However , at the same time , they will unexpectedly damage the representative ability of the learned deep features to some extent . Therefore , we propose a unified Bilateral-Branch Network ( BBN ) to take care of both representation learning and classifier learning simultaneously , where each branch does perform its own duty separately . In particular , our BBN model is further equipped with a novel cumulative learning strategy , which is designed to first learn the universal patterns and then pay attention to the tail data gradually . In NMT , the source embedding plays the role of the entrance while the target embedding acts as the terminal . These layers occupy most of the model parameters for representation learning . Furthermore , they indirectly interface via a soft-attention mechanism , which makes them comparatively isolated . We focus on metric learning due to the importance of computing distances between examples in learning algorithms and present Jiffy , a simple and scalable distance metric learning method for multivariate time series . Our approach is to reframe the task as a representation learning problem — rather than design an elaborate distance function , we use a CNN to learn an embedding such that the Euclidean distance is effective . Experiments on a diverse set of multivariate time series datasets show that our approach consistently outperforms existing methods . subsection : Conceptual Compression Variational auto - encoders can not only be used for representation learning but also for compression . The training objective of variational auto - encoders is to compress the total amount of information needed to encode the input . Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms . Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves . However , present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks . Research on sentence compression has been undergoing for many years in other languages , especially in English , but research on Chinese sentence compression is rarely found . In this paper , we describe an efficient probabilistic and syntactic approach to Chinese sentence compression . Distant supervision for neural relation extraction is an efficient approach to extracting massive relations with reference to plain texts . However , the existing neural methods fail to capture the critical words in sentence encoding and meanwhile lack useful sentence information for some positive training instances . To address the above issues , we propose a novel neural relation extraction model . We hypothesized a chunking‐related modulation of neural processing during the encoding and retention of sentences ( i.e. , sentence fragments ) as compared to word lists . Time – frequency analysis revealed a two‐fold oscillatory pattern for the memorization of sentences : Sentence encoding was accompanied by higher delta amplitude ( 4 Hz ) , originating both from regions processing syntax as well as semantics ( bilateral superior/middle temporal regions and fusiform gyrus ) . Subsequent sentence retention was reflected in decreased theta ( 6 Hz ) and beta/gamma ( 27–32 Hz ) amplitude instead . These translation tasks are characterized by the relative ability to commit to parallel parse trees and availability of word alignments , yet the unavailability of large-scale data , calling for a Bayesian tree-to-tree formalism . We formalize nonparametric Bayesian STSG with epsilon alignment in full generality , and provide a Gibbs sampling algorithm for posterior inference tailored to the task of extractive sentence compression . We achieve improvements against a number of baselines , including expectation maximization and variational Bayes training , illustrating the merits of nonparametric inference over the space of grammars as opposed to sparse parametric inference with a fixed grammar . In this paper , we propose Operation Network , a neural network approach for abstractive sentence compression , which combines the advantages of both delete-based and generate-based sentence compression models . The central idea of Operation Network is to model the sentence compression process as an editing procedure . First , unnecessary words are deleted from the source sentence , then new words are either generated from a large vocabulary or copied directly from the source sentence . This paper is concerned with the use of statistical information about dependency path length for sentence compression The sentence compression method employed here requires a quantity called inter-phrase dependency strength In the training process , original sentences are parsed , and the number of tokens is counted for each pair of phrases , connected with each other by a dependency path of certain length , that survive as a modifier-modified phrase pair in the corresponding compressed sentence in the training corpus The statistics is exploited to estimate the inter-phrase dependency strength required in the sentence compression process Results of subjective evaluation shows that the present method outperforms the conventional one of the same framework where the distribution of dependency distance is used to estimate the inter-phrase dependency strength . Extractive and abstractive are two types of summarization . Extractive summary picks the sentences from document and combines them to form a summary and Abstractive summary generate