1,841 research outputs found
Computational Approaches to Measuring the Similarity of Short Contexts : A Review of Applications and Methods
Measuring the similarity of short written contexts is a fundamental problem
in Natural Language Processing. This article provides a unifying framework by
which short context problems can be categorized both by their intended
application and proposed solution. The goal is to show that various problems
and methodologies that appear quite different on the surface are in fact very
closely related. The axes by which these categorizations are made include the
format of the contexts (headed versus headless), the way in which the contexts
are to be measured (first-order versus second-order similarity), and the
information used to represent the features in the contexts (micro versus macro
views). The unifying thread that binds together many short context applications
and methods is the fact that similarity decisions must be made between contexts
that share few (if any) words in common.Comment: 23 page
A Graph-Based Approach for the Summarization of Scientific Articles
Automatic text summarization is one of the eminent applications in the field of
Natural Language Processing. Text summarization is the process of generating
a gist from text documents. The task is to produce a summary which contains
important, diverse and coherent information, i.e., a summary should be self-contained.
The approaches for text summarization are conventionally extractive.
The extractive approaches select a subset of sentences from an input document
for a summary. In this thesis, we introduce a novel graph-based extractive summarization
approach.
With the progressive advancement of research in the various fields of science,
the summarization of scientific articles has become an essential requirement for
researchers. This is our prime motivation in selecting scientific articles as our
dataset. This newly formed dataset contains scientific articles from the PLOS
Medicine journal, which is a high impact journal in the field of biomedicine.
The summarization of scientific articles is a single-document summarization task.
It is a complex task due to various reasons, one of it being, the important information
in the scientific article is scattered all over it and another reason being, scientific
articles contain numerous redundant information. In our approach, we deal
with the three important factors of summarization: importance, non-redundancy
and coherence. To deal with these factors, we use graphs as they solve data sparsity
problems and are computationally less complex.
We employ bipartite graphical representation for the summarization task, exclusively.
We represent input documents through a bipartite graph that consists of
sentence nodes and entity nodes. This bipartite graph representation contains entity
transition information which is beneficial for selecting the relevant sentences
for a summary. We use a graph-based ranking algorithm to rank the sentences in
a document. The ranks are considered as relevance scores of the sentences which
are further used in our approach.
Scientific articles contain reasonable amount of redundant information, for example,
Introduction and Methodology sections contain similar information regarding
the motivation and approach. In our approach, we ensure that the summary contains
sentences which are non-redundant.
Though the summary should contain important and non-redundant information of
the input document, its sentences should be connected to one another such that
it becomes coherent, understandable and simple to read. If we do not ensure
that a summary is coherent, its sentences may not be properly connected. This
leads to an obscure summary. Until now, only few summarization approaches
take care of coherence. In our approach, we take care of coherence in two different
ways: by using the graph measure and by using the structural information. We
employ outdegree as the graph measure and coherence patterns for the structural
information, in our approach.
We use integer programming as an optimization technique, to select the best subset
of sentences for a summary. The sentences are selected on the basis of relevance,
diversity and coherence measure. The computation of these measures is
tightly integrated and taken care of simultaneously.
We use human judgements to evaluate coherence of summaries. We compare
ROUGE scores and human judgements of different systems on the PLOS Medicine
dataset. Our approach performs considerably better than other systems on this
dataset. Also, we apply our approach on the standard DUC 2002 dataset to compare
the results with the recent state-of-the-art systems. The results show that our
graph-based approach outperforms other systems on DUC 2002. In conclusion,
our approach is robust, i.e., it works on both scientific and news articles. Our
approach has the further advantage of being semi-supervised
A Survey on Event-based News Narrative Extraction
Narratives are fundamental to our understanding of the world, providing us
with a natural structure for knowledge representation over time. Computational
narrative extraction is a subfield of artificial intelligence that makes heavy
use of information retrieval and natural language processing techniques.
Despite the importance of computational narrative extraction, relatively little
scholarly work exists on synthesizing previous research and strategizing future
research in the area. In particular, this article focuses on extracting news
narratives from an event-centric perspective. Extracting narratives from news
data has multiple applications in understanding the evolving information
landscape. This survey presents an extensive study of research in the area of
event-based news narrative extraction. In particular, we screened over 900
articles that yielded 54 relevant articles. These articles are synthesized and
organized by representation model, extraction criteria, and evaluation
approaches. Based on the reviewed studies, we identify recent trends, open
challenges, and potential research lines.Comment: 37 pages, 3 figures, to be published in the journal ACM CSU
Proceedings of the First Workshop on Computing News Storylines (CNewsStory 2015)
This volume contains the proceedings of the 1st Workshop on Computing News Storylines (CNewsStory
2015) held in conjunction with the 53rd Annual Meeting of the Association for Computational
Linguistics and the 7th International Joint Conference on Natural Language Processing (ACL-IJCNLP
2015) at the China National Convention Center in Beijing, on July 31st 2015.
Narratives are at the heart of information sharing. Ever since people began to share their experiences,
they have connected them to form narratives. The study od storytelling and the field of literary theory
called narratology have developed complex frameworks and models related to various aspects of
narrative such as plots structures, narrative embeddings, characters’ perspectives, reader response, point
of view, narrative voice, narrative goals, and many others. These notions from narratology have been
applied mainly in Artificial Intelligence and to model formal semantic approaches to narratives (e.g.
Plot Units developed by Lehnert (1981)). In recent years, computational narratology has qualified as an
autonomous field of study and research. Narrative has been the focus of a number of workshops and
conferences (AAAI Symposia, Interactive Storytelling Conference (ICIDS), Computational Models of
Narrative). Furthermore, reference annotation schemes for narratives have been proposed (NarrativeML
by Mani (2013)).
The workshop aimed at bringing together researchers from different communities working on
representing and extracting narrative structures in news, a text genre which is highly used in NLP
but which has received little attention with respect to narrative structure, representation and analysis.
Currently, advances in NLP technology have made it feasible to look beyond scenario-driven, atomic
extraction of events from single documents and work towards extracting story structures from multiple
documents, while these documents are published over time as news streams. Policy makers, NGOs,
information specialists (such as journalists and librarians) and others are increasingly in need of tools
that support them in finding salient stories in large amounts of information to more effectively implement
policies, monitor actions of “big players” in the society and check facts. Their tasks often revolve around
reconstructing cases either with respect to specific entities (e.g. person or organizations) or events (e.g.
hurricane Katrina). Storylines represent explanatory schemas that enable us to make better selections
of relevant information but also projections to the future. They form a valuable potential for exploiting
news data in an innovative way.JRC.G.2-Global security and crisis managemen
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