GRAPH BASESD WORD SENSE DISAMBIGUATION FOR CLINICAL ABBREVIATIONS USING APACHE SPARK

Identification of the correct sense for an ambiguous word is one of the major challenges for language processing in all domains. Word Sense Disambiguation is the task of identifying the correct sense of an ambiguous word by referencing the surrounding context of the word. Similar to the narrative documents, clinical documents suffer from ambiguity issues that impact automatic extraction of correct sense from the document. In this project, we propose a graph-based solution based on an algorithm originally implemented by Osmar R. Zaine et al. for word sense disambiguation specifically focusing on clinical text. The algorithm makes use of proposed UMLS Metathesaurus as its source of knowledge. As an enhancement to the existing implementation of the algorithm, this project uses Apache Spark - A Big Data Technology for cluster based distributed processing and performance optimization

Using Context Awareness to Improve Domain-Specific Named Entity Disambiguation

In this project we designed and implemented a system based on the Learning To Rank framework to perform Named Entity Disambiguation (NED) of ancient author names and work titles being parts of canonical bibliographic citations. The data is made of abstracts extracted from modern publications in the context of Classical Studies. We had to deal with domain specific challenges like the small set of available anno- tated data, the high level of ambiguity of the citations and a specific knowledge base which does not include the common properties of the knowledge bases usually used in state-of-the-art NED systems like Wikipedia. Finally our system improved the already implemented baseline system and reached a F1 score of 77.62% (+7.1%) and 71.88% accuracy (+10.2%). We also demonstrated how we can further improve the disambiguation by exploiting the co-occurrence probability of entities extracted from the corpus. With this method we improved our system by 6.8% in terms of accuracy on a sub-set of 59 documents

HEALTH GeoJunction: place-time-concept browsing of health publications

<p>Abstract</p> <p>Background</p> <p>The volume of health science publications is escalating rapidly. Thus, keeping up with developments is becoming harder as is the task of finding important cross-domain connections. When geographic location is a relevant component of research reported in publications, these tasks are more difficult because standard search and indexing facilities have limited or no ability to identify geographic foci in documents. This paper introduces <it><smcaps>HEALTH</smcaps> GeoJunction</it>, a web application that supports researchers in the task of quickly finding scientific publications that are relevant geographically and temporally as well as thematically.</p> <p>Results</p> <p><it><smcaps>HEALTH</smcaps> GeoJunction </it>is a geovisual analytics-enabled web application providing: (a) web services using computational reasoning methods to extract place-time-concept information from bibliographic data for documents and (b) visually-enabled place-time-concept query, filtering, and contextualizing tools that apply to both the documents and their extracted content. This paper focuses specifically on strategies for visually-enabled, iterative, facet-like, place-time-concept filtering that allows analysts to quickly drill down to scientific findings of interest in PubMed abstracts and to explore relations among abstracts and extracted concepts in place and time. The approach enables analysts to: find publications without knowing all relevant query parameters, recognize unanticipated geographic relations within and among documents in multiple health domains, identify the thematic emphasis of research targeting particular places, notice changes in concepts over time, and notice changes in places where concepts are emphasized.</p> <p>Conclusions</p> <p>PubMed is a database of over 19 million biomedical abstracts and citations maintained by the National Center for Biotechnology Information; achieving quick filtering is an important contribution due to the database size. Including geography in filters is important due to rapidly escalating attention to geographic factors in public health. The implementation of mechanisms for iterative place-time-concept filtering makes it possible to narrow searches efficiently and quickly from thousands of documents to a small subset that meet place-time-concept constraints. Support for a <it>more-like-this </it>query creates the potential to identify unexpected connections across diverse areas of research. Multi-view visualization methods support understanding of the place, time, and concept components of document collections and enable comparison of filtered query results to the full set of publications.</p

SMAPH: A Piggyback Approach for Entity-Linking in Web Queries

We study the problem of linking the terms of a web-search query to a semantic representation given by the set of entities (a.k.a. concepts) mentioned in it. We introduce SMAPH, a system that performs this task using the information coming from a web search engine, an approach we call “piggybacking.” We employ search engines to alleviate the noise and irregularities that characterize the language of queries. Snippets returned as search results also provide a context for the query that makes it easier to disambiguate the meaning of the query. From the search results, SMAPH builds a set of candidate entities with high coverage. This set is filtered by linking back the candidate entities to the terms occurring in the input query, ensuring high precision. A greedy disambiguation algorithm performs this filtering; it maximizes the coherence of the solution by itera- tively discovering the pertinent entities mentioned in the query. We propose three versions of SMAPH that outperform state-of-the-art solutions on the known benchmarks and on the GERDAQ dataset, a novel dataset that we have built specifically for this problem via crowd-sourcing and that we make publicly available

