Identification of the certainty of events is an important text mining problem. In particular, biomedical texts report medical conditions or findings that might be factual, hedged or negated. Identification of negation...
详细信息
We propose a deep neural network basednaturallanguageprocessing system for clinical information (such as time information, event spans, and their attributes) extraction from raw clinical notes and pathology reports...
详细信息
We propose a new application of quantum computing to the field of naturallanguageprocessing. Ongoing work in this field attempts to incorporate grammatical structure into algorithms that compute meaning. In [5, 7], ...
详细信息
We propose a new application of quantum computing to the field of naturallanguageprocessing. Ongoing work in this field attempts to incorporate grammatical structure into algorithms that compute meaning. In [5, 7], Coecke, Sadrzadeh and Clark introduce such a model (the CSC model) based on tensor product composition. While this algorithm has many advantages, its implementation is hampered by the large classical computational resources that it requires. In this work we show how computational shortcomings of the CSC approach could be resolved using quantum computation (possibly in addition to existing techniques for dimension reduction). We address the value of quantum RAM [8] for this model and extend an algorithm from Wiebe, Braun and Lloyd [24] into a quantum algorithm to categorize sentences in CSC. Our new algorithm demonstrates a quadratic speedup over classical methods under certain conditions.
Relation classification is an important semantic processing task in the field of naturallanguageprocessing (NLP). State-of-the-art systems still rely on lexical resources such as WordNet or NLP systems like dependen...
详细信息
ISBN:
(纸本)9781945626012
Relation classification is an important semantic processing task in the field of naturallanguageprocessing (NLP). State-of-the-art systems still rely on lexical resources such as WordNet or NLP systems like dependency parser and named entity recognizers (NER) to get high-level features. Another challenge is that important information can appear at any position in the sentence. To tackle these problems, we propose Attention-based Bidirectional Long Short-Term Memory Networks(Att-BLSTM) to capture the most important semantic information in a sentence. The experimental results on the SemEval-2010 relation classification task show that our method outperforms most of the existing methods, with only word vectors.
In this paper, we describe the details of our methods for the participation in the subtask of the ImageCLEF 2016 Scalable Image Annotation task: naturallanguage Caption Generation. The model we used is the combinatio...
详细信息
In this paper, we describe the details of our methods for the participation in the subtask of the ImageCLEF 2016 Scalable Image Annotation task: naturallanguage Caption Generation. The model we used is the combination of a procedure of encoding and a procedure of decoding, which includes a Convolutional neural network(CNN) and a Long Short-Term Memory(LSTM) based Recurrent Neural Network. We first train a model on the MSCOCO dataset and then fine tune the model on difierent target datasets collected by us to get a more suitable model for the naturallanguage caption generation task. Both of the parameters of CNN and LSTM are learned together.
We believe that Deep Learning is one of the next big things in Recommendation Systems technology. The past few years have seen the tremendous success of deep neural networks in a number of complex tasks such as comput...
详细信息
ISBN:
(纸本)9781450340359
We believe that Deep Learning is one of the next big things in Recommendation Systems technology. The past few years have seen the tremendous success of deep neural networks in a number of complex tasks such as computer vision, naturallanguageprocessing and speech recognition. Despite this, only little work has been published on Deep Learning methods for Recommender Systems. Notable recent application areas are music recommendation, news recommendation, and session-based recommendation. The aim of the workshop is to encourage the application of Deep Learning techniques in Recommender Systems, to promote research in deep learning methods for Recommender Systems, and to bring together researchers from the Recommender Systems and Deep Learning communities.
The proceedings contain 11 papers. The topics discussed include: event-centered information retrieval using kernels on event graphs;reconstructing big semantic similarity networks;graph-based unsupervised learning of ...
ISBN:
(纸本)9781937284978
The proceedings contain 11 papers. The topics discussed include: event-centered information retrieval using kernels on event graphs;reconstructing big semantic similarity networks;graph-based unsupervised learning of word similarities using heterogeneous feature types;understanding seed selection in bootstrapping;graph-structures matching for review relevance identi?cation;automatic extraction of reasoning chains from textual reports;graph-based Approaches for organization entity resolution in MapReduce;and a graph-based approach to skill extraction from text.
Extractive summarization of text documents usually consists of ranking the document sentences and extracting the top-ranked sentences subject to the summary length constraints. In this paper, we explore the contributi...
详细信息
Expressive but complex kernel functions, such as Sequence or Tree kernels, are usually underemployed in NLP tasks, e.g., in community Question Answering (cQA), as for their significant complexity in both learning and ...
详细信息
Expressive but complex kernel functions, such as Sequence or Tree kernels, are usually underemployed in NLP tasks, e.g., in community Question Answering (cQA), as for their significant complexity in both learning and classification stages. Recently, the Nystrom methodology for data embedding has been proposed as a viable solution to scalability problems. By mapping data into low-dimensional approximations of kernel spaces, it positively increases scalability through compact linear representations for highly structured data. In this paper, we show that Nystrom methodology can be effectively used to apply a kernel-based method in the cQA task, achieving stateof- The-art results by reducing the computational cost of orders of magnitude.
Multimodal information fusion both at the signal and the semantics levels is a core part in most multimedia applications, including multimedia indexing, retrieval, summarization and others. Early or late fusion of mod...
详细信息
ISBN:
(纸本)9781450336031
Multimodal information fusion both at the signal and the semantics levels is a core part in most multimedia applications, including multimedia indexing, retrieval, summarization and others. Early or late fusion of modality-specific processing results has been addressed in multimedia prototypes since their very early days, through various methodologies including rule-based approaches, information-theoretic models and machine learning. Vision and language are two of the predominant modalities that are being fused and which have attracted special attention in international challenges with a long history of results, such as TRECVid, ImageClef and others. During the last decade, vision-language semantic integration has attracted attention from traditionally non-interdisciplinary research communities, such as Computer Vision and naturallanguageprocessing. This is due to the fact that one modality can greatly assist the processing of another providing cues for disambiguation, complementary information and noise/error filtering. The latest boom of deep learning methods has opened up new directions in joint modelling of visual and co-occurring verbal information in multimedia discourse. The workshop on Vision and language Integration Meets Multimedia Fusion has been held during the workshop weekend of the ACM Multimedia 2016 Conference and the European Conference on Computer Vision (ECCV 2016) on October 16, 2016 in Amsterdam, the Netherlands. The proceedings contain seven selected long papers, which have been orally presented at the workshop, and three abstracts of the invited keynote speeches. The papers and abstracts discuss data collection, representation learning, deep learning approaches, matrix and tensor factorization methods and graphbased clustering with regard to the fusion of multimedia data. A variety of applications is presented including image captioning, summarization of news, video hyperlinking, sub-shot segmentation of user generated video, cross-modal c
暂无评论