The BigGrams: the semi-supervised information extraction system from HTML: an improvement in the wrapper induction

The aim of this study is to propose an information extraction system, called BigGrams, which is able to retrieve relevant and structural information (relevant phrases, keywords) from semi-structural web pages, i.e. HTML documents. For this purpose, a novel semi-supervised wrappers induction algorithm has been developed and embedded in the BigGrams system. The wrappers induction algorithm utilizes a formal concept analysis to induce information extraction patterns. Also, in this article, the author (1) presents the impact of the configuration of the information extraction system components on information extraction results and (2) tests the boosting mode of this system. Based on empirical research, the author established that the proposed taxonomy of seeds and the HTML tags level analysis, with appropriate pre-processing, improve information extraction results. Also, the boosting mode works well when certain requirements are met, i.e. when well-diversified input data are ensured.

Categorization of Multilingual Scientific Documents by a Compound Classification System

The aim of this study was to propose a classification method for documents that include simultaneously text parts in various languages. For this purpose, we constructed a three-leveled classification system. On its first level, a data processing module prepares a suitable vector space model. Next, in the middle tier, a set of monolingual or multilingual classifiers assigns the probabilities of belonging each document or its parts to all possible categories. The models are trained by using Multinomial Naive Bayes and Long Short-Term Memory algorithms. Finally, in the last component, a multilingual decision module assigns a target class to each document. The module is built on a logistic regression classifier, which as the inputs receives probabilities produced by the classifiers. The system has been verified experimentally. According to the reported results, it can be assumed that the proposed system can deal with textual documents which content is composed of many languages at the same time. Therefore, the system can be useful in the automatic organizing of multilingual publications or other documents.

Article – Detection of the Innovative Logotypes on the Web Pages

The aim of this study was to describe a found method for detection of logotypes that indicate innovativeness of companies, where the images originate from their Internet domains. For this purpose, we elaborated a system that covers a supervised and heuristic approach to construct a reference dataset for each logotype category that is utilized by the logistic regression classifiers to recognize a logotype category. We proposed the approach that uses one-versus-the-rest learning strategy to learn the logistic regression classification models to recognize the classes of the innovative logotypes. Thanks to this we can detect whether a given company’s Internet domain contains an innovative logotype or not. More- over, we find a way to construct a simple and small dimension of feature space that is utilized by the image recognition process. The proposed feature space of logotype classification models is based on the weights of images similarity and the textual data of the images that are received from HTMLs ALT tags.

Article – A Diversified Classification Committee for Recognition of Innovative Internet Domains

The objective of this paper was to propose a classification method of innovative domains on the Internet. The proposed approach helped to estimate whether companies are innovative or not through analyzing their web pages. A Naïve Bayes classification committee was used as the classification system of the domains. The classifiers in the committee were based concurrently on Bernoulli and Multinomial feature distribution models, which were selected depending on the diversity of input data. Moreover, the information retrieval procedures were applied to find such documents in domains that most likely indicate innovativeness. The proposed methods have been verified experimentally. The results have shown that the diversified classification committee combined with the information retrieval approach in the preprocessing phase boosts the classification quality of domains that may represent innovative companies. This approach may be applied to other classification tasks.

Article – The hybrid decision support system for Fire Service – chosen project’s problems

This article describes the process of designing a hybrid decision support system HSWD for the Fire Service. This designing process realize a methodology of design for trustworthy software – DFTS. In this article describes chosen project problems and their solution on the first stage of proposed design process.

Article – Language-Independent Information Extraction Based on Formal Concept Analysis

This paper proposes application of Formal Concept Analysis (FCA) in creating character-level information extraction patterns and presents BigGrams: a prototype of a languageindependent information extraction system. The main goal of the system is to recognise and to extract of named entities belonging to some semantic classes (e.g. cars, actors, pop-stars, etc.) from semi structured text (web page documents).

Article – Review of methods and text data mining techniques

This article describes the author’s classification of the methods and techniques of textual data mining. In this article also describes the currently available methods and sauces representation of textual data and their processing techniques. Also conducted a discussion on the processing of text documents using the presented methods. This paper also discussed the possibilities and limitations of individual methods to process the presented text documents.

Article – Proposition of hybrid process model semi structured description of event from fire services rescues operation

The article „Proposition of hybrid process model semi structured description of event from fire services rescues operation” describes a review of actual developed knowledge representation and case representation for fire services cases based reasoning system. The article also describes a method of processing the cases of events. This processing method based on classification and information retrieval.

Article – Crowdsourcing in rescue fire service – proposed application

Few days ago a SIMIS magazine publicated my article Crowdsourcing in rescue fire service – proposed application. In this article I describes the proposal to apply crowdsourcing in Polish rescue fire service. This article also describes basic principles for implementing an crowdsourcing information platform in rescue fire service as well as the scheme of its implementation. Of this paper also to I describes the genesis of this proposal related to the evaluation of research conducted by the author on text mining analysis and extraction of information in the design of information systems.

Resolved problem with revoIPC 1.0-3 and new g++

Few days ago I had a installation problem package revoIPC 1.0-3 and doSMP 1.0.1. When I was compiled revoIPC 1.0-3, compilator returned this error:

g++ -I/usr/local/lib64/R/include  -I/usr/local/include   -I . -fpic
-g -O2 -c -o interface.o
In file included from ./boost/interprocess/detail/

                from ./boost/interprocess/mapped_region.hpp:20,
                from boost/interprocess/managed_mapped_file.hpp:20,
                from queue.h:17,
./boost/interprocess/detail/iterators.hpp:352:15: error: reference
‚m_value’ cannot be declared ‚mutable’ [-fpermissive]
make: *** [interface.o] Błąd 1
ERROR: compilation failed for package ‚revoIPC’

Rich Calaway from helped me quickly and sent solution. The patch with fix this problem is a:

„… go to src/boost/interprocess/detail/iterators.hpp and comment out lines 341-353”

Thanks for this advice we can compile revoIPC 1.0-3 and use it in with doSMP 1.0.1. Just one thinks, if we use doSMP 1.0.1 in new revoIPC 1.0-4 and we try execute this program:

rmSessions(all.names = TRUE)
w <- startWorkers(2)
foreach(i=1:3) %dopar% sqrt(i)

R return this error:

> foreach(i=1:3) %dopar% sqrt(i)  *** caught segfault ***
address 0x7fd0e562f58c, cause ‚memory not mapped’ Traceback:
 1: .Call(„returnResult”, q, t$task, serialize(res, NULL))
 2: ipcTaskReturnResult(taskq, taskchunk, resultchunk)
 3: doSMP:::workerLoop(qname, rank, verbose, out)
aborting …

Rich Calaway helped me fixed this problem to. I don’t check it but if someone want to use revoIPC 1.0-4 must:

„… remove the PKG_CPPFLAGS=-DNDEBUG line from in the src directory.”

Good luck.