8,344 research outputs found
Relevance-based Word Embedding
Learning a high-dimensional dense representation for vocabulary terms, also
known as a word embedding, has recently attracted much attention in natural
language processing and information retrieval tasks. The embedding vectors are
typically learned based on term proximity in a large corpus. This means that
the objective in well-known word embedding algorithms, e.g., word2vec, is to
accurately predict adjacent word(s) for a given word or context. However, this
objective is not necessarily equivalent to the goal of many information
retrieval (IR) tasks. The primary objective in various IR tasks is to capture
relevance instead of term proximity, syntactic, or even semantic similarity.
This is the motivation for developing unsupervised relevance-based word
embedding models that learn word representations based on query-document
relevance information. In this paper, we propose two learning models with
different objective functions; one learns a relevance distribution over the
vocabulary set for each query, and the other classifies each term as belonging
to the relevant or non-relevant class for each query. To train our models, we
used over six million unique queries and the top ranked documents retrieved in
response to each query, which are assumed to be relevant to the query. We
extrinsically evaluate our learned word representation models using two IR
tasks: query expansion and query classification. Both query expansion
experiments on four TREC collections and query classification experiments on
the KDD Cup 2005 dataset suggest that the relevance-based word embedding models
significantly outperform state-of-the-art proximity-based embedding models,
such as word2vec and GloVe.Comment: to appear in the proceedings of The 40th International ACM SIGIR
Conference on Research and Development in Information Retrieval (SIGIR '17
A comparative study of probabilistic and language models for information retrieval
Language models for information retrieval have received much attention in recent years, with many claims being made about their performance. However, previous studies evaluating the language modelling approach for information retrieval used different query sets and heterogeneous collections, which make reported results difficult to compare. This research is a broad-based study that evaluates language models against a variety of search tasks --- topic finding, named-page finding and topic distillation. The standard Text REtrieval Conference (TREC) methodology is used to compare language models to the probabilistic Okapi BM25 system. Using consistent parameter choices, we compare results of different language models on three different search tasks, multiple query sets and three different text collections. For ad hoc retrieval, the Dirichlet smoothing method was found to be significantly better than Okapi BM25, but for named-page finding Okapi BM25 was more effective than the language modelling methods. Optimal smoothing parameters for each method were found to be dependent on the collection and the query set. For longer queries, the language modelling approaches required more aggressive smoothing but they were found to be more effective than with shorter queries. The choice of smoothing method was also found to have a significant effect on the performance of language models for information retrieval
An Axiomatic Analysis of Diversity Evaluation Metrics: Introducing the Rank-Biased Utility Metric
Many evaluation metrics have been defined to evaluate the effectiveness
ad-hoc retrieval and search result diversification systems. However, it is
often unclear which evaluation metric should be used to analyze the performance
of retrieval systems given a specific task. Axiomatic analysis is an
informative mechanism to understand the fundamentals of metrics and their
suitability for particular scenarios. In this paper, we define a
constraint-based axiomatic framework to study the suitability of existing
metrics in search result diversification scenarios. The analysis informed the
definition of Rank-Biased Utility (RBU) -- an adaptation of the well-known
Rank-Biased Precision metric -- that takes into account redundancy and the user
effort associated to the inspection of documents in the ranking. Our
experiments over standard diversity evaluation campaigns show that the proposed
metric captures quality criteria reflected by different metrics, being suitable
in the absence of knowledge about particular features of the scenario under
study.Comment: Original version: 10 pages. Preprint of full paper to appear at
SIGIR'18: The 41st International ACM SIGIR Conference on Research &
Development in Information Retrieval, July 8-12, 2018, Ann Arbor, MI, USA.
ACM, New York, NY, US
Fast & Confident Probabilistic Categorization
We describe NRC's submission to the Anomaly Detection/Text Mining competition organised at the Text Mining Workshop 2007. This submission relies on a straightforward implementation of the probabilistic categoriser described in (Gaussier et al., ECIR'02). This categoriser is adapted to handle multiple labelling and a piecewise-linear confidence estimation layer is added to provide an estimate of the labelling confidence. This technique achieves a score of 1.689 on the test data
Embedding Web-based Statistical Translation Models in Cross-Language Information Retrieval
Although more and more language pairs are covered by machine translation
services, there are still many pairs that lack translation resources.
Cross-language information retrieval (CLIR) is an application which needs
translation functionality of a relatively low level of sophistication since
current models for information retrieval (IR) are still based on a
bag-of-words. The Web provides a vast resource for the automatic construction
of parallel corpora which can be used to train statistical translation models
automatically. The resulting translation models can be embedded in several ways
in a retrieval model. In this paper, we will investigate the problem of
automatically mining parallel texts from the Web and different ways of
integrating the translation models within the retrieval process. Our
experiments on standard test collections for CLIR show that the Web-based
translation models can surpass commercial MT systems in CLIR tasks. These
results open the perspective of constructing a fully automatic query
translation device for CLIR at a very low cost.Comment: 37 page
- …