Semantic Similarity in a Taxonomy by Refining the Relatedness of Concept Intended Senses

In this paper, we present an evolution of a novel approach for evaluating semantic similarity in a taxonomy, based on the well-known notion of information content. Such an approach takes into account not only the generic sense of a concept but also its intended sense in a given context. In this work semantic similarity is evaluated according to a refined relatedness measure between the generic sense and the intended sense of a concept, leading to higher correlation values with human judgment with respect to the original proposal

Information Retrieval Performance Enhancement Using The Average Standard Estimator And The Multi-criteria Decision Weighted Set

Information retrieval is much more challenging than traditional small document collection retrieval. The main difference is the importance of correlations between related concepts in complex data structures. These structures have been studied by several information retrieval systems. This research began by performing a comprehensive review and comparison of several techniques of matrix dimensionality estimation and their respective effects on enhancing retrieval performance using singular value decomposition and latent semantic analysis. Two novel techniques have been introduced in this research to enhance intrinsic dimensionality estimation, the Multi-criteria Decision Weighted model to estimate matrix intrinsic dimensionality for large document collections and the Average Standard Estimator (ASE) for estimating data intrinsic dimensionality based on the singular value decomposition (SVD). ASE estimates the level of significance for singular values resulting from the singular value decomposition. ASE assumes that those variables with deep relations have sufficient correlation and that only those relationships with high singular values are significant and should be maintained. Experimental results over all possible dimensions indicated that ASE improved matrix intrinsic dimensionality estimation by including the effect of both singular values magnitude of decrease and random noise distracters. Analysis based on selected performance measures indicates that for each document collection there is a region of lower dimensionalities associated with improved retrieval performance. However, there was clear disagreement between the various performance measures on the model associated with best performance. The introduction of the multi-weighted model and Analytical Hierarchy Processing (AHP) analysis helped in ranking dimensionality estimation techniques and facilitates satisfying overall model goals by leveraging contradicting constrains and satisfying information retrieval priorities. ASE provided the best estimate for MEDLINE intrinsic dimensionality among all other dimensionality estimation techniques, and further, ASE improved precision and relative relevance by 10.2% and 7.4% respectively. AHP analysis indicates that ASE and the weighted model ranked the best among other methods with 30.3% and 20.3% in satisfying overall model goals in MEDLINE and 22.6% and 25.1% for CRANFIELD. The weighted model improved MEDLINE relative relevance by 4.4%, while the scree plot, weighted model, and ASE provided better estimation of data intrinsic dimensionality for CRANFIELD collection than Kaiser-Guttman and Percentage of variance. ASE dimensionality estimation technique provided a better estimation of CISI intrinsic dimensionality than all other tested methods since all methods except ASE tend to underestimate CISI document collection intrinsic dimensionality. ASE improved CISI average relative relevance and average search length by 28.4% and 22.0% respectively. This research provided evidence supporting a system using a weighted multi-criteria performance evaluation technique resulting in better overall performance than a single criteria ranking model. Thus, the weighted multi-criteria model with dimensionality reduction provides a more efficient implementation for information retrieval than using a full rank model

Harnessing sense-level information for semantically augmented knowledge extraction

