A Practical Incremental Learning Framework For Sparse Entity Extraction

This work addresses challenges arising from extracting entities from textual data, including the high cost of data annotation, model accuracy, selecting appropriate evaluation criteria, and the overall quality of annotation. We present a framework that integrates Entity Set Expansion (ESE) and Active Learning (AL) to reduce the annotation cost of sparse data and provide an online evaluation method as feedback. This incremental and interactive learning framework allows for rapid annotation and subsequent extraction of sparse data while maintaining high accuracy. We evaluate our framework on three publicly available datasets and show that it drastically reduces the cost of sparse entity annotation by an average of 85% and 45% to reach 0.9 and 1.0 F-Scores respectively. Moreover, the method exhibited robust performance across all datasets.Comment: https://www.aclweb.org/anthology/C18-1059

EmoLabel: Semi-Automatic Methodology for Emotion Annotation of Social Media Text

The exponential growth of the amount of subjective information on the Web 2.0. has caused an increasing interest from researchers willing to develop methods to extract emotion data from these new sources. One of the most important challenges in textual emotion detection is the gathering of data with emotion labels because of the subjectivity of assigning these labels. Basing on this rationale, the main objective of our research is to contribute to the resolution of this important challenge. This is tackled by proposing EmoLabel: a semi-automatic methodology based on pre-annotation, which consists of two main phases: (1) an automatic process to pre-annotate the unlabelled English sentences; and (2) a manual process of refinement where human annotators determine which is the dominant emotion. Our objective is to assess the influence of this automatic pre-annotation method on manual emotion annotation from two points of view: agreement and time needed for annotation. The evaluation performed demonstrates the benefits of pre-annotation processes since the results on annotation time show a gain of near 20% when the pre-annotation process is applied (Pre-ML) without reducing annotator performance. Moreover, the benefits of pre-annotation are higher in those contributors whose performance is low (inaccurate annotators).This research has been supported by the Spanish Government (ref. RTI2018-094653-B-C22) and the Valencian Government (grant no. PROMETEU/2018/089). It has also been funded by the FPI grant (BES-2013-065950) and the research stay grant (EEBB-I-17-12578) from the Spanish Ministry of Science and Innovation

ROAM: a Rich Object Appearance Model with Application to Rotoscoping

Rotoscoping, the detailed delineation of scene elements through a video shot, is a painstaking task of tremendous importance in professional post-production pipelines. While pixel-wise segmentation techniques can help for this task, professional rotoscoping tools rely on parametric curves that offer the artists a much better interactive control on the definition, editing and manipulation of the segments of interest. Sticking to this prevalent rotoscoping paradigm, we propose a novel framework to capture and track the visual aspect of an arbitrary object in a scene, given a first closed outline of this object. This model combines a collection of local foreground/background appearance models spread along the outline, a global appearance model of the enclosed object and a set of distinctive foreground landmarks. The structure of this rich appearance model allows simple initialization, efficient iterative optimization with exact minimization at each step, and on-line adaptation in videos. We demonstrate qualitatively and quantitatively the merit of this framework through comparisons with tools based on either dynamic segmentation with a closed curve or pixel-wise binary labelling

Improving Syntactic Parsing of Clinical Text Using Domain Knowledge

Syntactic parsing is one of the fundamental tasks of Natural Language Processing (NLP). However, few studies have explored syntactic parsing in the medical domain. This dissertation systematically investigated different methods to improve the performance of syntactic parsing of clinical text, including (1) Constructing two clinical treebanks of discharge summaries and progress notes by developing annotation guidelines that handle missing elements in clinical sentences; (2) Retraining four state-of-the-art parsers, including the Stanford parser, Berkeley parser, Charniak parser, and Bikel parser, using clinical treebanks, and comparing their performance to identify better parsing approaches; and (3) Developing new methods to reduce syntactic ambiguity caused by Prepositional Phrase (PP) attachment and coordination using semantic information. Our evaluation showed that clinical treebanks greatly improved the performance of existing parsers. The Berkeley parser achieved the best F-1 score of 86.39% on the MiPACQ treebank. For PP attachment, our proposed methods improved the accuracies of PP attachment by 2.35% on the MiPACQ corpus and 1.77% on the I2b2 corpus. For coordination, our method achieved a precision of 94.9% and a precision of 90.3% for the MiPACQ and i2b2 corpus, respectively. To further demonstrate the effectiveness of the improved parsing approaches, we applied outputs of our parsers to two external NLP tasks: semantic role labeling and temporal relation extraction. The experimental results showed that performance of both tasks’ was improved by using the parse tree information from our optimized parsers, with an improvement of 3.26% in F-measure for semantic role labelling and an improvement of 1.5% in F-measure for temporal relation extraction

