4,381 research outputs found
Query Expansion with Locally-Trained Word Embeddings
Continuous space word embeddings have received a great deal of attention in
the natural language processing and machine learning communities for their
ability to model term similarity and other relationships. We study the use of
term relatedness in the context of query expansion for ad hoc information
retrieval. We demonstrate that word embeddings such as word2vec and GloVe, when
trained globally, underperform corpus and query specific embeddings for
retrieval tasks. These results suggest that other tasks benefiting from global
embeddings may also benefit from local embeddings
Third International Workshop on Gamification for Information Retrieval (GamifIR'16)
Stronger engagement and greater participation is often crucial
to reach a goal or to solve an issue. Issues like the emerging
employee engagement crisis, insufficient knowledge sharing,
and chronic procrastination. In many cases we need and
search for tools to beat procrastination or to change people’s
habits. Gamification is the approach to learn from often fun,
creative and engaging games. In principle, it is about understanding
games and applying game design elements in a
non-gaming environments. This offers possibilities for wide
area improvements. For example more accurate work, better
retention rates and more cost effective solutions by relating
motivations for participating as more intrinsic than conventional
methods. In the context of Information Retrieval (IR)
it is not hard to imagine that many tasks could benefit from
gamification techniques. Besides several manual annotation
tasks of data sets for IR research, user participation is important
in order to gather implicit or even explicit feedback
to feed the algorithms. Gamification, however, comes with
its own challenges and its adoption in IR is still in its infancy.
Given the enormous response to the first and second
GamifIR workshops that were both co-located with ECIR,
and the broad range of topics discussed, we now organized
the third workshop at SIGIR 2016 to address a range of
emerging challenges and opportunities
Teaching a New Dog Old Tricks: Resurrecting Multilingual Retrieval Using Zero-shot Learning
While billions of non-English speaking users rely on search engines every
day, the problem of ad-hoc information retrieval is rarely studied for
non-English languages. This is primarily due to a lack of data set that are
suitable to train ranking algorithms. In this paper, we tackle the lack of data
by leveraging pre-trained multilingual language models to transfer a retrieval
system trained on English collections to non-English queries and documents. Our
model is evaluated in a zero-shot setting, meaning that we use them to predict
relevance scores for query-document pairs in languages never seen during
training. Our results show that the proposed approach can significantly
outperform unsupervised retrieval techniques for Arabic, Chinese Mandarin, and
Spanish. We also show that augmenting the English training collection with some
examples from the target language can sometimes improve performance.Comment: ECIR 2020 (short
Deriving query suggestions for site search
Modern search engines have been moving away from simplistic interfaces that aimed at satisfying a user's need with a single-shot query. Interactive features are now integral parts of web search engines. However, generating good query modification suggestions remains a challenging issue. Query log analysis is one of the major strands of work in this direction. Although much research has been performed on query logs collected on the web as a whole, query log analysis to enhance search on smaller and more focused collections has attracted less attention, despite its increasing practical importance. In this article, we report on a systematic study of different query modification methods applied to a substantial query log collected on a local website that already uses an interactive search engine. We conducted experiments in which we asked users to assess the relevance of potential query modification suggestions that have been constructed using a range of log analysis methods and different baseline approaches. The experimental results demonstrate the usefulness of log analysis to extract query modification suggestions. Furthermore, our experiments demonstrate that a more fine-grained approach than grouping search requests into sessions allows for extraction of better refinement terms from query log files. © 2013 ASIS&T
Modeling Temporal Evidence from External Collections
Newsworthy events are broadcast through multiple mediums and prompt the
crowds to produce comments on social media. In this paper, we propose to
leverage on this behavioral dynamics to estimate the most relevant time periods
for an event (i.e., query). Recent advances have shown how to improve the
estimation of the temporal relevance of such topics. In this approach, we build
on two major novelties. First, we mine temporal evidences from hundreds of
external sources into topic-based external collections to improve the
robustness of the detection of relevant time periods. Second, we propose a
formal retrieval model that generalizes the use of the temporal dimension
across different aspects of the retrieval process. In particular, we show that
temporal evidence of external collections can be used to (i) infer a topic's
temporal relevance, (ii) select the query expansion terms, and (iii) re-rank
the final results for improved precision. Experiments with TREC Microblog
collections show that the proposed time-aware retrieval model makes an
effective and extensive use of the temporal dimension to improve search results
over the most recent temporal models. Interestingly, we observe a strong
correlation between precision and the temporal distribution of retrieved and
relevant documents.Comment: To appear in WSDM 201
Entity Query Feature Expansion Using Knowledge Base Links
Recent advances in automatic entity linking and knowledge base
construction have resulted in entity annotations for document and
query collections. For example, annotations of entities from large
general purpose knowledge bases, such as Freebase and the Google
Knowledge Graph. Understanding how to leverage these entity
annotations of text to improve ad hoc document retrieval is an open
research area. Query expansion is a commonly used technique to
improve retrieval effectiveness. Most previous query expansion
approaches focus on text, mainly using unigram concepts. In this
paper, we propose a new technique, called entity query feature
expansion (EQFE) which enriches the query with features from
entities and their links to knowledge bases, including structured
attributes and text. We experiment using both explicit query entity
annotations and latent entities. We evaluate our technique on TREC
text collections automatically annotated with knowledge base entity
links, including the Google Freebase Annotations (FACC1) data.
We find that entity-based feature expansion results in significant
improvements in retrieval effectiveness over state-of-the-art text
expansion approaches
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