Nowadays, building accurate computational models for the semantics of language lies at the very core of Natural Language Processing and Artificial Intelligence. A first and foremost step in this respect consists in moving from word-based to sense-based approaches, in which operating explicitly at the level of word senses enables a model to produce more accurate and unambiguous results. At the same time, word senses create a bridge towards structured lexico-semantic resources, where the vast amount of available machine-readable information can help overcome the shortage of annotated data in many languages and domains of knowledge. This latter phenomenon, known as the knowledge acquisition bottlneck, is a crucial problem that hampers the development of large-scale, data-driven approaches for many Natural Language Processing tasks, especially when lexical semantics is directly involved. One of these tasks is Information Extraction, where an effective model has to cope with data sparsity, as well as with lexical ambiguity that can arise at the level of both arguments and relational phrases. Even in more recent Information Extraction approaches where semantics is implicitly modeled, these issues have not yet been addressed in their entirety. On the other hand, however, having access to explicit sense-level information is a very demanding task on its own, which can rarely be performed with high accuracy on a large scale. With this in mind, in ths thesis we will tackle a two-fold objective: our first focus will be on studying fully automatic approaches to obtain high-quality sense-level information from textual corpora; then, we will investigate in depth where and how such sense-level information has the potential to enhance the extraction of knowledge from open text. In the first part of this work, we will explore three different disambiguation scenar- ios (semi-structured text, parallel text, and definitional text) and devise automatic disambiguation strategies that are not only capable of scaling to different corpus sizes and different languages, but that actually take advantage of a multilingual and/or heterogeneous setting to improve and refine their performance. As a result, we will obtain three sense-annotated resources that, when tested experimentally with a baseline system in a series of downstream semantic tasks (i.e. Word Sense Disam- biguation, Entity Linking, Semantic Similarity), show very competitive performances on standard benchmarks against both manual and semi-automatic competitors. In the second part we will instead focus on Information Extraction, with an emphasis on Open Information Extraction (OIE), where issues like sparsity and lexical ambiguity are especially critical, and study how to exploit at best sense-level information within the extraction process. We will start by showing that enforcing a deeper semantic analysis in a definitional setting enables a full-fledged extraction pipeline to compete with state-of-the-art approaches based on much larger (but noisier) data. We will then demonstrate how working at the sense level at the end of an extraction pipeline is also beneficial: indeed, by leveraging sense-based techniques, very heterogeneous OIE-derived data can be aligned semantically, and unified with respect to a common sense inventory. Finally, we will briefly shift the focus to the more constrained setting of hypernym discovery, and study a sense-aware supervised framework for the task that is robust and effective, even when trained on heterogeneous OIE-derived hypernymic knowledge

Neural Representations of Concepts and Texts for Biomedical Information Retrieval

Information retrieval (IR) methods are an indispensable tool in the current landscape of exponentially increasing textual data, especially on the Web. A typical IR task involves fetching and ranking a set of documents (from a large corpus) in terms of relevance to a user\u27s query, which is often expressed as a short phrase. IR methods are the backbone of modern search engines where additional system-level aspects including fault tolerance, scale, user interfaces, and session maintenance are also addressed. In addition to fetching documents, modern search systems may also identify snippets within the documents that are potentially most relevant to the input query. Furthermore, current systems may also maintain preprocessed structured knowledge derived from textual data as so called knowledge graphs, so certain types of queries that are posed as questions can be parsed as such; a response can be an output of one or more named entities instead of a ranked list of documents (e.g., what diseases are associated with EGFR mutations? ). This refined setup is often termed as question answering (QA) in the IR and natural language processing (NLP) communities. In biomedicine and healthcare, specialized corpora are often at play including research articles by scientists, clinical notes generated by healthcare professionals, consumer forums for specific conditions (e.g., cancer survivors network), and clinical trial protocols (e.g., www.clinicaltrials.gov). Biomedical IR is specialized given the types of queries and the variations in the texts are different from that of general Web documents. For example, scientific articles are more formal with longer sentences but clinical notes tend to have less grammatical conformity and are rife with abbreviations. There is also a mismatch between the vocabulary of consumers and the lingo of domain experts and professionals. Queries are also different and can range from simple phrases (e.g., COVID-19 symptoms ) to more complex implicitly fielded queries (e.g., chemotherapy regimens for stage IV lung cancer patients with ALK mutations ). Hence, developing methods for different configurations (corpus, query type, user type) needs more deliberate attention in biomedical IR. Representations of documents and queries are at the core of IR methods and retrieval methodology involves coming up with these representations and matching queries with documents based on them. Traditional IR systems follow the approach of keyword based indexing of documents (the so called inverted index) and matching query phrases against the document index. It is not difficult to see that this keyword based matching ignores the semantics of texts (synonymy at the lexeme level and entailment at phrase/clause/sentence levels) and this has lead to dimensionality reduction methods such as latent semantic indexing that generally have scale-related concerns; such methods also do not address similarity at the sentence level. Since the resurgence of neural network methods in NLP, the IR field has also moved to incorporate advances in neural networks into current IR methods. This dissertation presents four specific methodological efforts toward improving biomedical IR. Neural methods always begin with dense embeddings for words and concepts to overcome the limitations of one-hot encoding in traditional NLP/IR. In the first effort, we present a new neural pre-training approach to jointly learn word and concept embeddings for downstream use in applications. In the second study, we present a joint neural model for two essential subtasks of information extraction (IE): named entity recognition (NER) and entity normalization (EN). Our method detects biomedical concept phrases in texts and links them to the corresponding semantic types and entity codes. These first two studies provide essential tools to model textual representations as compositions of both surface forms (lexical units) and high level concepts with potential downstream use in QA. In the third effort, we present a document reranking model that can help surface documents that are likely to contain answers (e.g, factoids, lists) to a question in a QA task. The model is essentially a sentence matching neural network that learns the relevance of a candidate answer sentence to the given question parametrized with a bilinear map. In the fourth effort, we present another document reranking approach that is tailored for precision medicine use-cases. It combines neural query-document matching and faceted text summarization. The main distinction of this effort from previous efforts is to pivot from a query manipulation setup to transforming candidate documents into pseudo-queries via neural text summarization. Overall, our contributions constitute nontrivial advances in biomedical IR using neural representations of concepts and texts

