39,474 research outputs found
A study on text-score disagreement in online reviews
In this paper, we focus on online reviews and employ artificial intelligence
tools, taken from the cognitive computing field, to help understanding the
relationships between the textual part of the review and the assigned numerical
score. We move from the intuitions that 1) a set of textual reviews expressing
different sentiments may feature the same score (and vice-versa); and 2)
detecting and analyzing the mismatches between the review content and the
actual score may benefit both service providers and consumers, by highlighting
specific factors of satisfaction (and dissatisfaction) in texts.
To prove the intuitions, we adopt sentiment analysis techniques and we
concentrate on hotel reviews, to find polarity mismatches therein. In
particular, we first train a text classifier with a set of annotated hotel
reviews, taken from the Booking website. Then, we analyze a large dataset, with
around 160k hotel reviews collected from Tripadvisor, with the aim of detecting
a polarity mismatch, indicating if the textual content of the review is in
line, or not, with the associated score.
Using well established artificial intelligence techniques and analyzing in
depth the reviews featuring a mismatch between the text polarity and the score,
we find that -on a scale of five stars- those reviews ranked with middle scores
include a mixture of positive and negative aspects.
The approach proposed here, beside acting as a polarity detector, provides an
effective selection of reviews -on an initial very large dataset- that may
allow both consumers and providers to focus directly on the review subset
featuring a text/score disagreement, which conveniently convey to the user a
summary of positive and negative features of the review target.Comment: This is the accepted version of the paper. The final version will be
published in the Journal of Cognitive Computation, available at Springer via
http://dx.doi.org/10.1007/s12559-017-9496-
Unsupervised Terminological Ontology Learning based on Hierarchical Topic Modeling
In this paper, we present hierarchical relationbased latent Dirichlet
allocation (hrLDA), a data-driven hierarchical topic model for extracting
terminological ontologies from a large number of heterogeneous documents. In
contrast to traditional topic models, hrLDA relies on noun phrases instead of
unigrams, considers syntax and document structures, and enriches topic
hierarchies with topic relations. Through a series of experiments, we
demonstrate the superiority of hrLDA over existing topic models, especially for
building hierarchies. Furthermore, we illustrate the robustness of hrLDA in the
settings of noisy data sets, which are likely to occur in many practical
scenarios. Our ontology evaluation results show that ontologies extracted from
hrLDA are very competitive with the ontologies created by domain experts
A Generative Model of Words and Relationships from Multiple Sources
Neural language models are a powerful tool to embed words into semantic
vector spaces. However, learning such models generally relies on the
availability of abundant and diverse training examples. In highly specialised
domains this requirement may not be met due to difficulties in obtaining a
large corpus, or the limited range of expression in average use. Such domains
may encode prior knowledge about entities in a knowledge base or ontology. We
propose a generative model which integrates evidence from diverse data sources,
enabling the sharing of semantic information. We achieve this by generalising
the concept of co-occurrence from distributional semantics to include other
relationships between entities or words, which we model as affine
transformations on the embedding space. We demonstrate the effectiveness of
this approach by outperforming recent models on a link prediction task and
demonstrating its ability to profit from partially or fully unobserved data
training labels. We further demonstrate the usefulness of learning from
different data sources with overlapping vocabularies.Comment: 8 pages, 5 figures; incorporated feedback from reviewers; to appear
in Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence
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Supervised Typing of Big Graphs using Semantic Embeddings
We propose a supervised algorithm for generating type embeddings in the same
semantic vector space as a given set of entity embeddings. The algorithm is
agnostic to the derivation of the underlying entity embeddings. It does not
require any manual feature engineering, generalizes well to hundreds of types
and achieves near-linear scaling on Big Graphs containing many millions of
triples and instances by virtue of an incremental execution. We demonstrate the
utility of the embeddings on a type recommendation task, outperforming a
non-parametric feature-agnostic baseline while achieving 15x speedup and
near-constant memory usage on a full partition of DBpedia. Using
state-of-the-art visualization, we illustrate the agreement of our
extensionally derived DBpedia type embeddings with the manually curated domain
ontology. Finally, we use the embeddings to probabilistically cluster about 4
million DBpedia instances into 415 types in the DBpedia ontology.Comment: 6 pages, to be published in Semantic Big Data Workshop at ACM, SIGMOD
2017; extended version in preparation for Open Journal of Semantic Web (OJSW
Syntactic phrase-based statistical machine translation
Phrase-based statistical machine translation (PBSMT) systems represent the dominant approach in MT today. However, unlike systems in other paradigms, it has proven difficult to date to incorporate syntactic knowledge in order to improve translation quality. This paper improves on recent research which uses 'syntactified' target language phrases, by incorporating supertags as constraints to better resolve parse tree fragments. In addition, we do not impose any sentence-length limit, and using a log-linear decoder, we outperform a state-of-the-art PBSMT system by over 1.3 BLEU points (or 3.51% relative) on the NIST 2003 Arabic-English test corpus
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