Interactive Machine Learning with Applications in Health Informatics

Recent years have witnessed unprecedented growth of health data, including millions of biomedical research publications, electronic health records, patient discussions on health forums and social media, fitness tracker trajectories, and genome sequences. Information retrieval and machine learning techniques are powerful tools to unlock invaluable knowledge in these data, yet they need to be guided by human experts. Unlike training machine learning models in other domains, labeling and analyzing health data requires highly specialized expertise, and the time of medical experts is extremely limited. How can we mine big health data with little expert effort? In this dissertation, I develop state-of-the-art interactive machine learning algorithms that bring together human intelligence and machine intelligence in health data mining tasks. By making efficient use of human expert's domain knowledge, we can achieve high-quality solutions with minimal manual effort. I first introduce a high-recall information retrieval framework that helps human users efficiently harvest not just one but as many relevant documents as possible from a searchable corpus. This is a common need in professional search scenarios such as medical search and literature review. Then I develop two interactive machine learning algorithms that leverage human expert's domain knowledge to combat the curse of "cold start" in active learning, with applications in clinical natural language processing. A consistent empirical observation is that the overall learning process can be reliably accelerated by a knowledge-driven "warm start", followed by machine-initiated active learning. As a theoretical contribution, I propose a general framework for interactive machine learning. Under this framework, a unified optimization objective explains many existing algorithms used in practice, and inspires the design of new algorithms.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147518/1/raywang_1.pd

BSQA: integrated text mining using entity relation semantics extracted from biological literature of insects

Text mining is one promising way of extracting information automatically from the vast biological literature. To maximize its potential, the knowledge encoded in the text should be translated to some semantic representation such as entities and relations, which could be analyzed by machines. But large-scale practical systems for this purpose are rare. We present BeeSpace question/answering (BSQA) system that performs integrated text mining for insect biology, covering diverse aspects from molecular interactions of genes to insect behavior. BSQA recognizes a number of entities and relations in Medline documents about the model insect, Drosophila melanogaster. For any text query, BSQA exploits entity annotation of retrieved documents to identify important concepts in different categories. By utilizing the extracted relations, BSQA is also able to answer many biologically motivated questions, from simple ones such as, which anatomical part is a gene expressed in, to more complex ones involving multiple types of relations. BSQA is freely available at http://www.beespace.uiuc.edu/QuestionAnswer

Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). arXiv admin note: substantial text overlap with arXiv:1610.0770

