7,069 research outputs found
Sentiment analysis:towards a tool for analysing real-time students feedback
Students' real-time feedback has numerous advantages in education, however, analysing feedback while teaching is both stressful and time consuming. To address this problem, we propose to analyse feedback automatically using sentiment analysis. Sentiment analysis is domain dependent and although it has been applied to the educational domain before, it has not been previously used for real-time feedback. To find the best model for automatic analysis we look at four aspects: preprocessing, features, machine learning techniques and the use of the neutral class. We found that the highest result for the four aspects is Support Vector Machines (SVM) with the highest level of preprocessing, unigrams and no neutral class, which gave a 95 percent accuracy
Precise n-gram Probabilities from Stochastic Context-free Grammars
We present an algorithm for computing n-gram probabilities from stochastic
context-free grammars, a procedure that can alleviate some of the standard
problems associated with n-grams (estimation from sparse data, lack of
linguistic structure, among others). The method operates via the computation of
substring expectations, which in turn is accomplished by solving systems of
linear equations derived from the grammar. We discuss efficient implementation
of the algorithm and report our practical experience with it.Comment: 12 pages, to appear in ACL-9
A hierarchical loss and its problems when classifying non-hierarchically
Failing to distinguish between a sheepdog and a skyscraper should be worse
and penalized more than failing to distinguish between a sheepdog and a poodle;
after all, sheepdogs and poodles are both breeds of dogs. However, existing
metrics of failure (so-called "loss" or "win") used in textual or visual
classification/recognition via neural networks seldom leverage a-priori
information, such as a sheepdog being more similar to a poodle than to a
skyscraper. We define a metric that, inter alia, can penalize failure to
distinguish between a sheepdog and a skyscraper more than failure to
distinguish between a sheepdog and a poodle. Unlike previously employed
possibilities, this metric is based on an ultrametric tree associated with any
given tree organization into a semantically meaningful hierarchy of a
classifier's classes. An ultrametric tree is a tree with a so-called
ultrametric distance metric such that all leaves are at the same distance from
the root. Unfortunately, extensive numerical experiments indicate that the
standard practice of training neural networks via stochastic gradient descent
with random starting points often drives down the hierarchical loss nearly as
much when minimizing the standard cross-entropy loss as when trying to minimize
the hierarchical loss directly. Thus, this hierarchical loss is unreliable as
an objective for plain, randomly started stochastic gradient descent to
minimize; the main value of the hierarchical loss may be merely as a meaningful
metric of success of a classifier.Comment: 19 pages, 4 figures, 7 table
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
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