The paper introduces a framework for representation and acquisition of knowledge emerging from large samples of textual data. We utilise a tensor-based, distributional representation of simple statements extracted from text, and show how one can use the representation to infer emergent knowledge patterns from the textual data in an unsupervised manner. Examples of the patterns we investigate in the paper are implicit term relationships or conjunctive IF-THEN rules. To evaluate the practical relevance of our approach, we apply it to annotation of life science articles with terms from MeSH (a controlled biomedical vocabulary and thesaurus).
By comparing the row vectors in corpus tensor matricisations, one essentially compares the meaning of the corresponding label terms, as it is emerging from the underlying data. For exploring the matricised perspectives, one can use linear algebra methods that have been proven to work by countless successful applications to vector space analysis in the last couple of decades @cite_6 @cite_11 @cite_7 . Large feature spaces can be reliably reduced to more manageable and less noisy number of dimensions by techniques like singular value decomposition or random indexing (see http: en.wikipedia.org wiki Dimension_reduction ). After the (optional) dimensionality reduction, the perspective vectors can be compared in a well-founded manner by measures like cosine similarity (see http: en.wikipedia.org wiki Cosine_similarity ), as illustrated in Example .
@cite_7 @cite_6 @cite_11
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In a document retrieval, or other pattern matching environment where stored entities (documents) are compared with each other or with incoming patterns (search requests), it appears that the best indexing (property) space is one where each entity lies as far away from the others as possible; in these circumstances the value of an indexing system may be expressible as a function of the density of the object space; in particular, retrieval performance may correlate inversely with space density. An approach based on space density computations is used to choose an optimum indexing vocabulary for a collection of documents. Typical evaluation results are shown, demonstating the usefulness of the model. A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries. The particular technique used is singular-value decomposition, in which a large term by document matrix is decomposed into a set of ca. 100 orthogonal factors from which the original matrix can be approximated by linear combination. Documents are represented by ca. 100 item vectors of factor weights. Queries are represented as pseudo-document vectors formed from weighted combinations of terms, and documents with supra-threshold cosine values are returned. initial tests find this completely automatic method for retrieval to be promising.