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Automatic summarization systems usually are trained and evaluated in a particular domain with fixed data sets. When such a system is to be applied to slightly different input, labor- and cost-intensive annotations have to be created to retrain the system. We deal with this problem by providing users with a GUI which allows them to correct automatically produced imperfect summaries. The corrected summary in turn is added to the pool of training data. The performance of the system is expected to improve as it adapts to the new domain.
Content
1 Predicting learner knowledge of individual words using machine learning
Drilon Avdiu, Vanessa Bui, Klára Ptacinová Klimci´ková
2 Automatic Generation and Semantic Grading of Esperanto Sentences in a Teaching Context
Eckhard Bick
3 Toward automatic improvement of language produced by non-native language learners
Mathias Creutz, Eetu Sjöblom
4 Linguistic features and proficiency classification in L2 Spanish and L2 Portuguese
Iria del Ri´o
5 Integrating large-scale web data and curated corpus data in a search engine supporting German literacy education
Sabrina Dittrich, Zarah Weiss, Hannes Schröter, Detmar Meurers
6 Formalism for a language agnostic language learning game and productive grid generation
Sylvain Hatier, Arnaud Bey, Mathieu Loiseau
7 Understanding Vocabulary Growth Through An Adaptive Language Learning System
Elma Kerz, Andreas Burgdorf, Daniel Wiechmann, Stefan Meeger,Yu Qiao, Christian Kohlschein, Tobias Meisen
8 Summarization Evaluation meets Short-Answer Grading
Margot Mieskes, Ulrike Padó
9 Experiments on Non-native Speech Assessment and its Consistency
Ziwei Zhou, Sowmya Vajjala, Seyed Vahid Mirnezami
10 The Impact of Spelling Correction and Task Context on Short Answer Assessment for Intelligent Tutoring Systems
Ramon Ziai, Florian Nuxoll, Kordula De Kuthy, Björn Rudzewitz, Detmar Meurers
Current Natural Language Processing (NLP) systems feature high-complexity processing pipelines that require the use of components at different levels of linguistic and application specific processing. These components often have to interface with external e.g. machine learning and information retrieval libraries as well as tools for human annotation and visualization. At the UKP Lab, we are working on the Darmstadt Knowledge Processing Software Repository (DKPro) (Gurevych et al., 2007a; Müller et al., 2008) to create a highly flexible, scalable and easy-to-use toolkit that allows rapid creation of complex NLP pipelines for semantic information processing on demand. The DKPro repository consists of several main parts created to serve the purposes of different NLP application areas
We apply a decision tree based approach to pronoun resolution in spoken dialogue. Our system deals with pronouns with NP- and non-NP-antecedents. We present a set of features designed for pronoun resolution in spoken dialogue and determine the most promising features. We evaluate the system on twenty Switchboard dialogues and show that it compares well to Byron’s (2002) manually tuned system.
We present a supervised machine learning AND system which tackles semantic similarity between publication titles by means of word embeddings. Word embeddings are integrated as external components, which keeps the model small and efficient, while allowing for easy extensibility and domain adaptation. Initial experiments show that word embeddings can improve the Recall and F score of the binary classification sub-task of AND. Results for the clustering sub-task are less clear, but also promising and overall show the feasibility of the approach.
We present an implemented machine learning system for the automatic detection of nonreferential it in spoken dialog. The system builds on shallow features extracted from dialog transcripts. Our experiments indicate a level of performance that makes the system usable as a preprocessing filter for a coreference resolution system. We also report results of an annotation study dealing with the classification of it by naive subjects.
The sentiment polarity of an expression (whether it is perceived as positive, negative or neutral) can be influenced by a number of phenomena, foremost among them negation. Apart from closed-class negation words like no, not or without, negation can also be caused by so-called polarity shifters. These are content words, such as verbs, nouns or adjectives, that shift polarities in their opposite direction, e. g. abandoned in “abandoned hope” or alleviate in “alleviate pain”. Many polarity shifters can affect both positive and negative polar expressions, shifting them towards the opposing polarity. However, other shifters are restricted to a single shifting direction. Recoup shifts negative to positive in “recoup your losses”, but does not affect the positive polarity of fortune in “recoup a fortune”. Existing polarity shifter lexica only specify whether a word can, in general, cause shifting, but they do not specify when this is limited to one shifting direction. To address this issue we introduce a supervised classifier that determines the shifting direction of shifters. This classifier uses both resource-driven features, such as WordNet relations, and data-driven features like in-context polarity conflicts. Using this classifier we enhance the largest available polarity shifter lexicon.
This paper presents experiments on sentence boundary detection in transcripts of spoken dialogues. Segmenting spoken language into sentence-like units is a challenging task, due to disfluencies, ungrammatical or fragmented structures and the lack of punctuation. In addition, one of the main bottlenecks for many NLP applications for spoken language is the small size of the training data, as the transcription and annotation of spoken language is by far more time-consuming and labour-intensive than processing written language. We therefore investigate the benefits of data expansion and transfer learning and test different ML architectures for this task. Our results show that data expansion is not straightforward and even data from the same domain does not always improve results. They also highlight the importance of modelling, i.e. of finding the best architecture and data representation for the task at hand. For the detection of boundaries in spoken language transcripts, we achieve a substantial improvement when framing the boundary detection problem as a sentence pair classification task, as compared to a sequence tagging approach.
Beyond Citations: Corpus-based Methods for Detecting the Impact of Research Outcomes on Society
(2020)
This paper proposes, implements and evaluates a novel, corpus-based approach for identifying categories indicative of the impact of research via a deductive (top-down, from theory to data) and an inductive (bottom-up, from data to theory) approach. The resulting categorization schemes differ in substance. Research outcomes are typically assessed by using bibliometric methods, such as citation counts and patterns, or alternative metrics, such as references to research in the media. Shortcomings with these methods are their inability to identify impact of research beyond academia (bibliometrics) and considering text-based impact indicators beyond those that capture attention (altmetrics). We address these limitations by leveraging a mixed-methods approach for eliciting impact categories from experts, project personnel (deductive) and texts (inductive). Using these categories, we label a corpus of project reports per category schema, and apply supervised machine learning to infer these categories from project reports. The classification results show that we can predict deductively and inductively derived impact categories with 76.39% and 78.81% accuracy (F1-score), respectively. Our approach can complement solutions from bibliometrics and scientometrics for assessing the impact of research and studying the scope and types of advancements transferred from academia to society.