2,068 research outputs found
Matching Natural Language Sentences with Hierarchical Sentence Factorization
Semantic matching of natural language sentences or identifying the
relationship between two sentences is a core research problem underlying many
natural language tasks. Depending on whether training data is available, prior
research has proposed both unsupervised distance-based schemes and supervised
deep learning schemes for sentence matching. However, previous approaches
either omit or fail to fully utilize the ordered, hierarchical, and flexible
structures of language objects, as well as the interactions between them. In
this paper, we propose Hierarchical Sentence Factorization---a technique to
factorize a sentence into a hierarchical representation, with the components at
each different scale reordered into a "predicate-argument" form. The proposed
sentence factorization technique leads to the invention of: 1) a new
unsupervised distance metric which calculates the semantic distance between a
pair of text snippets by solving a penalized optimal transport problem while
preserving the logical relationship of words in the reordered sentences, and 2)
new multi-scale deep learning models for supervised semantic training, based on
factorized sentence hierarchies. We apply our techniques to text-pair
similarity estimation and text-pair relationship classification tasks, based on
multiple datasets such as STSbenchmark, the Microsoft Research paraphrase
identification (MSRP) dataset, the SICK dataset, etc. Extensive experiments
show that the proposed hierarchical sentence factorization can be used to
significantly improve the performance of existing unsupervised distance-based
metrics as well as multiple supervised deep learning models based on the
convolutional neural network (CNN) and long short-term memory (LSTM).Comment: Accepted by WWW 2018, 10 page
Enhancing Sensitivity Classification with Semantic Features using Word Embeddings
Government documents must be reviewed to identify any sensitive information
they may contain, before they can be released to the public. However,
traditional paper-based sensitivity review processes are not practical for reviewing
born-digital documents. Therefore, there is a timely need for automatic sensitivity
classification techniques, to assist the digital sensitivity review process.
However, sensitivity is typically a product of the relations between combinations
of terms, such as who said what about whom, therefore, automatic sensitivity
classification is a difficult task. Vector representations of terms, such as word
embeddings, have been shown to be effective at encoding latent term features
that preserve semantic relations between terms, which can also be beneficial to
sensitivity classification. In this work, we present a thorough evaluation of the
effectiveness of semantic word embedding features, along with term and grammatical
features, for sensitivity classification. On a test collection of government
documents containing real sensitivities, we show that extending text classification
with semantic features and additional term n-grams results in significant improvements
in classification effectiveness, correctly classifying 9.99% more sensitive
documents compared to the text classification baseline
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