8,371 research outputs found
Neural Based Statement Classification for Biased Language
Biased language commonly occurs around topics which are of controversial
nature, thus, stirring disagreement between the different involved parties of a
discussion. This is due to the fact that for language and its use,
specifically, the understanding and use of phrases, the stances are cohesive
within the particular groups. However, such cohesiveness does not hold across
groups.
In collaborative environments or environments where impartial language is
desired (e.g. Wikipedia, news media), statements and the language therein
should represent equally the involved parties and be neutrally phrased. Biased
language is introduced through the presence of inflammatory words or phrases,
or statements that may be incorrect or one-sided, thus violating such
consensus.
In this work, we focus on the specific case of phrasing bias, which may be
introduced through specific inflammatory words or phrases in a statement. For
this purpose, we propose an approach that relies on a recurrent neural networks
in order to capture the inter-dependencies between words in a phrase that
introduced bias.
We perform a thorough experimental evaluation, where we show the advantages
of a neural based approach over competitors that rely on word lexicons and
other hand-crafted features in detecting biased language. We are able to
distinguish biased statements with a precision of P=0.92, thus significantly
outperforming baseline models with an improvement of over 30%. Finally, we
release the largest corpus of statements annotated for biased language.Comment: The Twelfth ACM International Conference on Web Search and Data
Mining, February 11--15, 2019, Melbourne, VIC, Australi
Generating descriptive text from functional brain images
Recent work has shown that it is possible to take brain images of a subject acquired while they saw a scene and reconstruct an approximation of that scene from the images. Here we show that it is also possible to generate _text_ from brain images. We began with images collected as participants read names of objects (e.g., ``Apartment'). Without accessing information about the object viewed for an individual image, we were able to generate from it a collection of semantically pertinent words (e.g., "door," "window"). Across images, the sets of words generated overlapped consistently with those contained in articles about the relevant concepts from the online encyclopedia Wikipedia. The technique described, if developed further, could offer an important new tool in building human computer interfaces for use in clinical settings
StarSpace: Embed All The Things!
We present StarSpace, a general-purpose neural embedding model that can solve
a wide variety of problems: labeling tasks such as text classification, ranking
tasks such as information retrieval/web search, collaborative filtering-based
or content-based recommendation, embedding of multi-relational graphs, and
learning word, sentence or document level embeddings. In each case the model
works by embedding those entities comprised of discrete features and comparing
them against each other -- learning similarities dependent on the task.
Empirical results on a number of tasks show that StarSpace is highly
competitive with existing methods, whilst also being generally applicable to
new cases where those methods are not
Transfer Learning for Multi-language Twitter Election Classification
Both politicians and citizens are increasingly embracing social media as a means to disseminate information and comment on various topics, particularly during significant political events, such as elections. Such commentary during elections is also of interest to social scientists and pollsters. To facilitate the study of social media during elections, there is a need to automatically identify posts that are topically related to those elections. However, current studies have focused on elections within English-speaking regions, and hence the resultant election content classifiers are only applicable for elections in countries where the predominant language is English. On the other hand, as social media is becoming more prevalent worldwide, there is an increasing need for election classifiers that can be generalised across different languages, without building a training dataset for each election. In this paper, based upon transfer learning, we study the development of effective and reusable election classifiers for use on social media across multiple languages. We combine transfer learning with different classifiers such as Support Vector Machines (SVM) and state-of-the-art Convolutional Neural Networks (CNN), which make use of word embedding representations for each social media post. We generalise the learned classifier models for cross-language classification by using a linear translation approach to map the word embedding vectors from one language into another. Experiments conducted over two election datasets in different languages show that without using any training data from the target language, linear translations outperform a classical transfer learning approach, namely Transfer Component Analysis (TCA), by 80% in recall and 25% in F1 measure
Using Word Embeddings in Twitter Election Classification
Word embeddings and convolutional neural networks (CNN)
have attracted extensive attention in various classification
tasks for Twitter, e.g. sentiment classification. However,
the effect of the configuration used to train and generate
the word embeddings on the classification performance has
not been studied in the existing literature. In this paper,
using a Twitter election classification task that aims to detect
election-related tweets, we investigate the impact of
the background dataset used to train the embedding models,
the context window size and the dimensionality of word
embeddings on the classification performance. By comparing
the classification results of two word embedding models,
which are trained using different background corpora
(e.g. Wikipedia articles and Twitter microposts), we show
that the background data type should align with the Twitter
classification dataset to achieve a better performance. Moreover,
by evaluating the results of word embeddings models
trained using various context window sizes and dimensionalities,
we found that large context window and dimension
sizes are preferable to improve the performance. Our experimental
results also show that using word embeddings and
CNN leads to statistically significant improvements over various
baselines such as random, SVM with TF-IDF and SVM
with word embeddings
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