We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

A Study of Teams: How It All Began The Apollo Syndrome Teams Containing Similar Personalities Identifying further Team Roles Team Leadership The Missing Team Roles Developing an inventory Unsuccessful teams Winning teams Ideal team size Features of good members of a team Teams in Public Affairs How Belbin reports developed Case Studies in Using Belbin

Management teams: Why they succeed or fail

Word similarity is broadly used in many applications, such as information retrieval, information extraction, text classification, word sense disambiguation, example -based machine translation, etc. There are two different methods used to compute similarity: one is based on ontology or a semantic taxonomy; the other is based on collocations of words in a corpus. As a lexical knowledgebase with rich semantic information, How-net has been employed in various researches. Unlike other thesauri, such as WordNet and Tongyici Cilin, in which word similarity is defined based on the distance between words in a semantic taxonomy tree, How-net defines a word in a complicated multi-dimensional knowledge description language. As a result, a series of problems arise in the process of word similarity computation using How-net. The difficulties are outlined below: 1. The description of each word consists of a group of sememes. For example, the Chinese word “暗箱(camera obscura)” is described as: “part|部件, #TakePicture|拍攝, %tool|用具 , body|身”, and the Chinese word “寫信 (write a letter)” is described as: “write|寫, ContentProduct=letter|信件”; 2. The meaning of a word is not a simple combination of these sememes. Sememes are organized using a specific knowledge description language. To meet these challenges, our work includes: 1. A study on the How-net knowledge description language. We rewrite the How-net definition of a word in a more structural format, using the abstract data structure of set and feature structure. 2. A study on the algorithm used to compute word similarity based on How-net. The similarity between sememes, that between sets , and that between feature structures are given. To compute the similarity between two sememes, we

/pdf/ji-yu-zhi-wang-de-ci-hui-yu-yi-xiang-si-du-ji-suan-word-3q2xf1mp2c.pdf

基於《知網》的辭彙語義相似度計算 (Word Similarity Computing Based on How-net).

Many classification models work poorly on short texts due to data sparsity. To address this issue, we propose topic memory networks for short text classification with a novel topic memory mechanism to encode latent topic representations indicative of class labels. Different from most prior work that focuses on extending features with external knowledge or pre-trained topics, our model jointly explores topic inference and text classification with memory networks in an end-to-end manner. Experimental results on four benchmark datasets show that our model outperforms state-of-the-art models on short text classification, meanwhile generates coherent topics.

/pdf/topic-memory-networks-for-short-text-classification-1wfoazsi3u.pdf

Topic Memory Networks for Short Text Classification

Existing keyphrase extraction methods suffer from data sparsity problem when they are conducted on short and informal texts, especially microblog messages. Enriching context is one way to alleviate this problem. Considering that conversations are formed by reposting and replying messages, they provide useful clues for recognizing essential content in target posts and are therefore helpful for keyphrase identification. In this paper, we present a neural keyphrase extraction framework for microblog posts that takes their conversation context into account, where four types of neural encoders, namely, averaged embedding, RNN, attention, and memory networks, are proposed to represent the conversation context. Experimental results on Twitter and Weibo datasets show that our framework with such encoders outperforms state-of-the-art approaches.

/pdf/encoding-conversation-context-for-neural-keyphrase-3bqwypt9ap.pdf

Encoding Conversation Context for Neural Keyphrase Extraction from Microblog Posts

This paper studies automatic keyphrase extraction on social media. Previous works have achieved promising results on it, but they neglect human reading behavior during keyphrase annotating. The human attention is a crucial element of human reading behavior. It reveals the relevance of words to the main topics of the target text. Thus, this paper aims to integrate human attention into keyphrase extraction models. First, human attention is represented by the reading duration estimated from eye-tracking corpus. Then, we merge human attention with neural network models by an attention mechanism. In addition, we also integrate human attention into unsupervised models. To the best of our knowledge, we are the first to utilize human attention on keyphrase extraction tasks. The experimental results show that our models have significant improvements on two Twitter datasets.

/pdf/using-human-attention-to-extract-keyphrase-from-microblog-548wy2z7zy.pdf

Using Human Attention to Extract Keyphrase from Microblog Post.

The premise of manual keyphrase annotation is to read the corresponding content of an annotated object. Intuitively, when we read, more important words will occupy a longer reading time. H...

Enhancing keyphrase extraction from microblogs using human reading time

Collaborations are pervasive in current science. Collaborations have been studied and encouraged in many disciplines. However, little is known how a team really functions from the detailed division of labor within. In this research, we investigate the patterns of scientific collaboration and division of labor within individual scholarly articles by analyzing their co-contributorship networks. Co-contributorship networks are constructed by performing the one-mode projection of the author-task bipartite networks obtained from 138,787 papers published in PLoS journals. Given a paper, we define three types of contributors: Specialists, Team-players, and Versatiles. Specialists are those who contribute to all their tasks alone; team-players are those who contribute to every task with other collaborators; and versatiles are those who do both. We find that team-players are the majority and they tend to contribute to the five most common tasks as expected, such as "data analysis" and "performing experiments". The specialists and versatiles are more prevalent than expected by a random-graph null model. Versatiles tend to be senior authors associated with funding and supervisions. Specialists are associated with two contrasting roles: the supervising role as team leaders or marginal and specialized contributions.

Co-contributorship Network and Division of Labor in Individual Scientific Collaborations

Millions of messages are produced on microblog platforms every day, leading to the pressing need for automatic identification of key points from the massive texts. To absorb salient content from the vast bulk of microblog posts, this article focuses on the task of microblog keyphrase extraction. In previous work, most efforts treat messages as independent documents and might suffer from the data sparsity problem exhibited in short and informal microblog posts. On the contrary, we propose to enrich contexts via exploiting conversations initialized by target posts and formed by their replies, which are generally centered around relevant topics to the target posts and therefore helpful for keyphrase identification. Concretely, we present a neural keyphrase extraction framework, which has 2 modules: a conversation context encoder and a keyphrase tagger. The conversation context encoder captures indicative representation from their conversation contexts and feeds the representation into the keyphrase tagger, and the keyphrase tagger extracts salient words from target posts. The 2 modules were trained jointly to optimize the conversation context encoding and keyphrase extraction processes. In the conversation context encoder, we leverage hierarchical structures to capture the word‐level indicative representation and message‐level indicative representation hierarchically. In both of the modules, we apply character‐level representations, which enables the model to explore morphological features and deal with the out‐of‐vocabulary problem caused by the informal language style of microblog messages. Extensive comparison results on real‐life data sets indicate that our model outperforms state‐of‐the‐art models from previous studies.

Yingyi Zhang

Papers

Encoding Conversation Context for Neural Keyphrase Extraction from Microblog Posts

Using Human Attention to Extract Keyphrase from Microblog Post.

Enhancing keyphrase extraction from microblogs using human reading time

Co-contributorship Network and Division of Labor in Individual Scientific Collaborations

Joint Modeling of Characters, Words, and Conversation Contexts for Microblog Keyphrase Extraction