3,844 research outputs found
Recommended from our members
Verifying baselines for crisis event information classification on Twitter
Social media are rich information sources during and in the aftermath of crisis events such as earthquakes and terrorist attacks. Despite myriad challenges, with the right tools, significant insight can be gained which can assist emergency responders and related applications. However, most extant approaches are incomparable, using bespoke definitions, models, datasets and even evaluation metrics. Furthermore, it is rare that code, trained models, or exhaustive parametrisation details are made openly available. Thus, even confirmation of self-reported performance is problematic; authoritatively determining the state of the art (SOTA) is essentially impossible. Consequently, to begin addressing such endemic ambiguity, this paper seeks to make 3 contributions: 1) the replication and results confirmation of a leading (and generalisable) technique; 2) testing straightforward modifications of the technique likely to improve performance; and 3) the extension of the technique to a novel and complimentary type of crisis-relevant information to demonstrate it’s generalisability
NLSC: Unrestricted Natural Language-based Service Composition through Sentence Embeddings
Current approaches for service composition (assemblies of atomic services)
require developers to use: (a) domain-specific semantics to formalize services
that restrict the vocabulary for their descriptions, and (b) translation
mechanisms for service retrieval to convert unstructured user requests to
strongly-typed semantic representations. In our work, we argue that effort to
developing service descriptions, request translations, and matching mechanisms
could be reduced using unrestricted natural language; allowing both: (1)
end-users to intuitively express their needs using natural language, and (2)
service developers to develop services without relying on syntactic/semantic
description languages. Although there are some natural language-based service
composition approaches, they restrict service retrieval to syntactic/semantic
matching. With recent developments in Machine learning and Natural Language
Processing, we motivate the use of Sentence Embeddings by leveraging richer
semantic representations of sentences for service description, matching and
retrieval. Experimental results show that service composition development
effort may be reduced by more than 44\% while keeping a high precision/recall
when matching high-level user requests with low-level service method
invocations.Comment: This paper will appear on SCC'19 (IEEE International Conference on
Services Computing) on July 1
Information Extraction in Illicit Domains
Extracting useful entities and attribute values from illicit domains such as
human trafficking is a challenging problem with the potential for widespread
social impact. Such domains employ atypical language models, have `long tails'
and suffer from the problem of concept drift. In this paper, we propose a
lightweight, feature-agnostic Information Extraction (IE) paradigm specifically
designed for such domains. Our approach uses raw, unlabeled text from an
initial corpus, and a few (12-120) seed annotations per domain-specific
attribute, to learn robust IE models for unobserved pages and websites.
Empirically, we demonstrate that our approach can outperform feature-centric
Conditional Random Field baselines by over 18\% F-Measure on five annotated
sets of real-world human trafficking datasets in both low-supervision and
high-supervision settings. We also show that our approach is demonstrably
robust to concept drift, and can be efficiently bootstrapped even in a serial
computing environment.Comment: 10 pages, ACM WWW 201
Recommended from our members
Detecting Important Life Events on Twitter Using Frequent Semantic and Syntactic Subgraphs
Identifying global events from social media has been the focus of much research in recent years. However, the identification of personal life events poses new requirements and challenges that have received relatively little research attention. In this paper we explore a new approach for life event identification, where we expand social media posts into both semantic, and syntactic networks of content. Frequent graph patterns are mined from these networks and used as features to enrich life-event classifiers. Results show that our approach significantly outperforms the best performing baseline in accuracy (by 4.48% points) and F-measure (by 4.54% points) when used to identify five major life events identified from the psychology literature: Getting Married, Having Children, Death of a Parent, Starting School, and Falling in Love. In addition, our results show that, while semantic graphs are effective at discriminating the theme of the post (e.g. the topic of marriage), syntactic graphs help identify whether the post describes a personal event (e.g. someone getting married)
Dynamics of private social networks
Social networks, have been a significant turning point in ways individuals and companies interact. Various research has also revolved around public social networks, such as Twitter and Facebook. In most cases trying to understand what's happening in the network such predicting trends, and identifying natural phenomenon. Seeing the growth of public social networks several corporations have sought to build their own private networks to enable their staff to share knowledge, and expertise. Little research has been done in regards to the value private networks give to their stake holders. This is primarily due to the fact as their name implies, these networks are private, thus access to internal data is limited to a trusted few. This paper looks at a particular online private social network, and seeks to investigate the research possibilities made available, and how this can bring value to the organisation which runs the network. Notwithstanding the limitations of the network, this paper seeks to explore the connections graph between members of the network, as well as understanding the topics discussed within the network. The findings show that by visualising a social network one can assess the success or failure of their online networks. The Analysis conducted can also identify skill shortages within areas of the network, thus allowing corporations to take action and rectify any potential problems.peer-reviewe
- …