Information Retrieval
Lab Projects
Detecting Relationships among Categories
Opinion Mining and Sentiment Analysis of User Reviews
Query Session Analysis
Spam Detection in Short Text (SMS)
Contextual Search
Personalized Ranking Twitter Friends: Who to
follow
Domain Specific Search & Mining
Detecting Relationships among Categories
Knowledge of relationships among categories is of interest in different
domains such as text classification, content analysis, and text mining. We
propose and evaluate approaches to effectively identify relationships
among document categories. Our proposed novel method capitalizes on the
misclassification results of a text classifier to identify potential
relationships among categories. This leads to a relationship network. We
demonstrate that our system detects such relationships, even those
relationships that assessors failed to identify in manual
evaluation. Furthermore, we favorably compared the effectiveness of our
methods with the state of art method and demonstrated a significant
improvement in precision and recall. Furthermore, we are interested to
discover interesting relationships in the existing hierarchical knowledge
representations. The hierarchical nature of existing Web directories,
ontologies, and folksonomies, are known to provide meaningful information
that guide users and applications. We hypothesized that such hierarchical
structures provide richer information if they are further enriched by
incorporating additional links besides parents, and siblings, namely,
between non-sibling nodes. We call such structure a networked
hierarchy. Our empirical results indicate that such a networked hierarchy
introduces interesting links between nodes (non-sibling) that otherwise in
a hierarchical structure are not evident.
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N. Goharian, S. Mengle, Networked Hierarchies for Web Directories,
20th International World Wide Web conference (WWW'11), March 2011.
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S. Mengle and N. Goharian, Detecting Relationships among Categories
using Text Classification., Journal of American Society for Information
Science and Technology (JASIST), 61 (5), May 2010.
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S. Mengle, N. Goharian, A. Platt, Discovering Relationships among
Categories using Misclassification Information., ACM 23rd Symposium on
Applied Computing (SAC), March 2008.
Opinion Mining and Sentiment Analysis of User Reviews
As the popularity of online user reviews continues to increase, it is becoming
increasingly difficult for potential customers and even business owners to
understand what aspects business reviewers cared about and how the
reviewers felt about those aspects. Many websites allow and even encourage
people to submit reviews of various products and services. The text within
these reviews often contains valuable information not found in a single
1-5 "star rating". My research proposed and evaluated a novel approach to
efficiently model and analyze the text within user reviews to estimate how
much reviewers care about different aspects of a product (i.e., amenities,
food, location, room, etc. of a hotel) by estimating the aspects'
weights. A vector of aspect weights synthesizes the average customer's
preferences and expectations as well as the product's actual performance,
thus providing a way to characterize the subject of the reviews. This
approach performs statistically similar to, and arguably better than, the
best existing method, but with significantly lower computational
complexity (linear time). While the current domain of this research is a
hotel review data set, this method is not domain-specific and should work
for other types of reviews. This work is in collaboration with the Chief
Scientist at Orbitz Worldwide.
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A. Yates, N. Goharian, W. Yee, "Semi-supervised Sentiment Analysis: Merging
Labeled Sentences with Unlabeled Reviews to
Identify Sentiment", American Society for
Information Science and Technology (ASIST),
Nov 2013.
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J. Parker, A. Yates, N. Goharian, W.-G. Yee, "Efficient Estimation of
Aspect Weights", In proceedings of ACM 35th Conference on Research and
Development in Information Retrieval (SIGIR'12), August 2012.
Query Session Analysis
We developed and evaluated our approach that utilized our earlier research on
identifying the relationships among topics, now to understand the topic of
user queries and intent given sequence of user queries from a session or
multiple sessions. The context of the session queries is utilized to
improve the effectiveness of identifying the intent or topic of current
query. Earlier efforts utilized fixed number of preceding queries to
derive such contextual information. We proposed and evaluated an approach
(DQW) that identifies a set of "unambiguous" preceding queries in a
dynamically determined window to utilize in classifying an ambiguous query
to a topic. Furthermore, utilizing a relationship-net (R-net) that
represents relationships among known topics, we improved the
classification effectiveness for those ambiguous queries whose predicted
topic in this relationship-net is related to the topic of a query within
the window. Our results indicated that the hybrid approach (DQW+R-net)
statistically significantly improves the Conditional Random Field (CRF)
query classification approach when static query windowing and hierarchical
taxonomy are used (SQW+Tax), in terms of precision (10.8%), recall
(13.2%), and F1 measure (11.9%). The findings of this research can improve
our understanding of user query intent and consequently the search
results.
