Effect of Term Weighting on Keyword Extraction in Hierarchical Category Structure

While there have been several studies related to the effect of term weighting on classification accuracy, relatively few works have been conducted on how term weighting affects the quality of keywords extracted for characterizing a document or a category (i.e., document collection). Moreover, many tasks require more complicated category structure, such as hierarchical and network category structure, rather than a flat category structure. This paper presents a qualitative and quantitative study on how term weighting affects keyword extraction in the hierarchical category structure, in comparison to the flat category structure. A hierarchical structure triggers special characteristic in assigning a set of keywords or tags to represent a document or a document collection, with support of statistics in a hierarchy, including category itself, its parent category, its child categories, and sibling categories. An enhancement of term weighting is proposed particularly in the form of a series of modified TFIDF's, for improving keyword extraction. A text collection of public-hearing opinions is used to evaluate variant TFs and IDFs to identify which types of information in hierarchical category structure are useful. By experiments, we found that the most effective IDF family, namely TF-IDFr, is identity>sibling>child>parent in order. The TF-IDFr outperforms the vanilla version of TFIDF with a centroid-based classifier

Advancement Auto-Assessment of Students Knowledge States from Natural Language Input

Knowledge Assessment is a key element in adaptive instructional systems and in particular in Intelligent Tutoring Systems because fully adaptive tutoring presupposes accurate assessment. However, this is a challenging research problem as numerous factors affect students’ knowledge state estimation such as the difficulty level of the problem, time spent in solving the problem, etc. In this research work, we tackle this research problem from three perspectives: assessing the prior knowledge of students, assessing the natural language short and long students’ responses, and knowledge tracing.Prior knowledge assessment is an important component of knowledge assessment as it facilitates the adaptation of the instruction from the very beginning, i.e., when the student starts interacting with the (computer) tutor. Grouping students into groups with similar mental models and patterns of prior level of knowledge allows the system to select the right level of scaffolding for each group of students. While not adapting instruction to each individual learner, the advantage of adapting to groups of students based on a limited number of prior knowledge levels has the advantage of decreasing the authoring costs of the tutoring system. To achieve this goal of identifying or clustering students based on their prior knowledge, we have employed effective clustering algorithms. Automatically assessing open-ended student responses is another challenging aspect of knowledge assessment in ITSs. In dialogue-based ITSs, the main interaction between the learner and the system is natural language dialogue in which students freely respond to various system prompts or initiate dialogue moves in mixed-initiative dialogue systems. Assessing freely generated student responses in such contexts is challenging as students can express the same idea in different ways owing to different individual style preferences and varied individual cognitive abilities. To address this challenging task, we have proposed several novel deep learning models as they are capable to capture rich high-level semantic features of text. Knowledge tracing (KT) is an important type of knowledge assessment which consists of tracking students’ mastery of knowledge over time and predicting their future performances. Despite the state-of-the-art results of deep learning in this task, it has many limitations. For instance, most of the proposed methods ignore pertinent information (e.g., Prior knowledge) that can enhance the knowledge tracing capability and performance. Working toward this objective, we have proposed a generic deep learning framework that accounts for the engagement level of students, the difficulty of questions and the semantics of the questions and uses a novel times series model called Temporal Convolutional Network for future performance prediction. The advanced auto-assessment methods presented in this dissertation should enable better ways to estimate learner’s knowledge states and in turn the adaptive scaffolding those systems can provide which in turn should lead to more effective tutoring and better learning gains for students. Furthermore, the proposed method should enable more scalable development and deployment of ITSs across topics and domains for the benefit of all learners of all ages and backgrounds

The Detection of Contradictory Claims in Biomedical Abstracts

Research claims in the biomedical domain are not always consistent, and may even be contradictory. This thesis explores contradictions between research claims in order to determine whether or not it is possible to develop a solution to automate the detection of such phenomena. Such a solution will help decision-makers, including researchers, to alleviate the effects of contradictory claims on their decisions. This study develops two methodologies to construct corpora of contradictions. The first methodology utilises systematic reviews to construct a manually-annotated corpus of contradictions. The second methodology uses a different approach to construct a corpus of contradictions which does not rely on human annotation. This methodology is proposed to overcome the limitations of the manual annotation approach. Moreover, this thesis proposes a pipeline to detect contradictions in abstracts. The pipeline takes a question and a list of research abstracts which may contain answers to it. The output of the pipeline is a list of sentences extracted from abstracts which answer the question, where each sentence is annotated with an assertion value with respect to the question. Claims which feature opposing assertion values are considered as potentially contradictory claims. The research demonstrates that automating the detection of contradictory claims in research abstracts is a feasible problem