Doctor of Philosophy

dissertationManual annotation of clinical texts is often used as a method of generating reference standards that provide data for training and evaluation of Natural Language Processing (NLP) systems. Manually annotating clinical texts is time consuming, expensive, and requires considerable cognitive effort on the part of human reviewers. Furthermore, reference standards must be generated in ways that produce consistent and reliable data but must also be valid in order to adequately evaluate the performance of those systems. The amount of labeled data necessary varies depending on the level of analysis, the complexity of the clinical use case, and the methods that will be used to develop automated machine systems for information extraction and classification. Evaluating methods that potentially reduce cost, manual human workload, introduce task efficiencies, and reduce the amount of labeled data necessary to train NLP tools for specific clinical use cases are active areas of research inquiry in the clinical NLP domain. This dissertation integrates a mixed methods approach using methodologies from cognitive science and artificial intelligence with manual annotation of clinical texts. Aim 1 of this dissertation identifies factors that affect manual annotation of clinical texts. These factors are further explored by evaluating approaches that may introduce efficiencies into manual review tasks applied to two different NLP development areas - semantic annotation of clinical concepts and identification of information representing Protected Health Information (PHI) as defined by HIPAA. Both experiments integrate iv different priming mechanisms using noninteractive and machine-assisted methods. The main hypothesis for this research is that integrating pre-annotation or other machineassisted methods within manual annotation workflows will improve efficiency of manual annotation tasks without diminishing the quality of generated reference standards

QASem Parsing: Text-to-text Modeling of QA-based Semantics

Several recent works have suggested to represent semantic relations with questions and answers, decomposing textual information into separate interrogative natural language statements. In this paper, we consider three QA-based semantic tasks - namely, QA-SRL, QANom and QADiscourse, each targeting a certain type of predication - and propose to regard them as jointly providing a comprehensive representation of textual information. To promote this goal, we investigate how to best utilize the power of sequence-to-sequence (seq2seq) pre-trained language models, within the unique setup of semi-structured outputs, consisting of an unordered set of question-answer pairs. We examine different input and output linearization strategies, and assess the effect of multitask learning and of simple data augmentation techniques in the setting of imbalanced training data. Consequently, we release the first unified QASem parsing tool, practical for downstream applications who can benefit from an explicit, QA-based account of information units in a text

Informative sample generation using class aware generative adversarial networks for classification of chest Xrays

Training robust deep learning (DL) systems for disease detection from medical images is challenging due to limited images covering different disease types and severity. The problem is especially acute, where there is a severe class imbalance. We propose an active learning (AL) framework to select most informative samples for training our model using a Bayesian neural network. Informative samples are then used within a novel class aware generative adversarial network (CAGAN) to generate realistic chest xray images for data augmentation by transferring characteristics from one class label to another. Experiments show our proposed AL framework is able to achieve state-of-the-art performance by using about $35\%$ of the full dataset, thus saving significant time and effort over conventional methods

Analyzing short-answer questions and their automatic scoring - studies on semantic relations in reading comprehension and the reduction of human annotation effort

