772 research outputs found
Self-Supervised and Controlled Multi-Document Opinion Summarization
We address the problem of unsupervised abstractive summarization of
collections of user generated reviews with self-supervision and control. We
propose a self-supervised setup that considers an individual document as a
target summary for a set of similar documents. This setting makes training
simpler than previous approaches by relying only on standard log-likelihood
loss. We address the problem of hallucinations through the use of control
codes, to steer the generation towards more coherent and relevant
summaries.Finally, we extend the Transformer architecture to allow for multiple
reviews as input. Our benchmarks on two datasets against graph-based and recent
neural abstractive unsupervised models show that our proposed method generates
summaries with a superior quality and relevance.This is confirmed in our human
evaluation which focuses explicitly on the faithfulness of generated summaries
We also provide an ablation study, which shows the importance of the control
setup in controlling hallucinations and achieve high sentiment and topic
alignment of the summaries with the input reviews.Comment: 18 pages including 5 pages appendi
Stance detection on social media: State of the art and trends
Stance detection on social media is an emerging opinion mining paradigm for
various social and political applications in which sentiment analysis may be
sub-optimal. There has been a growing research interest for developing
effective methods for stance detection methods varying among multiple
communities including natural language processing, web science, and social
computing. This paper surveys the work on stance detection within those
communities and situates its usage within current opinion mining techniques in
social media. It presents an exhaustive review of stance detection techniques
on social media, including the task definition, different types of targets in
stance detection, features set used, and various machine learning approaches
applied. The survey reports state-of-the-art results on the existing benchmark
datasets on stance detection, and discusses the most effective approaches. In
addition, this study explores the emerging trends and different applications of
stance detection on social media. The study concludes by discussing the gaps in
the current existing research and highlights the possible future directions for
stance detection on social media.Comment: We request withdrawal of this article sincerely. We will re-edit this
paper. Please withdraw this article before we finish the new versio
Sentimental Analysis and Its Applications - A Review
Sentimental Analysis over the years has received a lot of recognition as it has shown a tremendous growth. Nowadays Sentimental analysis can be applied to any field as to bring out the emotions attached to it and also we can be able to know what the other person wants to convey. In our work we will be applying the sentimental analysis on the dataset of 15 hotels from the city and apply the new technique of Statistical Analysis. The Statistical Analysis Technique has taken the attributes like Food Quality, Ambience, area of Location etc into the account for calculating Document Index. The Reviews are taken from a trusted website. It is observed that people before making any decision do visit the reviews before making out the further decisions so e-WOM is the most effective to way to convey the views .Nowadays people rely a lot on electronic form of reviews because it is considered as the most trusted way that people can convey the views
PersoNER: Persian named-entity recognition
© 1963-2018 ACL. Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network
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History Modeling for Conversational Information Retrieval
Conversational search is an embodiment of an iterative and interactive approach to information retrieval (IR) that has been studied for decades. Due to the recent rise of intelligent personal assistants, such as Siri, Alexa, AliMe, Cortana, and Google Assistant, a growing part of the population is moving their information-seeking activities to voice- or text-based conversational interfaces. One of the major challenges of conversational search is to leverage the conversation history to understand and fulfill the users\u27 information needs. In this dissertation work, we investigate history modeling approaches for conversational information retrieval. We start from history modeling for user intent prediction. We analyze information-seeking conversations by user intent distribution, co-occurrence, and flow patterns, followed by a study of user intent prediction in an information-seeking setting with both feature-based methods and deep learning methods. We then move to history modeling for conversational question answering (ConvQA), which can be considered as a simplified setting of conversational search. We first propose a positional history answer embedding (PosHAE) method to seamlessly integrate conversation history into a ConvQA model based on BERT. We then build upon this method and design a history attention mechanism (HAM) to conduct a ``soft selection\u27\u27 for conversation history. After this, we extend the previous ConvQA task to an open-retrieval (ORConvQA) setting to emphasize the fundamental role of retrieval in conversational search. In this setting, we learn to retrieve evidence from a large collection before extracting answers. We build an end-to-end system for ORConvQA, featuring a learnable dense retriever. We conduct experiments with both fully-supervised and weakly-supervised approaches to tackle the training challenges of ORConvQA. Finally, we study history modeling for conversational re-ranking. Given a history of user feedback behaviors, such as issuing a query, clicking a document, and skipping a document, we propose to introduce behavior awareness to a neural ranker. Our experimental results show that the history modeling approaches proposed in this dissertation can effectively improve the performance of different conversation tasks and provide new insights into conversational information retrieval
Unveiling the frontiers of deep learning: innovations shaping diverse domains
Deep learning (DL) enables the development of computer models that are
capable of learning, visualizing, optimizing, refining, and predicting data. In
recent years, DL has been applied in a range of fields, including audio-visual
data processing, agriculture, transportation prediction, natural language,
biomedicine, disaster management, bioinformatics, drug design, genomics, face
recognition, and ecology. To explore the current state of deep learning, it is
necessary to investigate the latest developments and applications of deep
learning in these disciplines. However, the literature is lacking in exploring
the applications of deep learning in all potential sectors. This paper thus
extensively investigates the potential applications of deep learning across all
major fields of study as well as the associated benefits and challenges. As
evidenced in the literature, DL exhibits accuracy in prediction and analysis,
makes it a powerful computational tool, and has the ability to articulate
itself and optimize, making it effective in processing data with no prior
training. Given its independence from training data, deep learning necessitates
massive amounts of data for effective analysis and processing, much like data
volume. To handle the challenge of compiling huge amounts of medical,
scientific, healthcare, and environmental data for use in deep learning, gated
architectures like LSTMs and GRUs can be utilized. For multimodal learning,
shared neurons in the neural network for all activities and specialized neurons
for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table
Machine learning with limited label availability: algorithms and applications
L'abstract è presente nell'allegato / the abstract is in the attachmen
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