Word Sense Disambiguation for clinical abbreviations

Abbreviations are extensively used in electronic health records (EHR) of patients as well as medical documentation, reaching 30-50% of the words in clinical narrative. There are more than 197,000 unique medical abbreviations found in the clinical text and their meanings vary depending on the context in which they are used. Since data in electronic health records could be shareable across health information systems (hospitals, primary care centers, etc.) as well as others such as insurance companies information systems, it is essential determining the correct meaning of the abbreviations to avoid misunderstandings. Clinical abbreviations have specific characteristic that do not follow any standard rules for creating them. This makes it complicated to find said abbreviations and corresponding meanings. Furthermore, there is an added difficulty to working with clinical data due to privacy reasons, since it is essential to have them in order to develop and test algorithms. Word sense disambiguation (WSD) is an essential task in natural language processing (NLP) applications such as information extraction, chatbots and summarization systems among others. WSD aims to identify the correct meaning of the ambiguous word which has more than one meaning. Disambiguating clinical abbreviations is a type of lexical sample WSD task. Previous research works adopted supervised, unsupervised and Knowledge-based (KB) approaches to disambiguate clinical abbreviations. This thesis aims to propose a classification model that apart from disambiguating well known abbreviations also disambiguates rare and unseen abbreviations using the most recent deep neural network architectures for language modeling. In clinical abbreviation disambiguation several resources and disambiguation models were encountered. Different classification approaches used to disambiguate the clinical abbreviations were investigated in this thesis. Considering that computers do not directly understand texts, different data representations were implemented to capture the meaning of the words. Since it is also necessary to measure the performance of algorithms, the evaluation measurements used are discussed. As the different solutions proposed to clinical WSD we have explored static word embeddings data representation on 13 English clinical abbreviations of the UMN data set (from University of Minnesota) by testing traditional supervised machine learning algorithms separately for each abbreviation. Moreover, we have utilized a transformer-base pretrained model that was fine-tuned as a multi-classification classifier for the whole data set (75 abbreviations of the UMN data set). The aim of implementing just one multi-class classifier is to predict rare and unseen abbreviations that are most common in clinical narrative. Additionally, other experiments were conducted for a different type of abbreviations (scientific abbreviations and acronyms) by defining a hybrid approach composed of supervised and knowledge-based approaches. Most previous works tend to build a separated classifier for each clinical abbreviation, tending to leverage different data resources to overcome the data acquisition bottleneck. However, those models were restricted to disambiguate terms that have been seen in trained data. Meanwhile, based on our results, transfer learning by fine-tuning a transformer-based model could predict rare and unseen abbreviations. A remaining challenge for future work is to improve the model to automate the disambiguation of clinical abbreviations on run-time systems by implementing self-supervised learning models.Las abreviaturas se utilizan ampliamente en las historias clínicas electrónicas de los pacientes y en mucha documentación médica, llegando a ser un 30-50% de las palabras empleadas en narrativa clínica. Existen más de 197.000 abreviaturas únicas usadas en textos clínicos siendo términos altamente ambiguos El significado de las abreviaturas varía en función del contexto en el que se utilicen. Dado que los datos de las historias clínicas electrónicas pueden compartirse entre servicios, hospitales, centros de atención primaria así como otras organizaciones como por ejemplo, las compañías de seguros es fundamental determinar el significado correcto de las abreviaturas para evitar además eventos adversos relacionados con la seguridad del paciente. Nuevas abreviaturas clínicas aparecen constantemente y tienen la característica específica de que no siguen ningún estándar para su creación. Esto hace que sea muy difícil disponer de un recurso con todas las abreviaturas y todos sus significados. A todo esto hay que añadir la dificultad para trabajar con datos clínicos por cuestiones de privacidad cuando es esencial disponer de ellos para poder desarrollar algoritmos para su tratamiento. La desambiguación del sentido de las palabras (WSD, en inglés) es una tarea esencial en tareas de procesamiento del lenguaje natural (PLN) como extracción de información, chatbots o generadores de resúmenes, entre otros. WSD tiene como objetivo identificar el significado correcto de una palabra ambigua (que tiene más de un significado). Esta tarea se ha abordado previamente utilizando tanto enfoques supervisados, no supervisados así como basados en conocimiento. Esta tesis tiene como objetivo definir un modelo de clasificación que además de desambiguar abreviaturas conocidas desambigüe también abreviaturas menos frecuentes que no han aparecido previamente en los conjuntos de entrenaminto utilizando las arquitecturas de redes neuronales profundas más recientes relacionadas ocn los modelos del lenguaje. En la desambiguación de abreviaturas clínicas se emplean diversos recursos y modelos de desambiguación. Se han investigado los diferentes enfoques de clasificación utilizados para desambiguar las abreviaturas clínicas. Dado que un ordenador no comprende directamente los textos, se han implementado diferentes representaciones de textos para capturar el significado de las palabras. Puesto que también es necesario medir el desempeño de cualquier algoritmo, se describen también las medidas de evaluación utilizadas. La mayoría de los trabajos previos se han basado en la construcción de un clasificador separado para cada abreviatura clínica. De este modo, tienden a aprovechar diferentes recursos de datos para superar el cuello de botella de la adquisición de datos. Sin embargo, estos modelos se limitaban a desambiguar con los datos para los que el sistema había sido entrenado. Se han explorado además representaciones basadas vectores de palabras (word embeddings) estáticos para 13 abreviaturas clínicas en el corpus UMN en inglés (de la University of Minnesota) utilizando algoritmos de clasificación tradicionales de aprendizaje automático supervisados (un clasificador por cada abreviatura). Se ha llevado a cabo un segundo experimento utilizando un modelo multi-clasificador sobre todo el conjunto de las 75 abreviaturas del corpus UMN basado en un modelo Transformer pre-entrenado. El objetivo ha sido implementar un clasificador multiclase para predecir también abreviaturas raras y no vistas. Se realizó un experimento adicional para siglas científicas en documentos de dominio abierto mediante la aplicación de un enfoque híbrido compuesto por enfoques supervisados y basados en el conocimiento. Así, basándonos en los resultados de esta tesis, el aprendizaje por transferencia (transfer learning) mediante el ajuste (fine-tuning) de un modelo de lenguaje preentrenado podría predecir abreviaturas raras y no vistas sin necesidad de entrenarlas previamente. Un reto pendiente para el trabajo futuro es mejorar el modelo para automatizar la desambiguación de las abreviaturas clínicas en tiempo de ejecución mediante la implementación de modelos de aprendizaje autosupervisados.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Israel González Carrasco.- Secretario: Leonardo Campillos Llanos.- Vocal: Ana María García Serran

From Frequency to Meaning: Vector Space Models of Semantics

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field