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1
Text Processing
Rong Jin
2
Indexing Process
3
Processing Text
Converting documents to index terms Why?
Matching the exact string of characters typed by the user is too restrictive i.e., it doesn’t work very well in terms of effectiveness
Not all words are of equal value in a search Sometimes not clear where words begin and end
Not even clear what a word is in some languages (e.g., Chinese, Korean)
4
Tokenizing
Forming words from sequence of characters Surprisingly complex in English, can be
harder in other languages Early IR systems:
any sequence of alphanumeric characters of length 3 or more
terminated by a space or other special character upper-case changed to lower-case
5
Tokenizing
Example: “Bigcorp's 2007 bi-annual report showed profits
rose 10%.” becomes
1. bigcorp s 2007 bi annual report showed profits rose 10
2. bigcorp 2007 annual report showed profits rose
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Tokenizing
Example: “Bigcorp's 2007 bi-annual report showed profits
rose 10%.” becomes “bigcorp 2007 annual report showed profits rose”
Too simple for search applications or even large-scale experiments
Why? Too much information lost Small decisions in tokenizing can have major
impact on effectiveness of some queries
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Tokenizing Problems
Small words can be important in some queries, usually in combinations
xp, ma, pm, ben e king, el paso, master p, gm, j lo, world war II
Both hyphenated and non-hyphenated forms of many words are common Sometimes hyphen is not needed
e-bay, wal-mart, active-x, cd-rom, t-shirts At other times, hyphens should be considered either
as part of the word or a word separator winston-salem, mazda rx-7, e-cards, pre-diabetes, spanish-
speaking
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Tokenizing Problems Special characters are an important part of tags,
URLs, code in documents Capitalized words can have different meaning
from lower case words Bush, Apple
Apostrophes can be a part of a word, a part of a possessive, or just a mistake rosie o'donnell, can't, don't, 80's, 1890's, men's straw
hats, master's degree, england's ten largest cities, shriner's
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Tokenizing Problems
Numbers can be important, including decimals nokia 3250, top 10 courses, united 93, quicktime
6.5 pro, 92.3 the beat, 288358 Periods can occur in numbers, abbreviations,
URLs, ends of sentences, and other situations I.B.M., Ph.D., cs.umass.edu, F.E.A.R.
Note: tokenizing steps for queries must be identical to steps for documents
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Tokenizing: SMART Systemtext_stop_file /amd/beyla/d/smart.11.0/lib/common_words
database "."temp_dir ""
trace 0global_trace_start -1global_trace_end 2147483647global_trace_file ""global_start -1global_end 2147483647global_accounting 0
# Indexing Locations doc_loc -query_loc -qrels_text_file ""query_skel ""
# DEFAULTS FOR INDEXING PROCEDURES AND FLAGSindex_pp index.index_pp.index_ppindex_vec index.vec_index.docquery.index_vec index.vec_index.querypreparse index.preparse.genericnext_vecid index.next_vecid.next_vecidquery.next_vecid index.next_vecid.next_vecid_1addtextloc index.addtextloc.add_textlocquery.addtextloc index.addtextloc.emptytoken_sect index.token_sect.token_sectinterior_char "'.@!_"interior_hyphenation falseendline_hyphenation trueparse.single_case falsemakevec index.makevec.makevecexpand index.expand.noneweight convert.weight.weightstore index.store.store_vecdoc.store index.store.store_aux
# DEFAULTS FOR DATABASE FILES AND MODESdict_file dictdoc_file ""textloc_file textlocquery.textloc_file ""inv_file invquery_file ""qrels_file ""collstat_file ""rwmode SRDWRrmode SRDONLYrwcmode SRDWR|SCREATE
## DEFAULTS FOR DOCUMENT DESCRIPTIONS#### GENERIC PREPARSINGnum_pp_sections 0pp_section.string ""pp_section.section_name -pp_section.action copypp_section.oneline_flag falsepp_section.newdoc_flag falsepp.default_section_name -pp.default_section_action discardpp_filter ""
#### PARSING INPUTindex.section.name noneindex.section.ctype -1method index.parse_sect.nonetoken_to_con index.token_to_con.dictstem_wanted 1stemmer index.stem.triestemstopword_wanted 1stopword index.stop.stop_dictstop_file common_wordsthesaurus_wanted 0text_thesaurus_file ""
#### PARSING OUTPUTindex.ctype.name nonecon_to_token index.con_to_token.contok_dictstore_aux convert.tup.vec_invweight_ctype convert.wt_ctype.weight_tridoc_weight nnnquery_weight nnnsect_weight nnn
num_sections 0num_ctypes 0
## DEFAULTS FOR COLLECTION CREATIONstop_file_size 1501dict_file_size 30001
## DEFAULTS FOR CONVERSIONvec_inv.mem_usage 4194000vec_inv.virt_mem_usage 50000000in ""out ""deleted_doc_file ""
########################################### DEFAULTS FOR RETRIEVAL## Proceduresget_query retrieve.get_query.get_q_useruser_qdisp retrieve.user_query.edit_skelqdisp_query retrieve.query_index.std_vecget_seen_docs retrieve.get_seen_docs.emptycoll_sim retrieve.coll_sim.invertedinv_sim retrieve.ctype_coll.ctype_invseq_sim retrieve.ctype_vec.innervecs_vecs retrieve.vecs_vecs.vecs_vecsvec_vec retrieve.vec_vec.vec_vecretrieve.output retrieve.output.ret_trrank_tr retrieve.rank_tr.rank_didsim_ctype_weight 1.0eval.num_queries 0
