725 research outputs found
ALens: An Adaptive Domain-Oriented Abstract Writing Training Tool for Novice Researchers
The significance of novice researchers acquiring proficiency in writing
abstracts has been extensively documented in the field of higher education,
where they often encounter challenges in this process. Traditionally, students
have been advised to enroll in writing training courses as a means to develop
their abstract writing skills. Nevertheless, this approach frequently falls
short in providing students with personalized and adaptable feedback on their
abstract writing. To address this gap, we initially conducted a formative study
to ascertain the user requirements for an abstract writing training tool.
Subsequently, we proposed a domain-specific abstract writing training tool
called ALens, which employs rhetorical structure parsing to identify key
concepts, evaluates abstract drafts based on linguistic features, and employs
visualization techniques to analyze the writing patterns of exemplary
abstracts. A comparative user study involving an alternative abstract writing
training tool has been conducted to demonstrate the efficacy of our approach.Comment: Accepted by HHME/CHCI 202
Argumentative zoning information extraction from scientific text
Let me tell you, writing a thesis is not always a barrel of laughsâand strange things can happen, too. For example, at the height of my thesis paranoia, I had a re-current dream in which my cat Amy gave me detailed advice on how to restructure the thesis chapters, which was awfully nice of her. But I also had a lot of human help throughout this time, whether things were going fine or beserk. Most of all, I want to thank Marc Moens: I could not have had a better or more knowledgable supervisor. He always took time for me, however busy he might have been, reading chapters thoroughly in two days. He both had the calmness of mind to give me lots of freedom in research, and the right judgement to guide me away, tactfully but determinedly, from the occasional catastrophe or other waiting along the way. He was great fun to work with and also became a good friend. My work has profitted from the interdisciplinary, interactive and enlightened atmosphere at the Human Communication Centre and the Centre for Cognitive Science (which is now called something else). The Language Technology Group was a great place to work in, as my research was grounded in practical applications develope
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
Individual and Domain Adaptation in Sentence Planning for Dialogue
One of the biggest challenges in the development and deployment of spoken
dialogue systems is the design of the spoken language generation module. This
challenge arises from the need for the generator to adapt to many features of
the dialogue domain, user population, and dialogue context. A promising
approach is trainable generation, which uses general-purpose linguistic
knowledge that is automatically adapted to the features of interest, such as
the application domain, individual user, or user group. In this paper we
present and evaluate a trainable sentence planner for providing restaurant
information in the MATCH dialogue system. We show that trainable sentence
planning can produce complex information presentations whose quality is
comparable to the output of a template-based generator tuned to this domain. We
also show that our method easily supports adapting the sentence planner to
individuals, and that the individualized sentence planners generally perform
better than models trained and tested on a population of individuals. Previous
work has documented and utilized individual preferences for content selection,
but to our knowledge, these results provide the first demonstration of
individual preferences for sentence planning operations, affecting the content
order, discourse structure and sentence structure of system responses. Finally,
we evaluate the contribution of different feature sets, and show that, in our
application, n-gram features often do as well as features based on higher-level
linguistic representations
An improved method for text summarization using lexical chains
This work is directed toward the creation of a system for automatically sum-marizing documents by extracting selected sentences. Several heuristics including position, cue words, and title words are used in conjunction with lexical chain in-formation to create a salience function that is used to rank sentences for extraction. Compiler technology, including the Flex and Bison tools, is used to create the AutoExtract summarizer that extracts and combines this information from the raw text. The WordNet database is used for the creation of the lexical chains. The AutoExtract summarizer performed better than the Microsoft Word97 AutoSummarize tool and the Sinope commercial summarizer in tests against ideal extracts and in tests judged by humans
NLP Driven Models for Automatically Generating Survey Articles for Scientific Topics.
This thesis presents new methods that use natural language processing (NLP) driven models for summarizing research in scientific fields. Given a topic query in the form of a text string, we present methods for finding research articles relevant to the topic as well as summarization algorithms that use lexical and discourse information present in the text of these articles to generate coherent and readable extractive summaries of past research on the topic. In addition to summarizing prior research, good survey articles should also forecast future trends. With this motivation, we present work on forecasting future impact of scientific publications using NLP driven features.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113407/1/rahuljha_1.pd
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