summary with the help of sentence compression and fusion ) . Here we proposed a new system which is used to generate abstractive summary of multi-document text which is based on ontology . This study proposes a text summarization model that simultaneously performs sentence extraction and compression . We translate the text summarization task into a problem of extracting a set of dependency subtrees in the document cluster . Can textual data be compressed intelligently without losing accuracy in evaluating sentiment ? In this study , we propose a novel evolutionary compression algorithm , PARSEC ( PARts-of-Speech for sEntiment Compression ) , which makes use of Parts-of-Speech tags to compress text in a way that sacrifices minimal classification accuracy when used in conjunction with sentiment analysis algorithms . An analysis of PARSEC with eight commercial and non-commercial sentiment analysis algorithms on twelve English sentiment data sets reveals that accurate compression is possible with ( 0 % , 1.3 % , 3.3 % ) loss in sentiment classification accuracy for ( 20 % , 50 % , 75 % ) data compression with PARSEC using LingPipe , the most accurate of the sentiment algorithms . Using the Chinese corpora of four product domains , Sent_Comp significantly improves the performance of the aspect-based sentiment analysis . The features proposed for Sent_Comp , especially the potential semantic features , are useful for sentiment sentence compression . This model applied parameters of one Bi - LSTM to initialize the next Bi - LSTM to convey information , which shown better results than the model with a single Bi - LSTM . Besides LSTM , attention mechanisms could also be used to boost the effectiveness of sentence encoding . The model developed by Ghaeini et al . added self - attention to LSTM model , and achieved better performance . document : Globally Normalized Transition - Based Neural Networks We introduce a globally normalized transition - based neural network model that achieves state - of - the - art part - of - speech tagging , dependency parsing and sentence compression results . Our model is a simple feed - forward neural network that operates on a task - specific transition system , yet achieves comparable or better accuracies than recurrent models . Our results also significantly outperform the LSTM - based approaches of dyer - etAl:2015:ACL and BallesterosDS15 . subsection : Sentence Compression Our final structured prediction task is extractive sentence compression . paragraph : Data & Evaluation . Knowledge graph embedding is an essential problem in knowledge extraction . Recently , translation based embedding models ( e.g. , TransE ) have received increasingly attentions . Existing methods for knowledge graph embedding do not ensure the high-rank triples predicted by themselves to be as consistent as possible with the logical background which is made up of a knowledge graph and a logical theory . Users must take great effort to filter consistent triples before adding new triples to the knowledge graph . Knowledge embeddings are key ingredients of advanced questionanswering and recommender systems . Even though their predictions are accurate , they are rather hard to interpret by human users ; interpretability techniques are needed so as to provide meaningful human-friendly explanations for prediction generated by embeddings . As an efficient model for knowledge organization , the knowledge graph has been widely adopted in several fields , e.g. , biomedicine , sociology , and education . And there is a steady trend of learning embedding representations of knowledge graphs to facilitate knowledge graph construction and downstream tasks . In general , knowledge graph embedding techniques aim to learn vectorized representations which preserve the structural information of the graph . document : Hypernetwork Knowledge Graph Embeddings Knowledge graphs are large graph - structured databases of facts , which typically suffer from incompleteness . After splitting the skip-gram loss into multiple losses corresponding to different tasks , we propose a novel random-walk strategy to focus on the tasks with high loss values by considering the relative training ratio . Unlike previous random walk strategies , our proposed random-walk strategy generates training samples according to the relative training ratio among different tasks , which results in a balanced training for the node embedding . Our extensive experiments on node classification and recommendation demonstrate the superiority of BHIN2vec compared to the state-of-the-art methods . This procedure is a NP-hard question , therefore many previous algorithms just impose some constraints or use simple heuristic method to get the relative optimal result . Traditional researches almost focus on two independent stages : node embedding and link mapping . To address existing questions such as low acceptance ratio and high mapping time , we propose an efficient and fast embedding algorithm , named as AEF(An Efficient and Fast embedding algorithm ) , which is a two coordinated mapping stages algorithm . It is a central problem in many machine learning tasks ( e.g. , node classification , recommendation , community detection ) . The key problem in graph node embedding lies in how to define the dependence to neighbors . Existing approaches specify ( either explicitly or