Co-authorship networks, an important type of social networks, have been studied extensively from various angles such as degree distribution analysis, social community extraction and social entity ranking. Most of the previous studies consider the co-authorship relation between two authors as a collaboration. In this paper, we introduce a novel and interesting “supportiveness” measure on co-auth...

* This work is partially supported by the National Natural Science Foundation of China under grant of 60673157, the Ministry of Education key project under grant of 105071 and SEC E-Institute: Shanghai High Institutions Grid under grant of 200301. Abstract Trust is an integral component in many kinds of human interaction, allowing people to act under uncertainty and with the risk of negative co...

To support the efficient gathering of diverse information about a news event, we focus on descriptions of named entities (persons, organizations, locations) in news articles. We extend the stakeholder mining proposed by Ogawa et al. and extract descriptions of named entities in articles. We propose three measures (difference in opinion, difference in details, and difference in factor coverage) ...

With the large volume of new information created every day, determining the validity of information in a knowledge graph and filling in its missing parts are crucial tasks for many researchers and practitioners. To address this challenge, a number of knowledge graph completion methods have been developed using low-dimensional graph embeddings. Although researchers continue to improve these mode...

We present a novel approach for the problem of Named Entity Recognition and Classification (NERC), in the context of the CoNLL-2003 Shared Task. Our work is framed into the learning and inference paradigm for recognizing structures in Natural Language (Punyakanok and Roth, 2001; Carreras et al., 2002). We make use of several learned functions which, applied at local contexts, discriminatively s...

This paper presents a method to automatically acquire a given noun’s telic and agentive roles from corpus data. These relations form part of the qualia structure assumed in the generative lexicon, where the telic role represents a typical purpose of the entity and the agentive role represents the origin of the entity. Our proposed method employs a supervised machine-learning technique which mak...

In this report, we described the IRIS-IPS, an interactive people search system for HCIR 2012 data challenge. IRISIPS can recommend candidates from three different aspects, i.e. the content based relevance, the candidates’ authority rank and the social similarity. The social similarity is measured by the closeness between a candidate and the support entity list. The default the support entity li...

Non-linear models recently receive a lot of attention as people are starting to discover the power of statistical and embedding features. However, tree-based models are seldom studied in the context of structured learning despite their recent success on various classification and ranking tasks. In this paper, we propose S-MART, a tree-based structured learning framework based on multiple additi...

Entity disambiguation works by linking ambiguous mentions in text to their corresponding real-world entities in knowledge base. Recent collective disambiguation methods enforce coherence among contextual decisions at the cost of non-trivial inference processes. We propose a fast collective disambiguation approach based on stacking. First, we train a local predictor g0 with learning to rank as b...

Wikification, commonly referred to as Disambiguation to Wikipedia (D2W), is the task of identifying concepts and entities in text and disambiguating them into the most specific corresponding Wikipedia pages. Previous approaches to D2W focused on the use of local and global statistics over the given text, Wikipedia articles and its link structures, to evaluate context compatibility among a list ...