In no uncertain terms : a dataset for monolingual and multilingual automatic term extraction from comparable corpora

Automatic term extraction is a productive field of research within natural language processing, but it still faces significant obstacles regarding datasets and evaluation, which require manual term annotation. This is an arduous task, made even more difficult by the lack of a clear distinction between terms and general language, which results in low inter-annotator agreement. There is a large need for well-documented, manually validated datasets, especially in the rising field of multilingual term extraction from comparable corpora, which presents a unique new set of challenges. In this paper, a new approach is presented for both monolingual and multilingual term annotation in comparable corpora. The detailed guidelines with different term labels, the domain- and language-independent methodology and the large volumes annotated in three different languages and four different domains make this a rich resource. The resulting datasets are not just suited for evaluation purposes but can also serve as a general source of information about terms and even as training data for supervised methods. Moreover, the gold standard for multilingual term extraction from comparable corpora contains information about term variants and translation equivalents, which allows an in-depth, nuanced evaluation

Scientific Information Extraction with Semi-supervised Neural Tagging

This paper addresses the problem of extracting keyphrases from scientific articles and categorizing them as corresponding to a task, process, or material. We cast the problem as sequence tagging and introduce semi-supervised methods to a neural tagging model, which builds on recent advances in named entity recognition. Since annotated training data is scarce in this domain, we introduce a graph-based semi-supervised algorithm together with a data selection scheme to leverage unannotated articles. Both inductive and transductive semi-supervised learning strategies outperform state-of-the-art information extraction performance on the 2017 SemEval Task 10 ScienceIE task.Comment: accepted by EMNLP 201

Extracting Temporal and Causal Relations between Events

Structured information resulting from temporal information processing is crucial for a variety of natural language processing tasks, for instance to generate timeline summarization of events from news documents, or to answer temporal/causal-related questions about some events. In this thesis we present a framework for an integrated temporal and causal relation extraction system. We first develop a robust extraction component for each type of relations, i.e. temporal order and causality. We then combine the two extraction components into an integrated relation extraction system, CATENA---CAusal and Temporal relation Extraction from NAtural language texts---, by utilizing the presumption about event precedence in causality, that causing events must happened BEFORE resulting events. Several resources and techniques to improve our relation extraction systems are also discussed, including word embeddings and training data expansion. Finally, we report our adaptation efforts of temporal information processing for languages other than English, namely Italian and Indonesian.Comment: PhD Thesi

Automatic Recognition and Classification of Future Work Sentences from Academic Articles in a Specific Domain

Future work sentences (FWS) are the particular sentences in academic papers that contain the author's description of their proposed follow-up research direction. This paper presents methods to automatically extract FWS from academic papers and classify them according to the different future directions embodied in the paper's content. FWS recognition methods will enable subsequent researchers to locate future work sentences more accurately and quickly and reduce the time and cost of acquiring the corpus. The current work on automatic identification of future work sentences is relatively small, and the existing research cannot accurately identify FWS from academic papers, and thus cannot conduct data mining on a large scale. Furthermore, there are many aspects to the content of future work, and the subdivision of the content is conducive to the analysis of specific development directions. In this paper, Nature Language Processing (NLP) is used as a case study, and FWS are extracted from academic papers and classified into different types. We manually build an annotated corpus with six different types of FWS. Then, automatic recognition and classification of FWS are implemented using machine learning models, and the performance of these models is compared based on the evaluation metrics. The results show that the Bernoulli Bayesian model has the best performance in the automatic recognition task, with the Macro F1 reaching 90.73%, and the SCIBERT model has the best performance in the automatic classification task, with the weighted average F1 reaching 72.63%. Finally, we extract keywords from FWS and gain a deep understanding of the key content described in FWS, and we also demonstrate that content determination in FWS will be reflected in the subsequent research work by measuring the similarity between future work sentences and the abstracts

SemEval-2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers

International audienceThis paper describes the first task on semantic relation extraction and classification in scientific paper abstracts at SemEval 2018. The challenge focuses on domain-specific semantic relations and includes three different sub-tasks. The subtasks were designed so as to compare and quantify the effect of different pre-processing steps on the relation classification results. We expect the task to be relevant for a broad range of researchers working on extracting specialized knowledge from domain corpora, for example but not limited to scientific or bio-medical information extraction. The task attracted a total of 32 participants, with 158 submissions across different scenarios

Text Summarization Across High and Low-Resource Settings

Natural language processing aims to build automated systems that can both understand and generate natural language textual data. As the amount of textual data available online has increased exponentially, so has the need for intelligence systems to comprehend and present it to the world. As a result, automatic text summarization, the process by which a text\u27s salient content is automatically distilled into a concise form, has become a necessary tool. Automatic text summarization approaches and applications vary based on the input summarized, which may constitute single or multiple documents of different genres. Furthermore, the desired output style may consist of a sentence or sub-sentential units chosen directly from the input in extractive summarization or a fusion and paraphrase of the input document in abstractive summarization. Despite differences in the above use-cases, specific themes, such as the role of large-scale data for training these models, the application of summarization models in real-world scenarios, and the need for adequately evaluating and comparing summaries, are common across these settings. This dissertation presents novel data and modeling techniques for deep neural network-based summarization models trained across high-resource (thousands of supervised training examples) and low-resource (zero to hundreds of supervised training examples) data settings and a comprehensive evaluation of the model and metric progress in the field. We examine both Recurrent Neural Network (RNN)-based and Transformer-based models to extract and generate summaries from the input. To facilitate the training of large-scale networks, we introduce datasets applicable for multi-document summarization (MDS) for pedagogical applications and for news summarization. While the high-resource settings allow models to advance state-of-the-art performance, the failure of such models to adapt to settings outside of that in which it was initially trained requires smarter use of labeled data and motivates work in low-resource summarization. To this end, we propose unsupervised learning techniques for both extractive summarization in question answering, abstractive summarization on distantly-supervised data for summarization of community question answering forums, and abstractive zero and few-shot summarization across several domains. To measure the progress made along these axes, we revisit the evaluation of current summarization models. In particular, this dissertation addresses the following research objectives: 1) High-resource Summarization. We introduce datasets for multi-document summarization, focusing on pedagogical applications for NLP, news summarization, and Wikipedia topic summarization. Large-scale datasets allow models to achieve state-of-the-art performance on these tasks compared to prior modeling techniques, and we introduce a novel model to reduce redundancy. However, we also examine how models trained on these large-scale datasets fare when applied to new settings, showing the need for more generalizable models. 2) Low-resource Summarization. While high-resource summarization improves model performance, for practical applications, data-efficient models are necessary. We propose a pipeline for creating synthetic training data for training extractive question-answering models, a form of query-based extractive summarization with short-phrase summaries. In other work, we propose an automatic pipeline for training a multi-document summarizer in answer summarization on community question-answering forums without labeled data. Finally, we push the boundaries of abstractive summarization model performance when little or no training data is available across several domains. 3) Automatic Summarization Evaluation. To understand the extent of progress made across recent modeling techniques and better understand the current evaluation protocols, we examine the current metrics used to compare summarization output quality across 12 metrics across 23 deep neural network models and propose better-motivated summarization evaluation guidelines as well as point to open problems in summarization evaluation