A Human-centric Approach to NLP in Healthcare Applications

The abundance of personal health information available to healthcare professionals can be a facilitator to better care. However, it can also be a barrier, as the relevant information is often buried in the sheer amount of personal data, and healthcare professionals already lack time to take care of both patients and their data. This dissertation focuses on the role of natural language processing (NLP) in healthcare and how it can surface information relevant to healthcare professionals by modeling the extensive collections of documents that describe those whom they serve. In this dissertation, the extensive natural language data about a person is modeled as a set of documents, where the model inference is at the level of the individual, but evidence supporting that inference is found in a subset of their documents. The effectiveness of this modeling approach is demonstrated in the context of three healthcare applications. In the first application, clinical coding, document-level attention is used to model the hierarchy between a clinical encounter and its documents, jointly learning the encounter labels and the assignment of credits to specific documents. The second application, suicidality assessment using social media, further investigates how document-level attention can surface "high-signal" posts from the document set representing a potentially at-risk individual. Finally, the third application aims to help healthcare professionals write discharge summaries using an extract-then-abstract multidocument summarization pipeline to surface relevant information. As in many healthcare applications, these three applications seek to assist, not replace, clinicians. Evaluation and model design thus centers around healthcare professionals' needs. In clinical coding, document-level attention is shown to align well with professional clinical coders' expectations of evidence. In suicidality assessment, document-level attention leads to better and more time-efficient assessment by surfacing document-level evidence, shown empirically using a theoretically grounded time-aware evaluation measure and a dataset annotated by suicidality experts. Finally, extract-then-abstract summarization pipelines that assist healthcare professionals in writing discharge summaries are evaluated by their ability to surface faithful and relevant evidence

Lexical complexity prediction: an overview

The occurrence of unknown words in texts significantly hinders reading comprehension. To improve accessibility for specific target populations, computational modeling has been applied to identify complex words in texts and substitute them for simpler alternatives. In this article, we present an overview of computational approaches to lexical complexity prediction focusing on the work carried out on English data. We survey relevant approaches to this problem which include traditional machine learning classifiers (e.g., SVMs, logistic regression) and deep neural networks as well as a variety of features, such as those inspired by literature in psycholinguistics as well as word frequency, word length, and many others. Furthermore, we introduce readers to past competitions and available datasets created on this topic. Finally, we include brief sections on applications of lexical complexity prediction, such as readability and text simplification, together with related studies on languages other than English

Proceedings of the 17th Annual Conference of the European Association for Machine Translation

Proceedings of the 17th Annual Conference of the European Association for Machine Translation (EAMT

Cross-Lingual and Low-Resource Sentiment Analysis

Identifying sentiment in a low-resource language is essential for understanding opinions internationally and for responding to the urgent needs of locals affected by disaster incidents in different world regions. While tools and resources for recognizing sentiment in high-resource languages are plentiful, determining the most effective methods for achieving this task in a low-resource language which lacks annotated data is still an open research question. Most existing approaches for cross-lingual sentiment analysis to date have relied on high-resource machine translation systems, large amounts of parallel data, or resources only available for Indo-European languages. This work presents methods, resources, and strategies for identifying sentiment cross-lingually in a low-resource language. We introduce a cross-lingual sentiment model which can be trained on a high-resource language and applied directly to a low-resource language. The model offers the feature of lexicalizing the training data using a bilingual dictionary, but can perform well without any translation into the target language. Through an extensive experimental analysis, evaluated on 17 target languages, we show that the model performs well with bilingual word vectors pre-trained on an appropriate translation corpus. We compare in-genre and in-domain parallel corpora, out-of-domain parallel corpora, in-domain comparable corpora, and monolingual corpora, and show that a relatively small, in-domain parallel corpus works best as a transfer medium if it is available. We describe the conditions under which other resources and embedding generation methods are successful, and these include our strategies for leveraging in-domain comparable corpora for cross-lingual sentiment analysis. To enhance the ability of the cross-lingual model to identify sentiment in the target language, we present new feature representations for sentiment analysis that are incorporated in the cross-lingual model: bilingual sentiment embeddings that are used to create bilingual sentiment scores, and a method for updating the sentiment embeddings during training by lexicalization of the target language. This feature configuration works best for the largest number of target languages in both untargeted and targeted cross-lingual sentiment experiments. The cross-lingual model is studied further by evaluating the role of the source language, which has traditionally been assumed to be English. We build cross-lingual models using 15 source languages, including two non-European and non-Indo-European source languages: Arabic and Chinese. We show that language families play an important role in the performance of the model, as does the morphological complexity of the source language. In the last part of the work, we focus on sentiment analysis towards targets. We study Arabic as a representative morphologically complex language and develop models and morphological representation features for identifying entity targets and sentiment expressed towards them in Arabic open-domain text. Finally, we adapt our cross-lingual sentiment models for the detection of sentiment towards targets. Through cross-lingual experiments on Arabic and English, we demonstrate that our findings regarding resources, features, and language also hold true for the transfer of targeted sentiment