319,807 research outputs found
Theoretical aspects of the CEBAF 89-009 experiment on inclusive scattering of 4.05 GeV electrons from nuclei
We compare recent CEBAF data on inclusive electron scattering on nuclei with
predictions, based on a relation between structure functions (SF) of a nucleus,
a nucleon and a nucleus of point-nucleons. The latter contains nuclear
dynamics, e.g. binary collision contributions in addition to the asymptotic
limit. The agreement with the data is good, except in low-intensity regions.
Computed ternary collsion contributions appear too small for an explanation. We
perform scaling analyses in Gurvitz's scaling variable and found that for
, ratios of scaling functions for pairs of nuclei differ by less
than 15-20% from 1. Scaling functions for are, for increasing ,
shown to approach a plateau from above. We observe only weak -dependence
in FSI, which in the relevant kinematic region is ascribed to the diffractive
nature of the NN amplitudes appearing in FSI. This renders it difficult to
separate asymptotic from FSI parts and seriously hampers the extraction of
from scaling analyses in a model-independnent fashion.Comment: 11 p. Latex file, 2 ps fig
Query-Based Summarization using Rhetorical Structure Theory
Research on Question Answering is focused mainly on classifying the question type and finding
the answer. Presenting the answer in a way that suits the userâs needs has received little
attention. This paper shows how existing question answering systemsâwhich aim at finding
precise answers to questionsâcan be improved by exploiting summarization techniques to extract
more than just the answer from the document in which the answer resides. This is done
using a graph search algorithm which searches for relevant sentences in the discourse structure,
which is represented as a graph. The Rhetorical Structure Theory (RST) is used to create a
graph representation of a text document. The output is an extensive answer, which not only
answers the question, but also gives the user an opportunity to assess the accuracy of the answer
(is this what I am looking for?), and to find additional information that is related to the question,
and which may satisfy an information need. This has been implemented in a working multimodal
question answering system where it operates with two independently developed question
answering modules
Detecting and Explaining Causes From Text For a Time Series Event
Explaining underlying causes or effects about events is a challenging but
valuable task. We define a novel problem of generating explanations of a time
series event by (1) searching cause and effect relationships of the time series
with textual data and (2) constructing a connecting chain between them to
generate an explanation. To detect causal features from text, we propose a
novel method based on the Granger causality of time series between features
extracted from text such as N-grams, topics, sentiments, and their composition.
The generation of the sequence of causal entities requires a commonsense
causative knowledge base with efficient reasoning. To ensure good
interpretability and appropriate lexical usage we combine symbolic and neural
representations, using a neural reasoning algorithm trained on commonsense
causal tuples to predict the next cause step. Our quantitative and human
analysis show empirical evidence that our method successfully extracts
meaningful causality relationships between time series with textual features
and generates appropriate explanation between them.Comment: Accepted at EMNLP 201
Interpreting Embedding Models of Knowledge Bases: A Pedagogical Approach
Knowledge bases are employed in a variety of applications from natural
language processing to semantic web search; alas, in practice their usefulness
is hurt by their incompleteness. Embedding models attain state-of-the-art
accuracy in knowledge base completion, but their predictions are notoriously
hard to interpret. In this paper, we adapt "pedagogical approaches" (from the
literature on neural networks) so as to interpret embedding models by
extracting weighted Horn rules from them. We show how pedagogical approaches
have to be adapted to take upon the large-scale relational aspects of knowledge
bases and show experimentally their strengths and weaknesses.Comment: presented at 2018 ICML Workshop on Human Interpretability in Machine
Learning (WHI 2018), Stockholm, Swede
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