3,456 research outputs found
Detecting and ordering adjectival scalemates
This paper presents a pattern-based method that can be used to infer
adjectival scales, such as , from a corpus. Specifically,
the proposed method uses lexical patterns to automatically identify and order
pairs of scalemates, followed by a filtering phase in which unrelated pairs are
discarded. For the filtering phase, several different similarity measures are
implemented and compared. The model presented in this paper is evaluated using
the current standard, along with a novel evaluation set, and shown to be at
least as good as the current state-of-the-art.Comment: Paper presented at MAPLEX 2015, February 9-10, Yamagata, Japan
(http://lang.cs.tut.ac.jp/maplex2015/
BERT Knows Punta Cana is not just Beautiful, it's Gorgeous : Ranking Scalar Adjectives with Contextualised Representations
Adjectives like pretty, beautiful and gorgeous describe positive properties of the nouns they modify but with different intensity. These differences are important for natural language understanding and reasoning. We propose a novel BERT-based approach to intensity detection for scalar adjectives. We model intensity by vectors directly derived from contextualised representations and show they can successfully rank scalar adjectives. We evaluate our models both intrinsically, on gold standard datasets, and on an Indirect Question Answering task. Our results demonstrate that BERT encodes rich knowledge about the semantics of scalar adjectives, and is able to provide better quality intensity rankings than static embeddings and previous models with access to dedicated resources.Peer reviewe
HiER 2015. Proceedings des 9. Hildesheimer Evaluierungs- und Retrievalworkshop
Die Digitalisierung formt unsere Informationsumwelten. Disruptive Technologien dringen verstärkt und immer schneller in unseren Alltag ein und verändern unser Informations- und Kommunikationsverhalten. Informationsmärkte wandeln sich. Der 9. Hildesheimer Evaluierungs- und Retrievalworkshop HIER 2015 thematisiert die Gestaltung und Evaluierung von Informationssystemen vor dem Hintergrund der sich beschleunigenden Digitalisierung. Im Fokus stehen die folgenden Themen: Digital Humanities, Internetsuche und Online Marketing, Information Seeking und nutzerzentrierte Entwicklung, E-Learning
Deriving the Pricing Power of Product Features by Mining Consumer Reviews
The increasing pervasiveness of the Internet has dramatically changed
the way that consumers shop for goods. Consumer-generated product
reviews have become a valuable source of information for customers, who
read the reviews and decide whether to buy the product based on the
information provided. In this paper, we use techniques that decompose
the reviews into segments that evaluate the individual characteristics
of a product (e.g., image quality and battery life for a digital
camera). Then, as a major contribution of this paper, we adapt methods
from the econometrics literature, specifically the hedonic regression
concept, to estimate: (a) the weight that customers place on each
individual product feature, (b) the implicit evaluation score that
customers assign to each feature, and (c) how these evaluations affect
the revenue for a given product. Towards this goal, we develop a novel
hybrid technique combining text mining and econometrics that models
consumer product reviews as elements in a tensor product of feature and
evaluation spaces. We then impute the quantitative impact of consumer
reviews on product demand as a linear functional from this tensor
product space. We demonstrate how to use a low-dimension approximation
of this functional to significantly reduce the number of model
parameters, while still providing good experimental results. We evaluate
our technique using a data set from Amazon.com consisting of sales data
and the related consumer reviews posted over a 15-month period for 242
products. Our experimental evaluation shows that we can extract
actionable business intelligence from the data and better understand the
customer preferences and actions. We also show that the textual portion
of the reviews can improve product sales prediction compared to a
baseline technique that simply relies on numeric data
Comprehensive Review of Opinion Summarization
The abundance of opinions on the web has kindled the study of opinion summarization over the last few years. People have introduced various techniques and paradigms to solving this special task. This survey attempts to systematically investigate the different techniques and approaches used in opinion summarization. We provide a multi-perspective classification of the approaches used and highlight some of the key weaknesses of these approaches. This survey also covers evaluation techniques and data sets used in studying the opinion summarization problem. Finally, we provide insights into some of the challenges that are left to be addressed as this will help set the trend for future research in this area.unpublishednot peer reviewe
How to capture the heart ? Reviewing 20 years of emotion measurement in advertising.
