4,657 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
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
Technology Assisted Reviews: Finding the Last Few Relevant Documents by Asking Yes/No Questions to Reviewers
The goal of a technology-assisted review is to achieve high recall with low
human effort. Continuous active learning algorithms have demonstrated good
performance in locating the majority of relevant documents in a collection,
however their performance is reaching a plateau when 80\%-90\% of them has been
found. Finding the last few relevant documents typically requires exhaustively
reviewing the collection. In this paper, we propose a novel method to identify
these last few, but significant, documents efficiently. Our method makes the
hypothesis that entities carry vital information in documents, and that
reviewers can answer questions about the presence or absence of an entity in
the missing relevance documents. Based on this we devise a sequential Bayesian
search method that selects the optimal sequence of questions to ask. The
experimental results show that our proposed method can greatly improve
performance requiring less reviewing effort.Comment: This paper is accepted by SIGIR 201
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
CLEF 2017 NewsREEL Overview: Offline and Online Evaluation of Stream-based News Recommender Systems
The CLEF NewsREEL challenge allows researchers to evaluate news
recommendation algorithms both online (NewsREEL Live) and offline (News-
REEL Replay). Compared with the previous year NewsREEL challenged participants
with a higher volume of messages and new news portals. In the 2017
edition of the CLEF NewsREEL challenge a wide variety of new approaches have
been implemented ranging from the use of existing machine learning frameworks,
to ensemble methods to the use of deep neural networks. This paper gives an
overview over the implemented approaches and discusses the evaluation results.
In addition, the main results of Living Lab and the Replay task are explained
Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology Based Representations
We investigate the pertinence of methods from algebraic topology for text
data analysis. These methods enable the development of
mathematically-principled isometric-invariant mappings from a set of vectors to
a document embedding, which is stable with respect to the geometry of the
document in the selected metric space. In this work, we evaluate the utility of
these topology-based document representations in traditional NLP tasks,
specifically document clustering and sentiment classification. We find that the
embeddings do not benefit text analysis. In fact, performance is worse than
simple techniques like , indicating that the geometry of the
document does not provide enough variability for classification on the basis of
topic or sentiment in the chosen datasets.Comment: 5 pages, 3 figures. Rep4NLP workshop at ACL 201
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