implicitly ) certain dependencies on neighbors , which may lead to loss of subtle but important structural information within the graph and other dependencies among neighbors . Mobile agent is one of the useful distributed technologies of reducing network load and latency because of its disconnected operations and high asynchrony . In this paper , a component-based mobile agent framework , called EmHUMAN , is designed and implemented for structural embedding into the devices showing different functions and resource constraints . It consists of 3 layers of components . However , most of the existing principles of neural graph embedding do not incorporate auxiliary information such as node content flexibly . In this paper we take a matrix factorization perspective of graph embedding which generalizes to structural embedding as well as content embedding in a natural way . For structure embedding , we validate that the matrix we construct and factorize preserves the high-order proximities of the graph . In this paper , we address the problem of building effective latent embeddings for nodes on every layer from multilayer graph data , and our approach falls in the latter category . Constructing Node Embeddings : At their core , node embedding approaches attempt to identify low-rank representations that can best represent the network topology . In order to combat this limitation , recent approaches , such as DeepWalk ( Perozzi , Al-Rfou , and Skiena 2014 ) and Node2Vec ( Grover and Leskovec 2016 ) , have resorted to a distributional hypothesis , popularly adopted in language modeling ( Harris 1954 ) , where co-occurrence of two nodes in short random walks implies a strong notion of semantic similarity . As a result , by extending highly scalable neural embedding techniques such as Word2Vec ( Mikolov et al. 2013 ) to the construction of node embeddings , one can obtain state-of-the-art results in community detection with single-layer graphs . Proposed Work : In this paper , we develop a novel scalable technique for obtaining deep node embeddings from multilayer graphs . Recent research on graph analysis has been focused on representation learning , of which the goal is to encode the network structures into distributed embedding vectors , so as to enable various downstream applications through off-the-shelf machine learning . However , existing methods mostly focus on node-level embedding , which is insufficient for subgraph analysis . Moreover , their leverage of network structures through path sampling or neighborhood preserving is implicit and coarse . However , most existing structural embedding approaches suffer from high computation and space cost or rely on heuristic feature engineering . Here we propose RiWalk , a flexible paradigm for learning structural node representations as it decouples the structural embedding problem into a role identification procedure and a network embedding procedure . Through role identification , rooted kernels with structural dependencies kept are built to better integrate network embedding methods . Structural Network Embedding and End-to-End Node Classification .. Complex networks are used as an abstraction for systems modeling in physics , biology , sociology , and other areas . Our main contributions can be summarised as follows : ( i ) An unsupervised complex articulated kinematic structure estimation method that combines motion segments with skeleton information . ( ii ) An iterative fine-to-coarse merging strategy for adaptive motion segmentation and structural topology embedding . ( iii ) A skeleton estimation method based on a novel silhouette boundary generation from sparse feature points using an adaptive model selection method .
However , at the same time , they will unexpectedly damage the representative ability of the learned deep features to some extent . Therefore , we propose a unified Bilateral-Branch Network ( BBN ) to take care of both representation learning and classifier learning simultaneously , where each branch does perform its own duty separately . In particular , our BBN model is further equipped with a novel cumulative learning strategy , which is designed to first learn the universal patterns and then pay attention to the tail data gradually .
In NMT , the source embedding plays the role of the entrance while the target embedding acts as the terminal . These layers occupy most of the model parameters for representation learning . Furthermore , they indirectly interface via a soft-attention mechanism , which makes them comparatively isolated .
We focus on metric learning due to the importance of computing distances between examples in learning algorithms and present Jiffy , a simple and scalable distance metric learning method for multivariate time series . Our approach is to reframe the task as a representation learning problem — rather than design an elaborate distance function , we use a CNN to learn an embedding such that the Euclidean distance is effective . Experiments on a diverse set of multivariate time series datasets show that our approach consistently outperforms existing methods .
subsection : Conceptual Compression Variational auto - encoders can not only be used for representation learning but also for compression . The training objective of variational auto - encoders is to compress the total amount of information needed to encode the input .
Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms . Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves . However , present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks .
Research on sentence compression has been undergoing for many years in other languages , especially in English , but research on Chinese sentence compression is rarely found . In this paper , we describe an efficient probabilistic and syntactic approach to Chinese sentence compression .
Distant supervision for neural relation extraction is an efficient approach to extracting massive relations with reference to plain texts . However , the existing neural methods fail to capture the critical words in sentence encoding and meanwhile lack useful sentence information for some positive training instances . To address the above issues , we propose a novel neural relation extraction model .
We hypothesized a chunking‐related modulation of neural processing during the encoding and retention of sentences ( i.e. , sentence fragments ) as compared to word lists . Time – frequency analysis revealed a two‐fold oscillatory pattern for the memorization of sentences : Sentence encoding was accompanied by higher delta amplitude ( 4 Hz ) , originating both from regions processing syntax as well as semantics ( bilateral superior/middle temporal regions and fusiform gyrus ) . Subsequent sentence retention was reflected in decreased theta ( 6 Hz ) and beta/gamma ( 27–32 Hz ) amplitude instead .
These translation tasks are characterized by the relative ability to commit to parallel parse trees and availability of word alignments , yet the unavailability of large-scale data , calling for a Bayesian tree-to-tree formalism . We formalize nonparametric Bayesian STSG with epsilon alignment in full generality , and provide a Gibbs sampling algorithm for posterior inference tailored to the task of extractive sentence compression . We achieve improvements against a number of baselines , including expectation maximization and variational Bayes training , illustrating the merits of nonparametric inference over the space of grammars as opposed to sparse parametric inference with a fixed grammar .
In this paper , we propose Operation Network , a neural network approach for abstractive sentence compression , which combines the advantages of both delete-based and generate-based sentence compression models . The central idea of Operation Network is to model the sentence compression process as an editing procedure . First , unnecessary words are deleted from the source sentence , then new words are either generated from a large vocabulary or copied directly from the source sentence .
This paper is concerned with the use of statistical information about dependency path length for sentence compression The sentence compression method employed here requires a quantity called inter-phrase dependency strength In the training process , original sentences are parsed , and the number of tokens is counted for each pair of phrases , connected with each other by a dependency path of certain length , that survive as a modifier-modified phrase pair in the corresponding compressed sentence in the training corpus The statistics is exploited to estimate the inter-phrase dependency strength required in the sentence compression process Results of subjective evaluation shows that the present method outperforms the conventional one of the same framework where the distribution of dependency distance is used to estimate the inter-phrase dependency strength .
Extractive and abstractive are two types of summarization . Extractive summary picks the sentences from document and combines them to form a summary and Abstractive summary generate summary with the help of sentence compression and fusion ) . Here we proposed a new system which is used to generate abstractive summary of multi-document text which is based on ontology .
This study proposes a text summarization model that simultaneously performs sentence extraction and compression . We translate the text summarization task into a problem of extracting a set of dependency subtrees in the document cluster .
Can textual data be compressed intelligently without losing accuracy in evaluating sentiment ? In this study , we propose a novel evolutionary compression algorithm , PARSEC ( PARts-of-Speech for sEntiment Compression ) , which makes use of Parts-of-Speech tags to compress text in a way that sacrifices minimal classification accuracy when used in conjunction with sentiment analysis algorithms . An analysis of PARSEC with eight commercial and non-commercial sentiment analysis algorithms on twelve English sentiment data sets reveals that accurate compression is possible with ( 0 % , 1.3 % , 3.3 % ) loss in sentiment classification accuracy for ( 20 % , 50 % , 75 % ) data compression with PARSEC using LingPipe , the most accurate of the sentiment algorithms .
Using the Chinese corpora of four product domains , Sent_Comp significantly improves the performance of the aspect-based sentiment analysis . The features proposed for Sent_Comp , especially the potential semantic features , are useful for sentiment sentence compression .