num_wanted 10
## Filestr_file ""rr_file ""seen_docs_file ""run_name ""
########################################### DEFAULTS FOR FEEDBACK## Proceduresfeedback feedback.feedback.feedbackfeedback.expand feedback.expand.exp_constfeedback.occ_info feedback.occ_info.idefeedback.weight feedback.weight.idefeedback.form feedback.form.form_query
feedback.num_expand 15
feedback.num_iterations 0feedback.alpha 1.0feedback.beta 1.0feedback.gamma 0.5
########################################### DEFAULTS FOR TOP-LEVEL INTERACTIVE DISPLAY AND PRINTING DOCSindivtext print.indivtext.text_filterfilter ""print.format ""print.rawdocflag 0print.doctext print.print.doctextspec_list ""verbose 1max_title_len 68get_query.editor_only 0
## SUBSET OF TOP-LEVEL ROUTINES (these are ones that are often called## elsewhere, and substitution of routines may be desired.)index.doc index.top.doc_collindex.query index.top.query_colltop.exp_coll index.top.exp_colltop.inter inter.interexp_coll index.top.exp_collinter inter.interconvert convert.convert.convertprint print.top.printretrieve retrieve.top.retrieve
## OBSOLETE (only included for backward compatibility)text_loc_prefix ""spec_file ./spectoken index.token.std_tokenparse index.parse.std_parse
# Othersonlyspace_sep 0interior_slash 0interior_semicolon 0
An example of configuration file for SMART
## GENERIC PREPARSERnum_pp_sections 12pp_section.0.string "<DOC>"pp_section.0.oneline_flag truepp_section.0.newdoc_flag truepp_section.0.action discard…pp_section.8.string "<TEXT>"pp_section.8.section_name wpp_section.8.action copy## PARSE INPUT index.num_sections 1index.section.0.name windex.section.0.method index.parse_sect.fullindex.section.0.word.ctype 0index.section.0.proper.ctype 0index.section.0.name.ctype 1
11
Tokenizing Process
First step is to use parser to identify appropriate parts of document to tokenize
Defer complex decisions to other components word is any sequence of alphanumeric characters,
terminated by a space or special character, with everything converted to lower-case
everything indexed example: Wal-Mart → Wal Mart but search finds
documents with Wal and Mart adjacent incorporate some rules to reduce dependence on
query transformation components
12
Stopping
Function words (determiners, prepositions) have little meaning on their own
High occurrence frequencies Treated as stopwords (i.e. removed)
reduce index space, improve response time, improve effectiveness
Can be important in combinations e.g., “to be or not to be”
13
Stopping
Stopword list can be created from high-frequency words or based on a standard list
Lists are customized for applications, domains, and even parts of documents e.g., “click” is a good stopword for anchor text
Best policy is to index all words in documents, make decisions about which words to use at query time
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Stopwords in Web Search Engine
15
Stemming Many morphological variations of words
inflectional (plurals, tenses) derivational (making verbs nouns etc.)
In most cases, these have the same or very similar meanings
Stemmers attempt to reduce morphological variations of words to a common stem usually involves removing suffixes
Can be done at indexing time or as part of query processing (like stopwords)
16
Stemming Generally a small but significant effectiveness
improvement can be crucial for some languages e.g., 5-10% improvement for English, up to 50%
in Arabic
Words with the Arabic root ktb
17
Stemming
Two basic types Dictionary-based: uses lists of related words Algorithmic: uses program to determine related
words Algorithmic stemmers
E.g. remove ‘s’ endings assuming plural e.g., cats → cat, lakes → lake, wiis → wii
Many mistakes: UPS → up
18
Porter Stemmer
Algorithmic stemmer used in IR experiments since the 70s
Most common algorithm for stemming English Produces stems not words Makes a number of errors and difficult to modify
19
Problem with Rule-based Stemming Sometimes too aggressive in conflation (false
positive) E.g., policy/police, execute/executive, university/universe
Sometimes miss good conflations (false negative) E.g., European/Europe, matrices/matrix,
machine/machinery Produce stems that are not words
E.g., Iteration/iter, general/gener Corpus analysis can improve or replace a stemmer
20
Krovetz Stemmer
Hybrid algorithmic-dictionary Word checked in dictionary
If present, either left alone or replaced with “exception” If not present, word is checked for suffixes that could be
removed After removal, dictionary is checked again
Produces words not stems Comparable effectiveness Lower false positive rate, somewhat higher false
negative
21
Stemming Examples Original Text
marketing strategies carried out by U.S. companies for their agricultural chemicals, report predictions for market share of such chemicals, or report market statistics for agrochemicals.
