24,551 research outputs found
End-to-end Learning for Short Text Expansion
Effectively making sense of short texts is a critical task for many real
world applications such as search engines, social media services, and
recommender systems. The task is particularly challenging as a short text
contains very sparse information, often too sparse for a machine learning
algorithm to pick up useful signals. A common practice for analyzing short text
is to first expand it with external information, which is usually harvested
from a large collection of longer texts. In literature, short text expansion
has been done with all kinds of heuristics. We propose an end-to-end solution
that automatically learns how to expand short text to optimize a given learning
task. A novel deep memory network is proposed to automatically find relevant
information from a collection of longer documents and reformulate the short
text through a gating mechanism. Using short text classification as a
demonstrating task, we show that the deep memory network significantly
outperforms classical text expansion methods with comprehensive experiments on
real world data sets.Comment: KDD'201
Query Expansion for Survey Question Retrieval in the Social Sciences
In recent years, the importance of research data and the need to archive and
to share it in the scientific community have increased enormously. This
introduces a whole new set of challenges for digital libraries. In the social
sciences typical research data sets consist of surveys and questionnaires. In
this paper we focus on the use case of social science survey question reuse and
on mechanisms to support users in the query formulation for data sets. We
describe and evaluate thesaurus- and co-occurrence-based approaches for query
expansion to improve retrieval quality in digital libraries and research data
archives. The challenge here is to translate the information need and the
underlying sociological phenomena into proper queries. As we can show retrieval
quality can be improved by adding related terms to the queries. In a direct
comparison automatically expanded queries using extracted co-occurring terms
can provide better results than queries manually reformulated by a domain
expert and better results than a keyword-based BM25 baseline.Comment: to appear in Proceedings of 19th International Conference on Theory
and Practice of Digital Libraries 2015 (TPDL 2015
A study on the use of summaries and summary-based query expansion for a question-answering task
In this paper we report an initial study on the effectiveness of query-biased summaries for a question answering task. Our summarisation system presents searchers with short summaries of documents. The summaries are composed of a set of sentences that highlight the main points of the document as they relate to the query. These summaries are also used as evidence for a query expansion algorithm to test the use of summaries as evidence for interactive and automatic query expansion. We present the results of a set of experiments to test these two approaches and discuss the relative success of these techniques
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
On the Impact of Entity Linking in Microblog Real-Time Filtering
Microblogging is a model of content sharing in which the temporal locality of
posts with respect to important events, either of foreseeable or unforeseeable
nature, makes applica- tions of real-time filtering of great practical
interest. We propose the use of Entity Linking (EL) in order to improve the
retrieval effectiveness, by enriching the representation of microblog posts and
filtering queries. EL is the process of recognizing in an unstructured text the
mention of relevant entities described in a knowledge base. EL of short pieces
of text is a difficult task, but it is also a scenario in which the information
EL adds to the text can have a substantial impact on the retrieval process. We
implement a start-of-the-art filtering method, based on the best systems from
the TREC Microblog track realtime adhoc retrieval and filtering tasks , and
extend it with a Wikipedia-based EL method. Results show that the use of EL
significantly improves over non-EL based versions of the filtering methods.Comment: 6 pages, 1 figure, 1 table. SAC 2015, Salamanca, Spain - April 13 -
17, 201
CRL at Ntcir2
We have developed systems of two types for NTCIR2. One is an enhenced version
of the system we developed for NTCIR1 and IREX. It submitted retrieval results
for JJ and CC tasks. A variety of parameters were tried with the system. It
used such characteristics of newspapers as locational information in the CC
tasks. The system got good results for both of the tasks. The other system is a
portable system which avoids free parameters as much as possible. The system
submitted retrieval results for JJ, JE, EE, EJ, and CC tasks. The system
automatically determined the number of top documents and the weight of the
original query used in automatic-feedback retrieval. It also determined
relevant terms quite robustly. For EJ and JE tasks, it used document expansion
to augment the initial queries. It achieved good results, except on the CC
tasks.Comment: 11 pages. Computation and Language. This paper describes our results
of information retrieval in the NTCIR2 contes
Word-Entity Duet Representations for Document Ranking
This paper presents a word-entity duet framework for utilizing knowledge
bases in ad-hoc retrieval. In this work, the query and documents are modeled by
word-based representations and entity-based representations. Ranking features
are generated by the interactions between the two representations,
incorporating information from the word space, the entity space, and the
cross-space connections through the knowledge graph. To handle the
uncertainties from the automatically constructed entity representations, an
attention-based ranking model AttR-Duet is developed. With back-propagation
from ranking labels, the model learns simultaneously how to demote noisy
entities and how to rank documents with the word-entity duet. Evaluation
results on TREC Web Track ad-hoc task demonstrate that all of the four-way
interactions in the duet are useful, the attention mechanism successfully
steers the model away from noisy entities, and together they significantly
outperform both word-based and entity-based learning to rank systems
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