Towards a collocation writing assistant for learners of Spanish

This paper describes the process followed in creating a tool aimed at helping learners produce collocations in Spanish. First we present the Diccionario de colocaciones del español (DiCE), an online collocation dictionary, which represents the first stage of this process. The following section focuses on the potential user of a collocation learning tool: we examine the usability problems DiCE presents in this respect, and explore the actual learner needs through a learner corpus study of collocation errors. Next, we review how collocation production problems of English language learners can be solved using a variety of electronic tools devised for that language. Finally, taking all the above into account, we present a new tool aimed at assisting learners of Spanish in writing texts, with particular attention being paid to the use of collocations in this language

Production of Referring Expressions for an Unknown Audience : a Computational Model of Communal Common Ground

The research reported in this article is based on the Ph.D. project of Dr. RK, which was funded by the Scottish Informatics and Computer Science Alliance (SICSA). KvD acknowledges support from the EPSRC under the RefNet grant (EP/J019615/1).Peer reviewedPublisher PD

Know2Look: Commonsense Knowledge for Visual Search

With the rise in popularity of social media, images accompanied by contextual text form a huge section of the web. However, search and retrieval of documents are still largely dependent on solely textual cues. Although visual cues have started to gain focus, the imperfection in object/scene detection do not lead to significantly improved results. We hypothesize that the use of background commonsense knowledge on query terms can significantly aid in retrieval of documents with associated images. To this end we deploy three different modalities - text, visual cues, and commonsense knowledge pertaining to the query - as a recipe for efficient search and retrieval

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