| 000 | 03493nam a22005295i 4500 | ||
|---|---|---|---|
| 001 | 978-3-031-01910-4 | ||
| 003 | DE-He213 | ||
| 005 | 20240730164928.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 220601s2017 sz | s |||| 0|eng d | ||
| 020 |
_a9783031019104 _9978-3-031-01910-4 |
||
| 024 | 7 |
_a10.1007/978-3-031-01910-4 _2doi |
|
| 050 | 4 | _aQA76.9.D343 | |
| 072 | 7 |
_aUNF _2bicssc |
|
| 072 | 7 |
_aUYQE _2bicssc |
|
| 072 | 7 |
_aCOM021030 _2bisacsh |
|
| 072 | 7 |
_aUNF _2thema |
|
| 072 | 7 |
_aUYQE _2thema |
|
| 082 | 0 | 4 |
_a006.312 _223 |
| 100 | 1 |
_aLiu, Jialu. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _986709 |
|
| 245 | 1 | 0 |
_aPhrase Mining from Massive Text and Its Applications _h[electronic resource] / _cby Jialu Liu, Jingbo Shang, Jiawei Han. |
| 250 | _a1st ed. 2017. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2017. |
|
| 300 |
_aIX, 79 p. _bonline resource. |
||
| 336 |
_atext _btxt _2rdacontent |
||
| 337 |
_acomputer _bc _2rdamedia |
||
| 338 |
_aonline resource _bcr _2rdacarrier |
||
| 347 |
_atext file _bPDF _2rda |
||
| 490 | 1 |
_aSynthesis Lectures on Data Mining and Knowledge Discovery, _x2151-0075 |
|
| 505 | 0 | _aAcknowledgments -- Introduction -- Quality Phrase Mining with User Guidance -- Automated Quality Phrase Mining -- Phrase Mining Applications -- Bibliography -- Authors' Biographies . | |
| 520 | _aA lot of digital ink has been spilled on "big data" over the past few years. Most of this surge owes its origin to the various types of unstructured data in the wild, among which the proliferation of text-heavy data is particularly overwhelming, attributed to the daily use of web documents, business reviews, news, social posts, etc., by so many people worldwide.A core challenge presents itself: How can one efficiently and effectively turn massive, unstructured text into structured representation so as to further lay the foundation for many other downstream text mining applications? In this book, we investigated one promising paradigm for representing unstructured text, that is, through automatically identifying high-quality phrases from innumerable documents. In contrast to a list of frequent n-grams without proper filtering, users are often more interested in results based on variable-length phrases with certain semantics such as scientific concepts, organizations, slogans,and so on. We propose new principles and powerful methodologies to achieve this goal, from the scenario where a user can provide meaningful guidance to a fully automated setting through distant learning. This book also introduces applications enabled by the mined phrases and points out some promising research directions. | ||
| 650 | 0 |
_aData mining. _93907 |
|
| 650 | 0 |
_aStatistics . _931616 |
|
| 650 | 1 | 4 |
_aData Mining and Knowledge Discovery. _986712 |
| 650 | 2 | 4 |
_aStatistics. _914134 |
| 700 | 1 |
_aShang, Jingbo. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _986713 |
|
| 700 | 1 |
_aHan, Jiawei. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _92109 |
|
| 710 | 2 |
_aSpringerLink (Online service) _986716 |
|
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031007828 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031030383 |
| 830 | 0 |
_aSynthesis Lectures on Data Mining and Knowledge Discovery, _x2151-0075 _986717 |
|
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-01910-4 |
| 912 | _aZDB-2-SXSC | ||
| 942 | _cEBK | ||
| 999 |
_c85996 _d85996 |
||