Beyond Adjacency Pairs: Hierarchical Clustering of Long Sequences for Human-Machine Dialogues
This work proposes a framework to predict sequences in dialogues, using turn based syntactic features and dialogue control functions. Syntactic features were extracted using dependency parsing, while dialogue control functions were manually labelled. These features were transformed using tf-idf and word embedding; feature selection was done using Principal Component Analysis (PCA). We ran experiments on six combinations of features to predict sequences with Hierarchical Agglomerative Clustering. An analysis of the clustering results indicate that using word embeddings and syntactic features, significantly improved the results.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringDependency Parsingfeature selectionWord EmbeddingsMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Self-Supervised Metric Learning With Graph Clustering For Speaker Diarization
In this paper, we propose a novel algorithm for speaker diarization using metric learning for graph based clustering. The graph clustering algorithms use an adjacency matrix consisting of similarity scores. These scores …
ClusteringGraph ClusteringMetric LearningRepresentation Learning+3Analysis of spectral clustering algorithms for community detection: the general bipartite setting
We consider spectral clustering algorithms for community detection under a general bipartite stochastic block model (SBM). A modern spectral clustering algorithm consists of three steps: (1) regularization of an appropri…
ClusteringCommunity DetectionStochastic Block ModelLearning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic Clustering
In this paper, we propose a novel end-to-end neural architecture for ranking candidate answers, that adapts a hierarchical recurrent neural network and a latent topic clustering module. With our proposed model, a text is…
Answer SelectionClusteringLearning-To-RankQuestion AnsweringRelations Between Adjacency and Modularity Graph Partitioning
This paper develops the exact linear relationship between the leading eigenvector of the unnormalized modularity matrix and the eigenvectors of the adjacency matrix. We propose a method for approximating the leading eige…
Clusteringgraph partitioningClusterTabNet: Supervised clustering method for table detection and table structure recognition
We present a novel deep-learning-based method to cluster words in documents which we apply to detect and recognize tables given the OCR output. We interpret table structure bottom-up as a graph of relations between pairs…
ClusteringOptical Character Recognition (OCR)Table Detection