This model applied parameters of one Bi - LSTM to initialize the next Bi - LSTM to convey information , which shown better results than the model with a single Bi - LSTM . Besides LSTM , attention mechanisms could also be used to boost the effectiveness of sentence encoding . The model developed by Ghaeini et al . added self - attention to LSTM model , and achieved better performance .
document : Globally Normalized Transition - Based Neural Networks We introduce a globally normalized transition - based neural network model that achieves state - of - the - art part - of - speech tagging , dependency parsing and sentence compression results . Our model is a simple feed - forward neural network that operates on a task - specific transition system , yet achieves comparable or better accuracies than recurrent models .
Our results also significantly outperform the LSTM - based approaches of dyer - etAl:2015:ACL and BallesterosDS15 . subsection : Sentence Compression Our final structured prediction task is extractive sentence compression . paragraph : Data & Evaluation .
Knowledge graph embedding is an essential problem in knowledge extraction . Recently , translation based embedding models ( e.g. , TransE ) have received increasingly attentions .
Existing methods for knowledge graph embedding do not ensure the high-rank triples predicted by themselves to be as consistent as possible with the logical background which is made up of a knowledge graph and a logical theory . Users must take great effort to filter consistent triples before adding new triples to the knowledge graph .
Knowledge embeddings are key ingredients of advanced questionanswering and recommender systems . Even though their predictions are accurate , they are rather hard to interpret by human users ; interpretability techniques are needed so as to provide meaningful human-friendly explanations for prediction generated by embeddings .
As an efficient model for knowledge organization , the knowledge graph has been widely adopted in several fields , e.g. , biomedicine , sociology , and education . And there is a steady trend of learning embedding representations of knowledge graphs to facilitate knowledge graph construction and downstream tasks . In general , knowledge graph embedding techniques aim to learn vectorized representations which preserve the structural information of the graph .
document : Hypernetwork Knowledge Graph Embeddings Knowledge graphs are large graph - structured databases of facts , which typically suffer from incompleteness .
After splitting the skip-gram loss into multiple losses corresponding to different tasks , we propose a novel random-walk strategy to focus on the tasks with high loss values by considering the relative training ratio . Unlike previous random walk strategies , our proposed random-walk strategy generates training samples according to the relative training ratio among different tasks , which results in a balanced training for the node embedding . Our extensive experiments on node classification and recommendation demonstrate the superiority of BHIN2vec compared to the state-of-the-art methods .
This procedure is a NP-hard question , therefore many previous algorithms just impose some constraints or use simple heuristic method to get the relative optimal result . Traditional researches almost focus on two independent stages : node embedding and link mapping . To address existing questions such as low acceptance ratio and high mapping time , we propose an efficient and fast embedding algorithm , named as AEF(An Efficient and Fast embedding algorithm ) , which is a two coordinated mapping stages algorithm .
It is a central problem in many machine learning tasks ( e.g. , node classification , recommendation , community detection ) . The key problem in graph node embedding lies in how to define the dependence to neighbors . Existing approaches specify ( either explicitly or implicitly ) certain dependencies on neighbors , which may lead to loss of subtle but important structural information within the graph and other dependencies among neighbors .
Mobile agent is one of the useful distributed technologies of reducing network load and latency because of its disconnected operations and high asynchrony . In this paper , a component-based mobile agent framework , called EmHUMAN , is designed and implemented for structural embedding into the devices showing different functions and resource constraints . It consists of 3 layers of components .
However , most of the existing principles of neural graph embedding do not incorporate auxiliary information such as node content flexibly . In this paper we take a matrix factorization perspective of graph embedding which generalizes to structural embedding as well as content embedding in a natural way . For structure embedding , we validate that the matrix we construct and factorize preserves the high-order proximities of the graph .
In this paper , we address the problem of building effective latent embeddings for nodes on every layer from multilayer graph data , and our approach falls in the latter category . Constructing Node Embeddings : At their core , node embedding approaches attempt to identify low-rank representations that can best represent the network topology .
