionist, deep learning models of natural language processing, most notably large language models, to see what they can tell us about linguistic competence. Deep learning is a statistical technique for the classification of patterns through which artificial intelligence researchers train artificial neural networks containing multiple layers that crunch a gargantuan amount of textual and/or visual data. I argue that these models suggest that linguistic competence should be construed as stochastic, pattern-based, and stemming from domain-general mechanisms. Moreover, I distinguish syntactic from semantic competence, and I show for each the ramifications of the endorsement of a connectionist research program as opposed to the traditional symbolic cognitive science and transformational-generative grammar. I provide a unifying front, consisting of usage-based theories, a construction grammar approach, and an embodied approach to cognition to show that the more multimodal and diverse models are in terms of architectural features and training data, the stronger the case is for the connectionist linguistic competence. I also propose to discard the competence vs. performance distinction as theoretically inferior so that a novel and integrative account of linguistic competence originating in connectionism and empiricism that I propose and defend in the dissertation could be put forward in scientific and philosophical literature

When Automated Assessment Meets Automated Content Generation: Examining Text Quality in the Era of GPTs

The use of machine learning (ML) models to assess and score textual data has become increasingly pervasive in an array of contexts including natural language processing, information retrieval, search and recommendation, and credibility assessment of online content. A significant disruption at the intersection of ML and text are text-generating large-language models such as generative pre-trained transformers (GPTs). We empirically assess the differences in how ML-based scoring models trained on human content assess the quality of content generated by humans versus GPTs. To do so, we propose an analysis framework that encompasses essay scoring ML-models, human and ML-generated essays, and a statistical model that parsimoniously considers the impact of type of respondent, prompt genre, and the ML model used for assessment model. A rich testbed is utilized that encompasses 18,460 human-generated and GPT-based essays. Results of our benchmark analysis reveal that transformer pretrained language models (PLMs) more accurately score human essay quality as compared to CNN/RNN and feature-based ML methods. Interestingly, we find that the transformer PLMs tend to score GPT-generated text 10-15\% higher on average, relative to human-authored documents. Conversely, traditional deep learning and feature-based ML models score human text considerably higher. Further analysis reveals that although the transformer PLMs are exclusively fine-tuned on human text, they more prominently attend to certain tokens appearing only in GPT-generated text, possibly due to familiarity/overlap in pre-training. Our framework and results have implications for text classification settings where automated scoring of text is likely to be disrupted by generative AI.Comment: Data available at: https://github.com/nd-hal/automated-ML-scoring-versus-generatio