1,930 research outputs found
Fully-unsupervised embeddings-based hypernym discovery
Funding: Supported in part by Sardegna Ricerche project OKgraph (CRP 120) and MIUR MIUR PRIN 2017 (2019-2022) project HOPE—High quality Open data Publishing and Enrichment.Peer reviewedPublisher PD
On vocabulary size of grammar-based codes
We discuss inequalities holding between the vocabulary size, i.e., the number
of distinct nonterminal symbols in a grammar-based compression for a string,
and the excess length of the respective universal code, i.e., the code-based
analog of algorithmic mutual information. The aim is to strengthen inequalities
which were discussed in a weaker form in linguistics but shed some light on
redundancy of efficiently computable codes. The main contribution of the paper
is a construction of universal grammar-based codes for which the excess lengths
can be bounded easily.Comment: 5 pages, accepted to ISIT 2007 and correcte
A novel alignment algorithm for effective web data extraction from singleton-item pages
Automatic data extraction from template pages is an essential task for data integration and data analysis. Most researches focus on data extraction from list pages. The problem of data alignment for singleton item pages (singleton pages for short), which contain detail information of a single item is less addressed and is more challenging because the number of data attributes to be aligned is much larger than list pages. In this paper, we propose a novel alignment algorithm working on leaf nodes from the DOM trees of input pages for singleton pages data extraction. The idea is to detect mandatory templates via the longest increasing sequence from the landmark equivalence class leaf nodes and recursively apply the same procedure to each segment divided by mandatory templates. By this divide-and-conquer approach, we are able to efficiently conduct local alignment for each segment, while effectively handle multi-order attribute-value pairs with a two-pass procedure. The results show that the proposed approach (called Divide-and-Conquer Alignment, DCA) outperforms TEX (Sleiman and Corchuelo 2013) and WEIR (Bronzi et al. VLDB 6(10):805�816 2013) 2% and 12% on selected items of TEX and WEIR dataset respectively. The improvement is more obvious in terms of full schema evaluation, with 0.95 (DCA) versus 0.63 (TEX) F-measure, on 26 websites from TEX and EXALG (Arasu and Molina 2003)
The Google Similarity Distance
Words and phrases acquire meaning from the way they are used in society, from
their relative semantics to other words and phrases. For computers the
equivalent of `society' is `database,' and the equivalent of `use' is `way to
search the database.' We present a new theory of similarity between words and
phrases based on information distance and Kolmogorov complexity. To fix
thoughts we use the world-wide-web as database, and Google as search engine.
The method is also applicable to other search engines and databases. This
theory is then applied to construct a method to automatically extract
similarity, the Google similarity distance, of words and phrases from the
world-wide-web using Google page counts. The world-wide-web is the largest
database on earth, and the context information entered by millions of
independent users averages out to provide automatic semantics of useful
quality. We give applications in hierarchical clustering, classification, and
language translation. We give examples to distinguish between colors and
numbers, cluster names of paintings by 17th century Dutch masters and names of
books by English novelists, the ability to understand emergencies, and primes,
and we demonstrate the ability to do a simple automatic English-Spanish
translation. Finally, we use the WordNet database as an objective baseline
against which to judge the performance of our method. We conduct a massive
randomized trial in binary classification using support vector machines to
learn categories based on our Google distance, resulting in an a mean agreement
of 87% with the expert crafted WordNet categories.Comment: 15 pages, 10 figures; changed some text/figures/notation/part of
theorem. Incorporated referees comments. This is the final published version
up to some minor changes in the galley proof
On the Vocabulary of Grammar-Based Codes and the Logical Consistency of Texts
The article presents a new interpretation for Zipf-Mandelbrot's law in
natural language which rests on two areas of information theory. Firstly, we
construct a new class of grammar-based codes and, secondly, we investigate
properties of strongly nonergodic stationary processes. The motivation for the
joint discussion is to prove a proposition with a simple informal statement: If
a text of length describes independent facts in a repetitive way
then the text contains at least different words, under
suitable conditions on . In the formal statement, two modeling postulates
are adopted. Firstly, the words are understood as nonterminal symbols of the
shortest grammar-based encoding of the text. Secondly, the text is assumed to
be emitted by a finite-energy strongly nonergodic source whereas the facts are
binary IID variables predictable in a shift-invariant way.Comment: 24 pages, no figure
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