In the latest decades, emotions have become an important research topic in all behavioral sciences, and not the least in advertising. Yet, advertising literature on how to measure emotions is not straightforward. The major aim of this article is to give an update on the different methods used for measuring emotions in advertising and to discuss their validity and applicability. We further draw conclusions on the relation between emotions and traditional measures of advertising effectiveness. We finally formulate recommendations on the use of the different methods and make suggestions for future research.Research; Emotions; Science; Advertising; Effectiveness; Recommendations;
Self-Regulation Therapy to Reproduce Drug Effects: A Suggestion Technique to Change Personality and the DRD3 Gene Expression
This study proposes a strategy, based on self-regulation
therapy, to change personality and its biological substrate, the DRD3
gene expression. It has been demonstrated that acute doses of stimulating
drugs, like methylphenidate, are able to change personality
and the expression of certain genes in the short term. On the other
hand, self-regulation therapy has been proven to reproduce the effects
of drugs. Thus, it is feasible to hope that self-regulation therapy is
equally effective as methylphenidate in changing personality and the
gene expression. This is a preliminary study with a single-case experimental
design with replication in which 2 subjects participated. The
results and potential implications for research and psychotherapy are
discussed.Amigó Borrás, S.; Caselles Moncho, A.; Micó Ruiz, JC. (2013). Self-Regulation Therapy to Reproduce Drug Effects: A Suggestion Technique to Change Personality and the DRD3 Gene Expression. International Journal of Clinical and Experimental Hypnosis. 61(3):282-304. doi:10.1080/00207144.2013.784094S282304613Accili, D., Fishburn, C. S., Drago, J., Steiner, H., Lachowicz, J. E., Park, B. H., … Fuchs, S. (1996). A targeted mutation of the D3 dopamine receptor gene is associated with hyperactivity in mice. Proceedings of the National Academy of Sciences, 93(5), 1945-1949. doi:10.1073/pnas.93.5.1945Amigó, S., Caselles, A., & Micó, J. C. (2008). A dynamic extraversion model. The brain’s response to a single dose of a stimulant drug. British Journal of Mathematical and Statistical Psychology, 61(1), 211-231. doi:10.1348/000711007x185514Amigó, S., Caselles, A., & Micó, J. C. (2010). General Factor of Personality Questionnaire (GFPQ): Only one Factor to Understand Personality? 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Precursors and Laggards: An Analysis of Semantic Temporal Relationships on a Blog Network
We explore the hypothesis that it is possible to obtain information about the
dynamics of a blog network by analysing the temporal relationships between
blogs at a semantic level, and that this type of analysis adds to the knowledge
that can be extracted by studying the network only at the structural level of
URL links. We present an algorithm to automatically detect fine-grained
discussion topics, characterized by n-grams and time intervals. We then propose
a probabilistic model to estimate the temporal relationships that blogs have
with one another. We define the precursor score of blog A in relation to blog B
as the probability that A enters a new topic before B, discounting the effect
created by asymmetric posting rates. Network-level metrics of precursor and
laggard behavior are derived from these dyadic precursor score estimations.
This model is used to analyze a network of French political blogs. The scores
are compared to traditional link degree metrics. We obtain insights into the
dynamics of topic participation on this network, as well as the relationship
between precursor/laggard and linking behaviors. We validate and analyze
results with the help of an expert on the French blogosphere. Finally, we
propose possible applications to the improvement of search engine ranking
algorithms
THE IDENTIFICATION OF NOTEWORTHY HOTEL REVIEWS FOR HOTEL MANAGEMENT
The rapid emergence of user-generated content (UGC) inspires knowledge sharing among Internet users. A good example is the well-known travel site TripAdvisor.com, which enables users to share their experiences and express their opinions on attractions, accommodations, restaurants, etc. The UGC about travel provide precious information to the users as well as staff in travel industry. In particular, how to identify reviews that are noteworthy for hotel management is critical to the success of hotels in the competitive travel industry. We have employed two hotel managers to conduct an examination on Taiwan’s hotel reviews in Tripadvisor.com and found that noteworthy reviews can be characterized by their content features, sentiments, and review qualities. Through the experiments using tripadvisor.com data, we find that all three types of features are important in identifying noteworthy hotel reviews. Specifically, content features are shown to have the most impact, followed by sentiments and review qualities. With respect to the various methods for representing content features, LDA method achieves comparable performance to TF-IDF method with higher recall and much fewer features
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