In order to combat this limitation , recent approaches , such as DeepWalk ( Perozzi , Al-Rfou , and Skiena 2014 ) and Node2Vec ( Grover and Leskovec 2016 ) , have resorted to a distributional hypothesis , popularly adopted in language modeling ( Harris 1954 ) , where co-occurrence of two nodes in short random walks implies a strong notion of semantic similarity . As a result , by extending highly scalable neural embedding techniques such as Word2Vec ( Mikolov et al. 2013 ) to the construction of node embeddings , one can obtain state-of-the-art results in community detection with single-layer graphs . Proposed Work : In this paper , we develop a novel scalable technique for obtaining deep node embeddings from multilayer graphs .
Recent research on graph analysis has been focused on representation learning , of which the goal is to encode the network structures into distributed embedding vectors , so as to enable various downstream applications through off-the-shelf machine learning . However , existing methods mostly focus on node-level embedding , which is insufficient for subgraph analysis . Moreover , their leverage of network structures through path sampling or neighborhood preserving is implicit and coarse .
However , most existing structural embedding approaches suffer from high computation and space cost or rely on heuristic feature engineering . Here we propose RiWalk , a flexible paradigm for learning structural node representations as it decouples the structural embedding problem into a role identification procedure and a network embedding procedure . Through role identification , rooted kernels with structural dependencies kept are built to better integrate network embedding methods .
Structural Network Embedding and End-to-End Node Classification .. Complex networks are used as an abstraction for systems modeling in physics , biology , sociology , and other areas .
Our main contributions can be summarised as follows : ( i ) An unsupervised complex articulated kinematic structure estimation method that combines motion segments with skeleton information . ( ii ) An iterative fine-to-coarse merging strategy for adaptive motion segmentation and structural topology embedding . ( iii ) A skeleton estimation method based on a novel silhouette boundary generation from sparse feature points using an adaptive model selection method .
BBN: Bilateral-Branch Network With Cumulative Learning for Long-Tailed Visual Recognition Shared-Private Bilingual Word Embeddings for Neural Machine Translation A tale of two time series methods : representation learning for improved distance and risk metrics Towards Conceptual Compression node2vec: Scalable Feature Learning for Networks Probabilistic unsupervised Chinese sentence compression Distant supervision for neural relation extraction integrated with word attention and property features Oscillatory EEG dynamics underlying automatic chunking during sentence processing Bayesian Synchronous Tree-Substitution Grammar Induction and Its Application to Sentence Compression An Operation Network for Abstractive Sentence Compression Sentence Compression Using Statistical Information About Dependency Path Length Multi-Document Abstractive Summarization Based on Ontology Subtree Extractive Summarization via Submodular Maximization Text compression for sentiment analysis via evolutionary algorithms Sentence Compression for Aspect-Based Sentiment Analysis Attention Boosted Sequential Inference Model Globally Normalized Transition-Based Neural Networks Translating Embeddings for Knowledge Graph Completion with Relation Attention Mechanism Knowledge Graph Embedding with Logical Consistency Faithfully Explaining Predictions of Knowledge Embeddings Joint Embedding Learning of Educational Knowledge Graphs Hypernetwork Knowledge Graph Embeddings BHIN2vec: Balancing the Type of Relation in Heterogeneous Information Network An Efficient and Fast Embedding Algorithm for the Virtual Networks PINE: Universal Deep Embedding for Graph Nodes via Partial Permutation Invariant Set Functions A Component-Based Framework for Structural Embedding of Mobile Agent System Generalized Neural Graph Embedding with Matrix Factorization Improved Community Detection using Deep Embeddings from Multilayer Graphs Node, Motif and Subgraph: Leveraging Network Functional Blocks Through Structural Convolution RiWalk: Fast Structural Node Embedding via Role Identification Deep Node Ranking: Structural Network Embedding and End-to-End Node Classification. Highly Articulated Kinematic Structure Estimation Combining Motion and Skeleton Information
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