4,194 research outputs found
KERT: Automatic Extraction and Ranking of Topical Keyphrases from Content-Representative Document Titles
We introduce KERT (Keyphrase Extraction and Ranking by Topic), a framework
for topical keyphrase generation and ranking. By shifting from the
unigram-centric traditional methods of unsupervised keyphrase extraction to a
phrase-centric approach, we are able to directly compare and rank phrases of
different lengths. We construct a topical keyphrase ranking function which
implements the four criteria that represent high quality topical keyphrases
(coverage, purity, phraseness, and completeness). The effectiveness of our
approach is demonstrated on two collections of content-representative titles in
the domains of Computer Science and Physics.Comment: 9 page
Symbol Emergence in Robotics: A Survey
Humans can learn the use of language through physical interaction with their
environment and semiotic communication with other people. It is very important
to obtain a computational understanding of how humans can form a symbol system
and obtain semiotic skills through their autonomous mental development.
Recently, many studies have been conducted on the construction of robotic
systems and machine-learning methods that can learn the use of language through
embodied multimodal interaction with their environment and other systems.
Understanding human social interactions and developing a robot that can
smoothly communicate with human users in the long term, requires an
understanding of the dynamics of symbol systems and is crucially important. The
embodied cognition and social interaction of participants gradually change a
symbol system in a constructive manner. In this paper, we introduce a field of
research called symbol emergence in robotics (SER). SER is a constructive
approach towards an emergent symbol system. The emergent symbol system is
socially self-organized through both semiotic communications and physical
interactions with autonomous cognitive developmental agents, i.e., humans and
developmental robots. Specifically, we describe some state-of-art research
topics concerning SER, e.g., multimodal categorization, word discovery, and a
double articulation analysis, that enable a robot to obtain words and their
embodied meanings from raw sensory--motor information, including visual
information, haptic information, auditory information, and acoustic speech
signals, in a totally unsupervised manner. Finally, we suggest future
directions of research in SER.Comment: submitted to Advanced Robotic
Unsupervised Summarization by Jointly Extracting Sentences and Keywords
We present RepRank, an unsupervised graph-based ranking model for extractive
multi-document summarization in which the similarity between words, sentences,
and word-to-sentence can be estimated by the distances between their vector
representations in a unified vector space. In order to obtain desirable
representations, we propose a self-attention based learning method that
represent a sentence by the weighted sum of its word embeddings, and the
weights are concentrated to those words hopefully better reflecting the content
of a document. We show that salient sentences and keywords can be extracted in
a joint and mutual reinforcement process using our learned representations, and
prove that this process always converges to a unique solution leading to
improvement in performance. A variant of absorbing random walk and the
corresponding sampling-based algorithm are also described to avoid redundancy
and increase diversity in the summaries. Experiment results with multiple
benchmark datasets show that RepRank achieved the best or comparable
performance in ROUGE.Comment: 10 pages(includes 2 pages references), 1 figur
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