276 research outputs found
Multi-Task Learning of Keyphrase Boundary Classification
Keyphrase boundary classification (KBC) is the task of detecting keyphrases
in scientific articles and labelling them with respect to predefined types.
Although important in practice, this task is so far underexplored, partly due
to the lack of labelled data. To overcome this, we explore several auxiliary
tasks, including semantic super-sense tagging and identification of multi-word
expressions, and cast the task as a multi-task learning problem with deep
recurrent neural networks. Our multi-task models perform significantly better
than previous state of the art approaches on two scientific KBC datasets,
particularly for long keyphrases.Comment: ACL 201
Recommending Themes for Ad Creative Design via Visual-Linguistic Representations
There is a perennial need in the online advertising industry to refresh ad
creatives, i.e., images and text used for enticing online users towards a
brand. Such refreshes are required to reduce the likelihood of ad fatigue among
online users, and to incorporate insights from other successful campaigns in
related product categories. Given a brand, to come up with themes for a new ad
is a painstaking and time consuming process for creative strategists.
Strategists typically draw inspiration from the images and text used for past
ad campaigns, as well as world knowledge on the brands. To automatically infer
ad themes via such multimodal sources of information in past ad campaigns, we
propose a theme (keyphrase) recommender system for ad creative strategists. The
theme recommender is based on aggregating results from a visual question
answering (VQA) task, which ingests the following: (i) ad images, (ii) text
associated with the ads as well as Wikipedia pages on the brands in the ads,
and (iii) questions around the ad. We leverage transformer based cross-modality
encoders to train visual-linguistic representations for our VQA task. We study
two formulations for the VQA task along the lines of classification and
ranking; via experiments on a public dataset, we show that cross-modal
representations lead to significantly better classification accuracy and
ranking precision-recall metrics. Cross-modal representations show better
performance compared to separate image and text representations. In addition,
the use of multimodal information shows a significant lift over using only
textual or visual information.Comment: 7 pages, 8 figures, 2 tables, accepted by The Web Conference 202
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TeKET: a Tree-Based Unsupervised Keyphrase Extraction Technique
Automatic keyphrase extraction techniques aim to extract quality keyphrases for higher level summarization of a document. Majority of the existing techniques are mainly domain-specific, which require application domain knowledge and employ higher order statistical methods, and computationally expensive and require large train data, which is rare for many applications. Overcoming these issues, this paper proposes a new unsupervised keyphrase extraction technique. The proposed unsupervised keyphrase extraction technique, named TeKET or Tree-based Keyphrase Extraction Technique, is a domain-independent technique that employs limited statistical knowledge and requires no train data. This technique also introduces a new variant of a binary tree, called KeyPhrase Extraction (KePhEx) tree, to extract final keyphrases from candidate keyphrases. In addition, a measure, called Cohesiveness Index or CI, is derived which denotes a given node’s degree of cohesiveness with respect to the root. The CI is used in flexibly extracting final keyphrases from the KePhEx tree and is co-utilized in the ranking process. The effectiveness of the proposed technique and its domain and language independence are experimentally evaluated using available benchmark corpora, namely SemEval-2010 (a scientific articles dataset), Theses100 (a thesis dataset), and a German Research Article dataset, respectively. The acquired results are compared with other relevant unsupervised techniques belonging to both statistical and graph-based techniques. The obtained results demonstrate the improved performance of the proposed technique over other compared techniques in terms of precision, recall, and F1 scores
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