2,937 research outputs found
Large scale biomedical texts classification: a kNN and an ESA-based approaches
With the large and increasing volume of textual data, automated methods for
identifying significant topics to classify textual documents have received a
growing interest. While many efforts have been made in this direction, it still
remains a real challenge. Moreover, the issue is even more complex as full
texts are not always freely available. Then, using only partial information to
annotate these documents is promising but remains a very ambitious issue.
MethodsWe propose two classification methods: a k-nearest neighbours
(kNN)-based approach and an explicit semantic analysis (ESA)-based approach.
Although the kNN-based approach is widely used in text classification, it needs
to be improved to perform well in this specific classification problem which
deals with partial information. Compared to existing kNN-based methods, our
method uses classical Machine Learning (ML) algorithms for ranking the labels.
Additional features are also investigated in order to improve the classifiers'
performance. In addition, the combination of several learning algorithms with
various techniques for fixing the number of relevant topics is performed. On
the other hand, ESA seems promising for this classification task as it yielded
interesting results in related issues, such as semantic relatedness computation
between texts and text classification. Unlike existing works, which use ESA for
enriching the bag-of-words approach with additional knowledge-based features,
our ESA-based method builds a standalone classifier. Furthermore, we
investigate if the results of this method could be useful as a complementary
feature of our kNN-based approach.ResultsExperimental evaluations performed on
large standard annotated datasets, provided by the BioASQ organizers, show that
the kNN-based method with the Random Forest learning algorithm achieves good
performances compared with the current state-of-the-art methods, reaching a
competitive f-measure of 0.55% while the ESA-based approach surprisingly
yielded reserved results.ConclusionsWe have proposed simple classification
methods suitable to annotate textual documents using only partial information.
They are therefore adequate for large multi-label classification and
particularly in the biomedical domain. Thus, our work contributes to the
extraction of relevant information from unstructured documents in order to
facilitate their automated processing. Consequently, it could be used for
various purposes, including document indexing, information retrieval, etc.Comment: Journal of Biomedical Semantics, BioMed Central, 201
Query-driven document partitioning and collection selection
Abstract — We present a novel strategy to partition a document collection onto several servers and to perform effective collection selection. The method is based on the analysis of query logs. We proposed a novel document representation called query-vectors model. Each document is represented as a list recording the queries for which the document itself is a match, along with their ranks. To both partition the collection and build the collection selection function, we co-cluster queries and documents. The document clusters are then assigned to the underlying IR servers, while the query clusters represent queries that return similar results, and are used for collection selection. We show that this document partition strategy greatly boosts the performance of standard collection selection algorithms, including CORI, w.r.t. a round-robin assignment. Secondly, we show that performing collection selection by matching the query to the existing query clusters and successively choosing only one server, we reach an average precision-at-5 up to 1.74 and we constantly improve CORI precision of a factor between 11 % and 15%. As a side result we show a way to select rarely asked-for documents. Separating these documents from the rest of the collection allows the indexer to produce a more compact index containing only relevant documents that are likely to be requested in the future. In our tests, around 52 % of the documents (3,128,366) are not returned among the first 100 top-ranked results of any query. I
Exploiting association rules and ontology for semantic document indexing
International audienceThis paper describes a novel approach for document indexing based on the discovery of contextual semantic relations between concepts. The concepts are first extracted from WordNet ontology. Then we propose to extend and to use the association rules technique in order to discover conditional relations between concepts. Finally, concepts and related contextual relations are organized into a conditional graph
Extraction of temporal networks from term co-occurrences in online textual sources
A stream of unstructured news can be a valuable source of hidden relations between different entities, such as financial institutions, countries, or persons. We present an approach to continuously collect online news, recognize relevant entities in them, and extract time-varying networks. The nodes of the network are the entities, and the links are their co-occurrences. We present a method to estimate the significance of co-occurrences, and a benchmark model against which their robustness is evaluated. The approach is applied to a large set of financial news, collected over a period of two years. The entities we consider are 50 countries which issue sovereign bonds, and which are insured by Credit Default Swaps (CDS) in turn. We compare the country co-occurrence networks to the CDS networks constructed from the correlations between the CDS. The results show relatively small, but significant overlap between the networks extracted from the news and those from the CDS correlations
Terminology-based Text Embedding for Computing Document Similarities on Technical Content
We propose in this paper a new, hybrid document embedding approach in order
to address the problem of document similarities with respect to the technical
content. To do so, we employ a state-of-the-art graph techniques to first
extract the keyphrases (composite keywords) of documents and, then, use them to
score the sentences. Using the ranked sentences, we propose two approaches to
embed documents and show their performances with respect to two baselines. With
domain expert annotations, we illustrate that the proposed methods can find
more relevant documents and outperform the baselines up to 27% in terms of
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