Vector representations and vector space modeling (VSM) play a central role in modern machine learning. We propose a novel approach to ‘vector similarity searching’ over dense semantic representations of words and documents that can be deployed on top of traditional inverted-index-based fulltext engines, taking advantage of their robustness, stability, scalability and ubiquity. We show that this approach allows the indexing and querying of dense vectors in text domains. This opens up exciting avenues for major efficiency gains, along with simpler deployment, scaling and monitoring. The end result is a fast and scalable vector database with a tunable trade-off between vector search performance and quality, backed by a standard fulltext engine such as Elasticsearch. We empirically demonstrate its querying performance and quality by applying this solution to the task of semantic searching over a dense vector representation of the entire English Wikipedia.

This work describes the process of creation of a 70 billion word text corpus of English. We used an existing language resource, namely the ClueWeb09 dataset, as source for the corpus data. Processing such a vast amount of data presented several challenges, mainly associated with pre-processing (boilerplate cleaning, text de-duplication) and post-processing (indexing for efficient corpus querying using the CQL -- Corpus Query Language) steps. In this paper we explain how we tackled them: we describe the tools used for boilerplate cleaning (jusText) and for de-duplication (onion) that was performed not only on full (document-level) duplicates but also on the level of near-duplicate texts. Moreover we show the impact of each of the performed pre-processing steps on the final corpus size. Furthermore we show how effective parallelization of the corpus indexation procedure was employed within the Manatee corpus management system and during computation of word sketches (one-page, automatic, corpus-derived summaries of a word's grammatical and collocational behaviour) from the resulting corpus.

For many languages there are no large, general-language corpora available. Until the web, all but the institutions could do little but shake their heads in dismay as corpus-building was long, slow and expensive. But with the advent of the Web it can be highly automated and thereby fast and inexpensive. We have developed a ‘corpus factory’ where we build large corpora. In this paper we describe the method we use, and how it has worked, and how various problems were solved, for eight languages: Dutch, Hindi, Indonesian, Norwegian, Swedish, Telugu, Thai and Vietnamese. We use the BootCaT method: we take a set of 'seed words' for the language from Wikipedia. Then, several hundred times over, we * randomly select three or four of the seed words * send as a query to Google or Yahoo or Bing, which returns a 'search hits' page * gather the pages that Google or Yahoo point to and save the text. This forms the corpus, which we then * 'clean' (to remove navigation bars, advertisements etc) * remove duplicates * tokenise and (if tools are available) lemmatise and part-of-speech tag * load into our corpus query tool, the Sketch Engine The corpora we have developed are available for use in the Sketch Engine corpus query tool.

Word sketches are part of the Sketch Engine corpus query system. They represent automatic, corpus-derived summaries of the words’ grammatical and collocational behaviour. Besides the corpus itself, word sketches require a sketch grammar, a regular expression-based shallow grammar over the part-of-speech tags, to extract evidence for the properties of the targeted words from the corpus. The paper presents a sketch grammar for German, a language which is not strictly configurational and which shows a considerable amount of case syncretism, and evaluates its accuracy, which has not been done for other sketch grammars. The evaluation focuses on NP case as a crucial part of the German grammar. We present various versions of NP definitions, so demonstrating the influence of grammar detail on precision and recall.

We have analyzed the SPEX algorithm by Bernstein and Zobel (2004) for detecting co-derivative documents using duplicate n-grams. Although we totally agree with the claim that not using unique n-grams can greatly increase the efficiency and scalability of the process of detecting co-derivative documents, we have found serious bottlenecks in the way SPEX finds the duplicate n-grams. While the memory requirements for computing co-derivative documents can be reduced to up to 1% by only using duplicate n-grams, SPEX needs about 40 times more memory for computing the list of duplicate n-grams itself. Therefore the memory requirements of the whole process are not reduced enough to make the algorithm practical for very large collections. We propose a solution for this problem using an external sort with the suffix array in-memory sorting and temporary file compression. The proposed algorithm for computing duplicate n-grams uses a fixed amount of memory for any input size.

Current Semantic Web implementation efforts pose a number of challenges. One of the big ones among them is development and evolution of specific resources --- the ontologies --- as a base for representation of the meaning of the web. This paper deals with the automatic acquisition of semantic relations from the text of scientific publications (journal articles, conference papers, project descriptions, etc.). We also describe the process of building of corresponding ontological resources and their application for semi--automatic generation of scientific portals. Extracted relations and ontologies are crucial for the structuring of the information at the portal pages, automatic classification of the presented documents as well as for personalisation at the presentation level. Besides a general description of the portal generating system, we give also a detailed overview of extraction of semantic relations in the form of a domain--specific ontology. The overview consists of presentation of an architecture of the ontology extraction system, description of methods used for mining of semantic relations and analysis of selected results and examples.