98,706 research outputs found
Generating Concise and Readable Summaries of XML Documents
XML has become the de-facto standard for data representation and exchange,
resulting in large scale repositories and warehouses of XML data. In order for
users to understand and explore these large collections, a summarized, bird's
eye view of the available data is a necessity. In this paper, we are interested
in semantic XML document summaries which present the "important" information
available in an XML document to the user. In the best case, such a summary is a
concise replacement for the original document itself. At the other extreme, it
should at least help the user make an informed choice as to the relevance of
the document to his needs. In this paper, we address the two main issues which
arise in producing such meaningful and concise summaries: i) which tags or text
units are important and should be included in the summary, ii) how to generate
summaries of different sizes.%for different memory budgets. We conduct user
studies with different real-life datasets and show that our methods are useful
and effective in practice
A Novel ILP Framework for Summarizing Content with High Lexical Variety
Summarizing content contributed by individuals can be challenging, because
people make different lexical choices even when describing the same events.
However, there remains a significant need to summarize such content. Examples
include the student responses to post-class reflective questions, product
reviews, and news articles published by different news agencies related to the
same events. High lexical diversity of these documents hinders the system's
ability to effectively identify salient content and reduce summary redundancy.
In this paper, we overcome this issue by introducing an integer linear
programming-based summarization framework. It incorporates a low-rank
approximation to the sentence-word co-occurrence matrix to intrinsically group
semantically-similar lexical items. We conduct extensive experiments on
datasets of student responses, product reviews, and news documents. Our
approach compares favorably to a number of extractive baselines as well as a
neural abstractive summarization system. The paper finally sheds light on when
and why the proposed framework is effective at summarizing content with high
lexical variety.Comment: Accepted for publication in the journal of Natural Language
Engineering, 201
Movie Popularity Classification based on Inherent Movie Attributes using C4.5,PART and Correlation Coefficient
Abundance of movie data across the internet makes it an obvious candidate for
machine learning and knowledge discovery. But most researches are directed
towards bi-polar classification of movie or generation of a movie
recommendation system based on reviews given by viewers on various internet
sites. Classification of movie popularity based solely on attributes of a movie
i.e. actor, actress, director rating, language, country and budget etc. has
been less highlighted due to large number of attributes that are associated
with each movie and their differences in dimensions. In this paper, we propose
classification scheme of pre-release movie popularity based on inherent
attributes using C4.5 and PART classifier algorithm and define the relation
between attributes of post release movies using correlation coefficient.Comment: 6 page
Recommended from our members
STRATEGIST : a program that models strategy-driven and content-driven inference behavior
In the course of understanding a text, different readers use different inference strategies to guide their choice of interpretations of the events in the text. This is in contrast to previous computer models of understanding, which all use the content-driven inference. The separate strategies are theorized to be composed of the same component inference processes, but of different rules for application of the processes. The use of different strategies occasionally results in different results of new experimental data and a working computer program, called STRATEGIST, that models both strategy-drive and content-driven inference behavior. The rules which make up two of these strategies are presented
BDGS: A Scalable Big Data Generator Suite in Big Data Benchmarking
Data generation is a key issue in big data benchmarking that aims to generate
application-specific data sets to meet the 4V requirements of big data.
Specifically, big data generators need to generate scalable data (Volume) of
different types (Variety) under controllable generation rates (Velocity) while
keeping the important characteristics of raw data (Veracity). This gives rise
to various new challenges about how we design generators efficiently and
successfully. To date, most existing techniques can only generate limited types
of data and support specific big data systems such as Hadoop. Hence we develop
a tool, called Big Data Generator Suite (BDGS), to efficiently generate
scalable big data while employing data models derived from real data to
preserve data veracity. The effectiveness of BDGS is demonstrated by developing
six data generators covering three representative data types (structured,
semi-structured and unstructured) and three data sources (text, graph, and
table data)
TimeMachine: Timeline Generation for Knowledge-Base Entities
We present a method called TIMEMACHINE to generate a timeline of events and
relations for entities in a knowledge base. For example for an actor, such a
timeline should show the most important professional and personal milestones
and relationships such as works, awards, collaborations, and family
relationships. We develop three orthogonal timeline quality criteria that an
ideal timeline should satisfy: (1) it shows events that are relevant to the
entity; (2) it shows events that are temporally diverse, so they distribute
along the time axis, avoiding visual crowding and allowing for easy user
interaction, such as zooming in and out; and (3) it shows events that are
content diverse, so they contain many different types of events (e.g., for an
actor, it should show movies and marriages and awards, not just movies). We
present an algorithm to generate such timelines for a given time period and
screen size, based on submodular optimization and web-co-occurrence statistics
with provable performance guarantees. A series of user studies using Mechanical
Turk shows that all three quality criteria are crucial to produce quality
timelines and that our algorithm significantly outperforms various baseline and
state-of-the-art methods.Comment: To appear at ACM SIGKDD KDD'15. 12pp, 7 fig. With appendix. Demo and
other info available at http://cs.stanford.edu/~althoff/timemachine
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