Short-answer questions are a wide-spread exercise type in many educational areas. Answers given by learners to such questions are scored by teachers based on their content alone ignoring their linguistic correctness as far as possible. They typically have a length of up to a few sentences. Manual scoring is a time-consuming task, so that automatic scoring of short-answer questions using natural language processing techniques has become an important task. This thesis focuses on two aspects of short-answer questions and their scoring: First, we concentrate on a reading comprehension scenario for learners of German as a foreign language, where students answer questions about a reading text. Within this scenario, we examine the multiple relations between reading texts, learner answers and teacher-specified target answers. Second, we investigate how to reduce human scoring workload by both fully automatic and computer-assisted scoring. The latter is a scenario where scoring is not done entirely automatically, but where a teacher receives scoring support, for example, by means of clustering similar answers together. Addressing the first aspect, we conduct a series of corpus annotation studies which highlight the relations between pairs of learner answers and target answers, as well as between both types of answers and the reading text they refer to. We annotate sentences from the reading text that were potentially used by learners or teachers for constructing answers and observe that, unsurprisingly, most correct answers can easily be linked to the text; incorrect answers often link to the text as well, but are often backed up by a part of the text not relevant to answer the question. Based on these findings, we create a new baseline scoring model which considers for correctness whether learners looked for an answer in the right place or not. After identifying those links into the text, we label the relation between learner answers and target answers as well as between reading texts and answers by annotating entailment relations. In contrast to the widespread assumption that scoring can be fully mapped to the task of recognizing textual entailment, we find the two tasks to be only closely related and not completely equivalent. Correct answers do often, but not always, entail the target answer, as well as part of the related text, and incorrect answers do most of the time not stand in an entailment relation to the target answer, but often have some overlap with the text. This close relatedness allows us to use gold-standard entailment information to improve the performance of automatic scoring. We also use links between learner answers and both reading texts and target answers in a statistical alignment-based scoring approach using methods from machine translation and reach a performance comparable to an existing knowledge-based alignment approach. Our investigations into how human scoring effort can be reduced when learner answers are manually scored by teachers are based on two methods: active learning and clustering. In the active learning approach, we score particularly informative items first, i.e., items from which a classifier can learn most, identifying them using uncertainty-based sample selection. In this way, we reach a higher performance with a given number of annotation steps compared to randomly selected answers. In the second research strand, we use clustering methods to group similar answers together, such that groups of answers can be scored in one scoring step. In doing so, the number of necessary labeling steps can be substantially reduced. When comparing clustering-based scoring to classical supervised machine learning setups, where the human annotations are used to train a classifier, supervised machine learning is still in the lead in terms of performance, whereas clusters provide the advantage of structured output. However, we are able to close part of the performance gap by means of supervised feature selection and semi-supervised clustering. In an additional study, we investigate the automatic processing of learner language with respect to the performance of part-of-speech (POS) tagging tools. We manually annotate a German reading comprehension corpus both with spelling normalization and POS information and find that the performance of automatic POS tagging can be improved by spell-checking the data using the reading text as additional evidence for lexical material intended in a learner answer.Short-Answer-Fragen sind ein weit verbreiteter Aufgabentyp in vielen Bildungsbereichen. Die Antworten, die Lerner zu solchen Aufgaben geben, werden von Lehrenden allein auf Grundlage ihres Inhalts bewertet; linguistische Korrektheit wird soweit möglich ignoriert. Diese Doktorarbeit legt ihren Schwerpunkt auf zwei Aspekte im Zusammenhang mit Short- Answer-Fragen und ihrer Bewertung: Zum einen betrachten wir ein Leseverständnisszenario, bei dem Studenten Fragen zu Lesetexten beantworten. Dabei untersuchen wir insbesondere die verschiedenen Beziehungen, die es zwischen Lesetexten, Lernerantworten und vom Lehrer erstellten Musterantworten