Spam Detection in Short Text (SMS)
Spam detection has historically focused on email spam. However,
with ever increasing sources of short texts, on the order of 10s of
characters, such as in twitter and mobile phone texting, it is important
to be able to detect spam where the text provides such little
information. We examined the affect of various text-based features such as
various character-grams, word grams, length, and specific words such as
“rate”, “award”, etc. to classify spam. We found that simple textual
features such as n-character grams are good indicators. We are interested
to enhance the work and potentially to apply in other domain.
Contextual Search
There has been a
growing interest in contextual (personalized & location-specific) search.
We propose a learning to rank model that combines general, city-specific, and
personalized information. This model is used to produce a personalized and
city-specific resultset by reranking location-specific results retrieved
from the open Web.
Personalized Ranking Twitter Friends: Who to follow or not to follow
One of the challenges for the users of social media, such as in Twitter, is
the fast growing number of people each user is following. The features
available in Twitter provide meaningful information that can be harvested
to provide a ranked list of "friends" (i.e., followees) to each user. We
hypothesize that retweet and mention features can be further enriched by
incorporating both temporal and additional/indirect links from within
user's community.
Domain Specific Search & Mining: Mining Social Media for Healthcare
Online discussions of virtually all topics are increasing; this phenomenon is
ever more so in the domain of healthcare. Individuals today are rapidly
and steadily posting remarks regarding their individual and their
loved-onesâ health on a diversity of social media. Given these publicly
available statements, there is interest and potential to harness these
sources to further our knowledge and understanding about drug behavior. We
focus on using several drug related and other general social media sites,
query analysis, peer-to-peer, and Web sites to detect expected and
unexpected adverse reaction to drugs and devices. To understand users
intentions, we utilize consumer medical terminology from UMLS and various
other approaches to generate an adverse reaction synonym set that we use
to identify both expected adverse reactions, as already recorded by the
FDA, and unexpected adverse reactions mentioned in online reviews. ADRs
Background (drug) language is utilized to evaluate the strength of the
detected unexpected ADRs. Existing synonym discovery methods perform
poorly when faced with the realistic task of identifying a target term's
synonyms from among many candidates. We approach domain-specific synonym
discovery as a graded relevance ranking problem in which a target term.s
synonym candidates are ranked by their quality. In this scenario a human
editor uses each ranked list of synonym candidates to build a
domain-specific thesaurus. We evaluate our method for graded relevance
ranking of synonym candidates and find that it outperforms existing
methods. Currently, we are enhancing the system.
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A. Yates, J. Parker, N. Goharian, and O. Frieder, "A Framework for Public
Health Surveillance", In Proceedings of the
Ninth International Conference on Language
Resources and Evaluation (LREC'14), May 2014.
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A. Yates, N. Goharian, O. Frieder, "Relevance-Ranked Domain-Specific Synonym
Discovery", in Proceedings of the 36th European Conference on
Information Retrieval (ECIR '14), April
2014.
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E. W. Burger, H. Federoff, O. Frieder, N. Goharian, A. Yates, "Social Media
Communications Networks and Pharmacovigilance:
SequelAE-2.0'), IEEE 15th International
Conference on e-Health Networking,
Applications and Services (Healthcom), Oct
2013, (short).
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J. Parker, Y. Wei, A. Yates, N. Goharian, O. Frieder, "A Framework for
Detecting Public Health Trends with Twitter",
The 2013 IEEE/ACM International Conference on
Advances in Social Network Analysis and
Mining, Aug. 2013.
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A. Yates, N. Goharian, O. Frieder, "Extracting Adverse Drug Reactions from
Forum Posts and Linking them to Drugs", SIGIR
Workshop on Health Search and Discovery,
July-Aug 2013.
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A. Yates, N. Goharian, and O. Frieder, Graded Relevance Ranking for Synonym
Discovery, 22nd International Conference on World Wide Web (WWW), May 2013
(short).
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A. Yates and N. Goharian, ADRTrace: Detecting Expected and Unexpected Adverse
Drug Reactions from User Reviews on Social Media Sites, in 35th European
Conference on Information Retrieval (ECIR), 2013. (short)
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A. Yates, N. Goharian, Mining Social Media for Healthcare, ICBI Biomedical
Informatics Symposium at Georgetown University, Best Poster Award (1 out
of 28), Oct 2012.
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