Temporal disambiguation of relative temporal expressions in clinical texts using temporally fine-tuned contextual word embeddings.

Temporal reasoning is the ability to extract and assimilate temporal information to reconstruct a series of events such that they can be reasoned over to answer questions involving time. Temporal reasoning in the clinical domain is challenging due to specialized medical terms and nomenclature, shorthand notation, fragmented text, a variety of writing styles used by different medical units, redundancy of information that has to be reconciled, and an increased number of temporal references as compared to general domain texts. Work in the area of clinical temporal reasoning has progressed, but the current state-of-the-art still has a ways to go before practical application in the clinical setting will be possible. Much of the current work in this field is focused on direct and explicit temporal expressions and identifying temporal relations. However, there is little work focused on relative temporal expressions, which can be difficult to normalize, but are vital to ordering events on a timeline. This work introduces a new temporal expression recognition and normalization tool, Chrono, that normalizes temporal expressions into both SCATE and TimeML schemes. Chrono advances clinical timeline extraction as it is capable of identifying more vague and relative temporal expressions than the current state-of-the-art and utilizes contextualized word embeddings from fine-tuned BERT models to disambiguate temporal types, which achieves state-of-the-art performance on relative temporal expressions. In addition, this work shows that fine-tuning BERT models on temporal tasks modifies the contextualized embeddings so that they achieve improved performance in classical SVM and CNN classifiers. Finally, this works provides a new tool for linking temporal expressions to events or other entities by introducing a novel method to identify which tokens an entire temporal expression is paying the most attention to by summarizing the attention weight matrices output by BERT models

Event-based Access to Historical Italian War Memoirs

The progressive digitization of historical archives provides new, often domain specific, textual resources that report on facts and events which have happened in the past; among these, memoirs are a very common type of primary source. In this paper, we present an approach for extracting information from Italian historical war memoirs and turning it into structured knowledge. This is based on the semantic notions of events, participants and roles. We evaluate quantitatively each of the key-steps of our approach and provide a graph-based representation of the extracted knowledge, which allows to move between a Close and a Distant Reading of the collection.Comment: 23 pages, 6 figure

暗黙知の獲得に関連する要因の経験的調査

Tohoku University乾健太郎課

Dependency Enhanced Contextual Representations for Japanese Temporal Relation Classification

Ochanomizu UniversityKyoto UnivresityNational Institute for Japanese Language and LinguisticsOchanomizu UniversityOchanomizu Universit

Sentiment Analysis for Performance Evaluation of Maintenance in Healthcare

This paper presents a framework which makes use of Sentiment Analysis techniques for retrieving Real World Data (RWD) starting from scheduled and corrective maintenance data. The scope of the analysis is to automatically extract features from maintenance work orders, in order to calculate Key Performance Indicators of maintenance operations on medical devices, for Health Technologies Assessment purposes. Data are extracted from Computerized Maintenance Management System reports of healthcare facilities

Adaptive sentiment analysis

Domain dependency is one of the most challenging problems in the field of sentiment analysis. Although most sentiment analysis methods have decent performance if they are targeted at a specific domain and writing style, they do not usually work well with texts that are originated outside of their domain boundaries. Often there is a need to perform sentiment analysis in a domain where no labelled document is available. To address this scenario, researchers have proposed many domain adaptation or unsupervised sentiment analysis methods. However, there is still much room for improvement, as those methods typically cannot match conventional supervised sentiment analysis methods. In this thesis, we propose a novel aspect-level sentiment analysis method that seamlessly integrates lexicon- and learning-based methods. While its performance is comparable to existing approaches, it is less sensitive to domain boundaries and can be applied to cross-domain sentiment analysis when the target domain is similar to the source domain. It also offers more structured and readable results by detecting individual topic aspects and determining their sentiment strengths. Furthermore, we investigate a novel approach to automatically constructing domain-specific sentiment lexicons based on distributed word representations (aka word embeddings). The induced lexicon has quality on a par with a handcrafted one and could be used directly in a lexiconbased algorithm for sentiment analysis, but we find that a two-stage bootstrapping strategy could further boost the sentiment classification performance. Compared to existing methods, such an end-to-end nearly-unsupervised approach to domain-specific sentiment analysis works out of the box for any target domain, requires no handcrafted lexicon or labelled corpus, and achieves sentiment classification accuracy comparable to that of fully supervised approaches. Overall, the contribution of this Ph.D. work to the research field of sentiment analysis is twofold. First, we develop a new sentiment analysis system which can — in a nearlyunsupervised manner—adapt to the domain at hand and perform sentiment analysis with minimal loss of performance. Second, we showcase this system in several areas (including finance, politics, and e-business), and investigate particularly the temporal dynamics of sentiment in such contexts