5,608 research outputs found
Tree-based Focused Web Crawling with Reinforcement Learning
A focused crawler aims at discovering as many web pages relevant to a target
topic as possible, while avoiding irrelevant ones. Reinforcement Learning (RL)
has been utilized to optimize focused crawling. In this paper, we propose TRES,
an RL-empowered framework for focused crawling. We model the crawling
environment as a Markov Decision Process, which the RL agent aims at solving by
determining a good crawling strategy. Starting from a few human provided
keywords and a small text corpus, that are expected to be relevant to the
target topic, TRES follows a keyword set expansion procedure, which guides
crawling, and trains a classifier that constitutes the reward function. To
avoid a computationally infeasible brute force method for selecting a best
action, we propose Tree-Frontier, a decision-tree-based algorithm that
adaptively discretizes the large state and action spaces and finds only a few
representative actions. Tree-Frontier allows the agent to be likely to select
near-optimal actions by being greedy over selecting the best representative
action. Experimentally, we show that TRES significantly outperforms
state-of-the-art methods in terms of harvest rate (ratio of relevant pages
crawled), while Tree-Frontier reduces by orders of magnitude the number of
actions needed to be evaluated at each timestep
Methodologies for the Automatic Location of Academic and Educational Texts on the Internet
Traditionally online databases of web resources have been compiled by a human editor, or though the submissions of authors or interested parties. Considerable resources are needed to maintain a constant level of input and relevance in the face of increasing material quantity and quality, and much of what is in databases is of an ephemeral nature. These pressures dictate that many databases stagnate after an initial period of enthusiastic data entry. The solution to this problem would seem to be the automatic harvesting of resources, however, this process necessitates the automatic classification of resources as âappropriateâ to a given database, a problem only solved by complex text content analysis.
This paper outlines the component methodologies necessary to construct such an automated harvesting system, including a number of novel approaches. In particular this paper looks at the specific problems of automatically identifying academic research work and Higher Education pedagogic materials. Where appropriate, experimental data is presented from searches in the field of Geography as well as the Earth and Environmental Sciences. In addition, appropriate software is reviewed where it exists, and future directions are outlined
Methodologies for the Automatic Location of Academic and Educational Texts on the Internet
Traditionally online databases of web resources have been compiled by a human editor, or though the submissions of authors or interested parties. Considerable resources are needed to maintain a constant level of input and relevance in the face of increasing material quantity and quality, and much of what is in databases is of an ephemeral nature. These pressures dictate that many databases stagnate after an initial period of enthusiastic data entry. The solution to this problem would seem to be the automatic harvesting of resources, however, this process necessitates the automatic classification of resources as âappropriateâ to a given database, a problem only solved by complex text content analysis.
This paper outlines the component methodologies necessary to construct such an automated harvesting system, including a number of novel approaches. In particular this paper looks at the specific problems of automatically identifying academic research work and Higher Education pedagogic materials. Where appropriate, experimental data is presented from searches in the field of Geography as well as the Earth and Environmental Sciences. In addition, appropriate software is reviewed where it exists, and future directions are outlined
Towards using web-crawled data for domain adaptation in statistical machine translation
This paper reports on the ongoing work focused on domain adaptation of statistical machine translation using domain-speciïŹc data obtained by domain-focused web crawling. We present a strategy for crawling monolingual and parallel data and their exploitation for testing, language modelling, and system tuning in a phrase--based machine translation framework. The proposed approach is evaluated on the domains of Natural Environment and Labour Legislation and two language
pairs: EnglishâFrench and EnglishâGreek
Profiling Web Archive Coverage for Top-Level Domain and Content Language
The Memento aggregator currently polls every known public web archive when
serving a request for an archived web page, even though some web archives focus
on only specific domains and ignore the others. Similar to query routing in
distributed search, we investigate the impact on aggregated Memento TimeMaps
(lists of when and where a web page was archived) by only sending queries to
archives likely to hold the archived page. We profile twelve public web
archives using data from a variety of sources (the web, archives' access logs,
and full-text queries to archives) and discover that only sending queries to
the top three web archives (i.e., a 75% reduction in the number of queries) for
any request produces the full TimeMaps on 84% of the cases.Comment: Appeared in TPDL 201
Determining the Characteristic Vocabulary for a Specialized Dictionary using Word2vec and a Directed Crawler
Specialized dictionaries are used to understand concepts in specific domains,
especially where those concepts are not part of the general vocabulary, or
having meanings that differ from ordinary languages. The first step in creating
a specialized dictionary involves detecting the characteristic vocabulary of
the domain in question. Classical methods for detecting this vocabulary involve
gathering a domain corpus, calculating statistics on the terms found there, and
then comparing these statistics to a background or general language corpus.
Terms which are found significantly more often in the specialized corpus than
in the background corpus are candidates for the characteristic vocabulary of
the domain. Here we present two tools, a directed crawler, and a distributional
semantics package, that can be used together, circumventing the need of a
background corpus. Both tools are available on the web
Termhood-based Comparability Metrics of Comparable Corpus in Special Domain
Cross-Language Information Retrieval (CLIR) and machine translation (MT)
resources, such as dictionaries and parallel corpora, are scarce and hard to
come by for special domains. Besides, these resources are just limited to a few
languages, such as English, French, and Spanish and so on. So, obtaining
comparable corpora automatically for such domains could be an answer to this
problem effectively. Comparable corpora, that the subcorpora are not
translations of each other, can be easily obtained from web. Therefore,
building and using comparable corpora is often a more feasible option in
multilingual information processing. Comparability metrics is one of key issues
in the field of building and using comparable corpus. Currently, there is no
widely accepted definition or metrics method of corpus comparability. In fact,
Different definitions or metrics methods of comparability might be given to
suit various tasks about natural language processing. A new comparability,
namely, termhood-based metrics, oriented to the task of bilingual terminology
extraction, is proposed in this paper. In this method, words are ranked by
termhood not frequency, and then the cosine similarities, calculated based on
the ranking lists of word termhood, is used as comparability. Experiments
results show that termhood-based metrics performs better than traditional
frequency-based metrics
Applying digital content management to support localisation
The retrieval and presentation of digital content such as that on the World Wide Web (WWW) is a substantial area of research. While recent years have seen huge expansion in the size of web-based archives that can be searched efficiently by commercial search engines, the presentation of potentially relevant content is still limited to ranked document lists represented by simple text snippets or image keyframe surrogates. There is expanding interest in techniques to personalise the presentation of content to improve the richness and effectiveness of the user experience. One of the most significant challenges to achieving this is the increasingly multilingual nature of this data, and the need to provide suitably localised responses to users based on this content. The Digital Content Management (DCM) track of the Centre for Next Generation Localisation (CNGL) is seeking to develop technologies to support advanced personalised access and presentation of information by combining elements from the existing research areas of Adaptive Hypermedia and Information Retrieval. The combination of these technologies is intended to produce significant improvements in the way users access information. We review key features of these technologies and introduce early ideas for how these technologies can support localisation and localised content before concluding with some impressions of future directions in DCM
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