An Enriched Information-Theoretic Definition of Semantic Similarity in a Taxonomy

This paper addresses the notion of semantic similarity between concepts organized according to a taxonomy, based on the well-known information content approach. This approach has been widely experimented in the literature over the years and, in general, outperforms other proposals which do not originate from it. However, it shows some limitations related to the notion of generic sense of a concept. In this paper we illustrate the problem arising by using the traditional approach, and a novel information-theoretic definition of semantic similarity in a taxonomy is proposed which also takes into account the intended sense of a concept in a given context. This proposal has been applied to some among the most representative state-of-the-art similarity measures based on the information content approach, and the experiment shows that it achieves very high correlation values with human judgment

Capturing and Measuring Thematic Relatedness

In this paper we explain the difference between two aspects of semantic relatedness: taxonomic and thematic relations. We notice the lack of evaluation tools for measuring thematic relatedness, identify two datasets that can be recommended as thematic benchmarks, and verify them experimentally. In further experiments, we use these datasets to perform a comprehensive analysis of the performance of an extensive sample of computational models of semantic relatedness, classified according to the sources of information they exploit. We report models that are best at each of the two dimensions of semantic relatedness and those that achieve a good balance between the two

A Deep Learning-Based Privacy-Preserving Model for Smart Healthcare in Internet of Medical Things Using Fog Computing

With the emergence of COVID-19, smart healthcare, the Internet of Medical Things, and big data-driven medical applications have become even more important. The biomedical data produced is highly confidential and private. Unfortunately, conventional health systems cannot support such a colossal amount of biomedical data. Hence, data is typically stored and shared through the cloud. The shared data is then used for different purposes, such as research and discovery of unprecedented facts. Typically, biomedical data appear in textual form (e.g., test reports, prescriptions, and diagnosis). Unfortunately, such data is prone to several security threats and attacks, for example, privacy and confidentiality breach. Although significant progress has been made on securing biomedical data, most existing approaches yield long delays and cannot accommodate real-time responses. This paper proposes a novel fog-enabled privacy-preserving model called [Formula: see text] sanitizer, which uses deep learning to improve the healthcare system. The proposed model is based on a Convolutional Neural Network with Bidirectional-LSTM and effectively performs Medical Entity Recognition. The experimental results show that [Formula: see text] sanitizer outperforms the state-of-the-art models with 91.14% recall, 92.63% in precision, and 92% F1-score. The sanitization model shows 28.77% improved utility preservation as compared to the state-of-the-art

Automatic privacy and utility evaluation of anonymized documents via deep learning

