Generalization of Clustering Agreements and Distances for Overlapping Clusters and Network Communities

A measure of distance between two clusterings has important applications, including clustering validation and ensemble clustering. Generally, such distance measure provides navigation through the space of possible clusterings. Mostly used in cluster validation, a normalized clustering distance, a.k.a. agreement measure, compares a given clustering result against the ground-truth clustering. Clustering agreement measures are often classified into two families of pair-counting and information theoretic measures, with the widely-used representatives of Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI), respectively. This paper sheds light on the relation between these two families through a generalization. It further presents an alternative algebraic formulation for these agreement measures which incorporates an intuitive clustering distance, which is defined based on the analogous between cluster overlaps and co-memberships of nodes in clusters. Unlike the original measures, it is easily extendable for different cases, including overlapping clusters and clusters of inter-related data for complex networks. These two extensions are, in particular, important in the context of finding clusters in social and information networks, a.k.a communities

Generating Responses Expressing Emotion in an Open-domain Dialogue System

Neural network-based Open-ended conversational agents automatically generate responses based on predictive models learned from a large number of pairs of utterances. The generated responses are typically acceptable as a sentence but are often dull, generic, and certainly devoid of any emotion. In this paper, we present neural models that learn to express a given emotion in the generated response. We propose four models and evaluate them against 3 baselines. An encoder-decoder framework-based model with multiple attention layers provides the best overall performance in terms of expressing the required emotion. While it does not outperform other models on all emotions, it presents promising results in most cases

ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT

This paper describes the system submitted by ANA Team for the SemEval-2019 Task 3: EmoContext. We propose a novel Hierarchical LSTMs for Contextual Emotion Detection (HRLCE) model. It classifies the emotion of an utterance given its conversational context. The results show that, in this task, our HRCLE outperforms the most recent state-of-the-art text classification framework: BERT. We combine the results generated by BERT and HRCLE to achieve an overall score of 0.7709 which ranked 5th on the final leader board of the competition among 165 Teams.Comment: Accepted at the SemEval-2019 International Workshop on Semantic Evaluatio

Estimating True And False Positive Rates In Higher Dimensional Problems and its Data Mining Applications

If we can estimate the accuracy of our observations then we can estimate the true and false positive rates over a series of samples in high dimensional data mining problems. To date such issues have been largely neglected and previously no algorithm has been provided to facilitate the computations involved.In high dimensional data mining tasks, increasing sparsity leads to decreasing true positive rates. Estimating this effect allows the estimation of the true size of membership of a class or cluster allowing us to identify the top candidates for these false negatives, while tracking the likelihood of false positives. These estimates of true and false positive rates can also help researchers avoid unnecessary costs by collecting only the number of samples that are really needed. We propose an algorithm for these computation

Local Community Identification in Social Networks

There has been much recent research on identifying global community structure in networks. However, most existing approaches require complete information of the graph in question, which is impractical for some networks, e.g. the World Wide Web (WWW). Algorithms for local community detection have been proposed but their results usually contain many outliers. In this paper, we propose a new measure of local community structure, coupled with a two-phase algorithm that extracts all possible candidates first, and then optimizes the community hierarchy. We compare our results with previous methods on real world networks such as the co-purchase network from Amazon. Experimental results verify the feasibility and effectiveness of our approach. 1

An Unsupervised Approach to Cluster Web Search Results based on Word Sense Communities

Effectively organizing web search results into clusters is important to facilitate quick user navigation to relevant documents. Previous methods may rely on a training process and do not provide a measure for whether page clustering is actually required. In this paper, we reformalize the clustering problem as a word sense discovery problem. Given a query and a list of result pages, our unsupervised method detects word sense communities in the extracted keyword network. The documents are assigned to several refined word sense communities to form clusters. We use the modularity score of the discovered keyword community structure to measure page clustering necessity. Experimental results verify our method’s feasibility and effectiveness.

Detecting Communities in Large Networks by Iterative Local Expansion

Much structured data of scientific interest can be represented as networks, where sets of nodes or vertices are joined together in pairs by links or edges. Although these networks may belong to different research areas, there is one property that many of them do have in common: the network community structure, which means that there exists densely connected groups of vertices, with only sparser connections between groups. Identifying community structure in networks has attracted much research attention. However, most existing approaches require structure information of the graph in question to be completely accessible, which is impractical for some large networks, e.g., the World Wide Web (WWW). In this paper, we propose a community discovery algorithm for large networks that iteratively finds communities based on local information only. We compare our algorithm with previous global approaches to show its scalability. Experimental results on real world networks, such as the co-purchase network from Amazon, verify the feasibility and effectiveness of our approach. 1

ANA at SemEval-2020 Task 4: mUlti-task learNIng for cOmmonsense reasoNing (UNION)

In this paper, we describe our mUlti-task learNIng for cOmmonsense reasoNing (UNION) system submitted for Task C of the SemEval2020 Task 4, which is to generate a reason explaining why a given false statement is non-sensical. However, we found in the early experiments that simple adaptations such as fine-tuning GPT2 often yield dull and non-informative generations (e.g. simple negations). In order to generate more meaningful explanations, we propose UNION, a unified end-to-end framework, to utilize several existing commonsense datasets so that it allows a model to learn more dynamics under the scope of commonsense reasoning. In order to perform model selection efficiently, accurately and promptly, we also propose a couple of auxiliary automatic evaluation metrics so that we can extensively compare the models from different perspectives. Our submitted system not only results in a good performance in the proposed metrics but also outperforms its competitors with the highest achieved score of 2.10 for human evaluation while remaining a BLEU score of 15.7. Our code is made publicly available at GitHub.Comment: 7 pages, 1 figure, 3 tables, SemEval 202

Seq2Emo for Multi-label Emotion Classification Based on Latent Variable Chains Transformation

Emotion detection in text is an important task in NLP and is essential in many applications. Most of the existing methods treat this task as a problem of single-label multi-class text classification. To predict multiple emotions for one instance, most of the existing works regard it as a general Multi-label Classification (MLC) problem, where they usually either apply a manually determined threshold on the last output layer of their neural network models or train multiple binary classifiers and make predictions in the fashion of one-vs-all. However, compared to labels in the general MLC datasets, the number of emotion categories are much fewer (less than 10). Additionally, emotions tend to have more correlations with each other. For example, the human usually does not express "joy" and "anger" at the same time, but it is very likely to have "joy" and "love" expressed together. Given this intuition, in this paper, we propose a Latent Variable Chain (LVC) transformation and a tailored model -- Seq2Emo model that not only naturally predicts multiple emotion labels but also takes into consideration their correlations. We perform the experiments on the existing multi-label emotion datasets as well as on our newly collected datasets. The results show that our model compares favorably with existing state-of-the-art methods.Comment: 10 pages, 2 figures, 5 table

On Generality and Knowledge Transferability in Cross-Domain Duplicate Question Detection for Heterogeneous Community Question Answering

Duplicate question detection is an ongoing challenge in community question answering because semantically equivalent questions can have significantly different words and structures. In addition, the identification of duplicate questions can reduce the resources required for retrieval, when the same questions are not repeated. This study compares the performance of deep neural networks and gradient tree boosting, and explores the possibility of domain adaptation with transfer learning to improve the under-performing target domains for the text-pair duplicates classification task, using three heterogeneous datasets: general-purpose Quora, technical Ask Ubuntu, and academic English Stack Exchange. Ultimately, our study exposes the alternative hypothesis that the meaning of a "duplicate" is not inherently general-purpose, but rather is dependent on the domain of learning, hence reducing the chance of transfer learning through adapting to the domain