Neural networks with attention have proven effective for many natural language processing tasks. In this paper, we develop attention mechanisms for uncertainty detection. In particular, we generalize standardly used attention mechanisms by introducing external attention and sequence-preserving attention. These novel architectures differ from standard approaches in that they use external resources to compute attention weights and preserve sequence information. We compare them to other configurations along different dimensions of attention. Our novel architectures set the new state of the art on a Wikipedia benchmark dataset and perform similar to the state-of-the-art model on a biomedical benchmark which uses a large set of linguistic features.
Uncertainty has been extensively studied in linguistics and NLP @cite_25 @cite_19 @cite_37 , including modality @cite_15 @cite_4 @cite_20 and negation @cite_30 @cite_43 . , and conducted cross domain experiments. Domains studied include news @cite_11 , biomedicine @cite_34 , Wikipedia @cite_31 and social media @cite_28 . Corpora such as FactBank @cite_11 are annotated in detail with respect to perspective, level of factuality and polarity. conducted uncertainty detection experiments on a version of FactBank extended by crowd sourcing. In this work, we use CoNLL 2010 shared task data @cite_10 since CoNLL provides larger train test sets and the CoNLL annotation consists of only two labels (certain uncertain) instead of various perspectives and degrees of uncertainty. When using uncertainty detection for information extraction tasks like KB population ( intro ), it is a reasonable first step to consider only two labels.
@cite_30 @cite_37 @cite_31 @cite_4 @cite_28 @cite_19 @cite_43 @cite_15 @cite_34 @cite_10 @cite_25 @cite_20 @cite_11
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Uncertainty is an important linguistic phenomenon that is relevant in many fields of language processing. In its most general sense, it can be interpreted as lack of information: the hearer or the reader cannot be certain about some pieces of information. Thus, uncertain propositions are those whose truth value or reliability cannot be determined due to lack of information. Distinguishing between factual (i.e. true or false) and uncertain propositions is of primary importance both in linguistics and natural language processing applications. For instance, in information extraction an uncertain piece of information might be of some interest for an end-user as well, but such information must not be confused with factual textual evidence (reliable information) and the two should be kept separated. The main objective of this thesis is to detect uncertainty in English and Hungarian natural language texts. As opposed to earlier studies that focused on specific domains and were English-oriented, we will offer here a comprehensive approach to uncertainty detection, which can be easily adapted to the specific needs of many domains and languages. In our investigations, we will pay attention to create linguistically plausible models of uncertainty that will be exploited in creating manually annotated corpora that will serve as the base for the implementation of our uncertainty detectors for several domains, with the help of supervised machine learning techniques. Furthermore, we will also demonstrate that uncertainty detection can be fruitfully applied in a real-world application, namely, information extraction from clinical discharge summaries. Uncertainty detection is essential for many NLP applications. For instance, in information retrieval, it is of primary importance to distinguish among factual, negated and uncertain information. Current research on uncertainty detection has mostly focused on the English language, in contrast, here we present the first machine learning algorithm that aims at identifying linguistic markers of uncertainty in Hungarian texts from two domains: Wikipedia and news media. The system is based on sequence labeling and makes use of a rich feature set including orthographic, lexical, morphological, syntactic and semantic features as well. Having access to annotated data from two domains, we also focus on the domain specificities of uncertainty detection by comparing results obtained in indomain and cross-domain settings. Our results show that the domain of the text has significant influence on uncertainty detection. The CoNLL-2010 Shared Task was dedicated to the detection of uncertainty cues and their linguistic scope in natural language texts. The motivation behind this task was that distinguishing factual and uncertain information in texts is of essential importance in information extraction. This paper provides a general overview of the shared task, including the annotation protocols of the training and evaluation datasets, the exact task definitions, the evaluation metrics employed and the overall results. The paper concludes with an analysis of the prominent approaches and an overview of the systems submitted to the shared task. This article explores a combination of deep and shallow approaches to the problem of resolving the scope of speculation and negation within a sentence, specifically in the domain of biomedical research literature. The first part of the article focuses on speculation. After first showing how speculation cues can be accurately identified using a very simple classifier informed only by local lexical context, we go on to explore two different syntactic approaches to resolving the in-sentence scopes of these cues. Whereas one uses manually crafted rules operating over dependency structures, the other automatically learns a discriminative ranking function over nodes in constituent trees. We provide an in-depth error analysis and discussion of various linguistic properties characterizing the problem, and show that although both approaches perform well in isolation, even better results can be obtained by combining them, yielding the best published results to date on the CoNLL-2010 Shared Task data. The last part of the article describes how our speculation system is ported to also resolve the scope of negation. With only modest modifications to the initial design, the system obtains state-of-the-art results on this task also.