Text anonymization methods are evaluated by comparing their outputs with human-based anonymizations through standard information retrieval (IR) metrics. On the one hand, the residual disclosure risk is quantified with the recall metric, which gives the proportion of re-identifying terms successfully detected by the anonymization algorithm. On the other hand, the preserved utility is measured with the precision metric, which accounts the proportion of masked terms that were also annotated by the human experts. Nevertheless, because these evaluation metrics were meant for information retrieval rather than privacy-oriented tasks, they suffer from several drawbacks. First, they assume a unique ground truth, and this does not hold for text anonymization, where several masking choices could be equally valid to prevent re-identification. Second, annotation-based evaluation relies on human judgements, which are inherently subjective and may be prone to errors. Finally, both metrics weight terms uniformly, thereby ignoring the fact that the influence on the disclosure risk or on utility preservation of some terms may be much larger than of others. To overcome these drawbacks, in this thesis we propose two novel methods to evaluate both the disclosure risk and the utility preserved in anonymized texts. Our approach leverages deep learning methods to perform this evaluation automatically, thereby not requiring human annotations. For assessing disclosure risks, we propose using a re-identification attack, which we define as a multi-class classification task built on top of state-of-the art language models. To make it feasible, the attack has been designed to capture the means and computational resources expected to be available at the attacker's end. For utility assessment, we propose a method that measures the information loss incurred during the anonymization process, which relies on a neural masked language modeling. We illustrate the effectiveness of our methods by evaluating the disclosure risk and retained utility of several well-known techniques and tools for text anonymization on a common dataset. Empirical results show significant privacy risks for all of them (including manual anonymization) and consistently proportional utility preservation

Understanding and Enhancing the Use of Context for Machine Translation

To understand and infer meaning in language, neural models have to learn complicated nuances. Discovering distinctive linguistic phenomena from data is not an easy task. For instance, lexical ambiguity is a fundamental feature of language which is challenging to learn. Even more prominently, inferring the meaning of rare and unseen lexical units is difficult with neural networks. Meaning is often determined from context. With context, languages allow meaning to be conveyed even when the specific words used are not known by the reader. To model this learning process, a system has to learn from a few instances in context and be able to generalize well to unseen cases. The learning process is hindered when training data is scarce for a task. Even with sufficient data, learning patterns for the long tail of the lexical distribution is challenging. In this thesis, we focus on understanding certain potentials of contexts in neural models and design augmentation models to benefit from them. We focus on machine translation as an important instance of the more general language understanding problem. To translate from a source language to a target language, a neural model has to understand the meaning of constituents in the provided context and generate constituents with the same meanings in the target language. This task accentuates the value of capturing nuances of language and the necessity of generalization from few observations. The main problem we study in this thesis is what neural machine translation models learn from data and how we can devise more focused contexts to enhance this learning. Looking more in-depth into the role of context and the impact of data on learning models is essential to advance the NLP field. Moreover, it helps highlight the vulnerabilities of current neural networks and provides insights into designing more robust models.Comment: PhD dissertation defended on November 10th, 202

Knowledge graph embedding enhancement using ontological knowledge in the biomedical domain

The biomedical field is a critical area for natural language processing (NLP) applications because it involves a vast amount of unstructured data, including clinical notes, medical publications, and electronic health records. NLP techniques can help extract valuable information from these documents, such as disease symptoms, medication usage, and treatment outcomes, which can improve patient care and clinical decision-making. MAPS S.p.A. currently produces Clinika, a software that extracts knowledge from clinical corpora. Clinika performs the task of Named Entity Recognition (NER) by linking entities to medical concepts from an established knowledge base, in this case, the Unified Medical Language System (UMLS). This dissertation details how we approached designing and implementing a component for the new version of Clinika, specifically a mention embedder that uses embeddings to perform entity linking with UMLS concepts. We focused on enhancing existing dense contextual embeddings by injecting ontological knowledge, using two parallel approaches: (1) taking the embeddings as a by-product of an entity alignment model aided by an ontology, and (2) fine-tuning a contextual language model with custom sampling strategies. We evaluated both approaches with suitable experiments from the relevant literature. After testing, we discontinued the first approach but found more significant results using the second. The results on the tasks chosen to evaluate the embeddings were not promising, we address the causes in the Error Analysis section, and discuss further work on this topic