gibt. Zum anderen untersuchen wir, wie der menschliche Bewertungsaufwand durch voll-automatisches und computergestütztes Bewerten reduziert werden kann. Bei letzterem handelt es sich um ein Szenario, in dem Lehrer bei der Bewertung unterstützt werden, z.B. indem ähnliche Antworten automatisch gruppiert werden. Zur Untersuchung des ersten Aspekts unternehmen wir eine Reihe von Korpusannotationsstudien, die sowohl die Beziehungen zwischen Lerner- und Musterantworten beleuchten, als auch die Beziehung zwischen diesen Antworten und dem Lesetext, auf den sie sich beziehen. Wir annotieren Sätze aus dem Lesetext, die vermutlich bei der Formulierung einer Antwort benutzt wurden und machen die zu erwartende Beobachtung, dass die meisten korrekten Antworten problemlos mit bestimmten Textpassagen in Verbindung gebracht werden können. Inkorrekte Antworten haben ebenfalls oft eine Verbindung zu bestimmten Textpassagen, die aber oft für die jeweilige Frage nicht relevant sind. Auf Grundlage dieser Erkenntnisse entwerfen wir ein neues Baseline-Bewertungsmodell, das für die Korrektheit einer Antwort nur in Betracht zieht, ob der Lerner die Antwort an der richtigen Stelle im Lesetext gesucht hat oder nicht. Nachdem wir diese Verbindungen in den Text identifiziert haben, annotieren wir die Relation zwischen Lerner- und Musterantworten und zwischen Texten und Antworten mit Entailment- Relationen. Im Gegensatz zur der weitverbreiteten Annahme, dass das Bewerten von Short- Answer-Fragen und das Erkennen von Textual-Entailment-Relationen zwischen Lerner und Musterantworten sich direkt entsprechen, finden wir heraus, dass die beiden Aufgaben nur nahe verwandt aber nicht vollständig äquivalent sind. Korrekte Antworten entailen meistens, aber nicht immer, die Musterantwort und auch den entsprechenden Satz im Lesetext. Inkorrekte Antworten stehen meist in keiner Entailmentrelation mit der Musterantwort, haben aber oft zumindest teilweisen Overlap mit dem Text. Diese nahe Verwandtschaft erlaubt es uns, Goldstandard-Entailmentinformation zu benutzen, um die Performanz beim automatischen Bewerten zu verbessern. Wir benutzen die annotierten Verbindungen zwischen Lesetexten und Antworten auch in einem Scoringansatz, der auf statistischem Alignment basiert und Methoden aus dem Bereich der maschinellen Übersetzung nutzt. Dabei erreichen wir eine Scoringgenauigkeit, die mit Ansätzen, die ein existierendes wissensbasiertes Alignment nutzen, vergleichbar ist. Unsere Untersuchungen, wie der Bewertungsaufwand beim Menschen verringert werden kann, wenn Antworten vom Lehrer manuell bewertet werden, basieren auf zwei Methoden: Active Learning und Clustering. Beim Active-Learning-Ansatz werden besonders informative Antworten vorrangig zur Bewertung ausgewählt, d.h. solche Antworten, von denen ein Klassifikator besonders viel lernen kann. Wir identifizieren solche Antworten durch Uncertainty-Sampling- Methoden und erreichen dadurch mit einer gegebenen Anzahl von Annotationsschritten eine höhere Klassifikationsgenauigkeit als mit zufällig ausgewählten Antworten. In unserem zweiten Forschungszweig nutzen wir Clusteringmethoden um ähnliche Antworten zu gruppieren, so dass Gruppen von Antworten in einem Annotationsschritt bewertet werden können. Dadurch kann die Anzahl der insgesamt nötigen Bewertungsschritte drastisch reduziert werden. Beim Vergleich zwischen clusteringbasierten Bewertungsverfahren und klassischem überwachten maschinellen Lernen, bei dem menschliche Annotationen dazu genutzt werden, einen Klassifikator zu trainieren, erbringen überwachte maschinelle Lernverfahren immer noch eine höhere Bewertungsgenauigkeit. Demgegenüber bringen Cluster den Vorteil eines strukturierten Outputs mit sich. Wir sind jedoch in der Lage, einen Teil diese Genauigkeitslücke zu schließen, in dem wir überwachte Featureauswahl und halbüberwachtes Clustering anwenden. In einer zusätzlichen Studie untersuchen wir die automatische Verarbeitung von Lernersprache im Hinblick auf die Performanz vonWerkzeugen für dasWortarten-Tagging. Wir annotieren ein deutsches Leseverstehenskorpus manuell sowohl mit Normalisierungsinformation in Bezug auf Rechtschreibung als auch mit Wortartinformation. Als Ergebnis der Studie finden wir, dass die Performanz bei der automatischen Wortartenzuweisung durch Rechtschreibkorrektur verbessert werden kann, insbesondere wenn wir den Lesetext als zusätzliche Evidenz dafür verwenden, welche Wörter der Leser in einer Antwort vermutlich benutzen wollte

A Frame-Based NLP System for Cancer-Related Information Extraction.

We propose a frame-based natural language processing (NLP) method that extracts cancer-related information from clinical narratives. We focus on three frames: cancer diagnosis, cancer therapeutic procedure, and tumor description. We utilize a deep learning-based approach, bidirectional Long Short-term Memory (LSTM) Conditional Random Field (CRF), which uses both character and word embeddings. The system consists of two constituent sequence classifiers: a frame identification (lexical unit) classifier and a frame element classifier. The classifier achieves an