Porter Stemmer (stopwords removed)market strateg carr compan agricultur chemic report predict market share chemic report market statist agrochem
KSTEM (stopwords removed)marketing strategy carry company agriculture chemical report prediction market share chemical report market statistic
22
Corpus-Based StemmingHypothesis: Word variants that should be conflated co-occur in
documents (text windows) in the corpus
Initial equivalence classes generated by another source E.g., Porter stemmer E.g., Common initial prefix or n-grams
New equivalence classes are clusters formed using co-occur statistics between pairs of words in initial classes
Can be used for other languages
23
Corpus-Based Stemming
(Xu & Croft, 1998)
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Phrases Many queries are 2-3 word phrases Phrases are
More precise than single words e.g., documents containing “black sea” vs. two words
“black” and “sea” Less ambiguous
e.g., “big apple” vs. “apple”
In IR, phrases are restricted to noun phrases (or a sequence of nouns or adjectives followed by nouns)
25
Phrases
Text processing issue – how are phrases recognized?
Three possible approaches: Identify syntactic phrases using a part-of-speech
(POS) tagger Use word n-grams Store word positions in indexes and use proximity
operators in queries
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POS Tagging
POS taggers use statistical models of text to predict syntactic tags of words Example tags:
NN (singular noun), NNS (plural noun), VB (verb), VBD (verb, past tense), VBN (verb, past participle), IN (preposition), JJ (adjective), CC (conjunction, e.g., “and”, “or”), PRP (pronoun), and MD (modal auxiliary, e.g., “can”, “will”).
Phrases can then be defined as simple noun groups, for example
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Pos Tagging Example
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Pos Tagging Example
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Example Noun Phrases
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Word N-Grams
POS tagging too slow for large collections Simpler definition – phrase is any sequence of n
words – known as n-grams bigram: 2 word sequence, trigram: 3 word sequence,
unigram: single words N-grams also used at character level for applications such
as OCR N-grams typically formed from overlapping
sequences of words i.e. move n-word “window” one word at a time “Document will describe…” “document will” and
“will describe”
31
N-Grams
Frequent n-grams are more likely to be meaningful phrases
N-grams form a Zipf distribution Better fit than words alone
Could index all n-grams up to specified length Much faster than POS tagging Uses a lot of storage
e.g., document containing 1,000 words would contain 3,990 instances of word n-grams of length 2 ≤ n ≤ 5
32
Google N-Grams
Web search engines index n-grams Google sample:
Most frequent trigram in English is “all rights reserved” In Chinese, “limited liability corporation”
33
Document Structure and Markup
Some parts of documents are more important than others
Document parser recognizes structure using markup, such as HTML tags Headers, anchor text, bolded text all likely to be
important Metadata can also be important Links used for link analysis
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Example Web Page
35
Example Web Page
36
Link Analysis
Links are a key component of the Web Important for navigation, but also for search
e.g., <a href="http://example.com" >Example website</a>
“Example website” is the anchor text “http://example.com” is the destination link both are used by search engines
37
Anchor Text Used as a description of the content of the
destination page i.e., collection of anchor text in all links pointing
to a page used as an additional text field Anchor text tends to be short, descriptive, and
similar to query text Retrieval experiments have shown that anchor
text has significant impact on effectiveness for some types of queries i.e., more than link analysis
38
Link Quality
Link quality is affected by spam and other factors e.g., link farms to increase PageRank trackback links in blogs can create loops
39
Information Extraction
Automatically extract structure from text annotate document using tags to identify
extracted structure Named entity recognition
identify words that refer to something of interest in a particular application
e.g., people, companies, locations, dates, product names, prices, etc.
40
Named Entity Recognition
Example showing semantic annotation of text using XML tags
Information extraction also includes document structure and more complex features such as relationships and events
41
Named Entity Recognition
Rule-based Uses lexicons (lists of words and phrases) that
categorize names e.g., locations, peoples’ names, organizations, etc.
Rules also used to verify or find new entity names e.g., “<number> <word> street” for addresses “<street address>, <city>” or “in <city>” to verify
city names “<street address>, <city>, <state>” to find new cities “<title> <name>” to find new names
42
Named Entity Recognition
Rules either developed manually by trial and error or using machine learning techniques
Statistical uses a probabilistic model of the words in and
around an entity probabilities estimated using training data
(manually annotated text) Hidden Markov Model (HMM) is one approach
43
HMM Sentence Model
• Probabilistic finite state automata • Each state is associated with a probability distribution over
words (the output)
44
Internationalization
2/3 of the Web is in English About 50% of Web users do not use English
as their primary language Many (maybe most) search applications have
to deal with multiple languages monolingual search: search in one language, but
with many possible languages cross-language search: search in multiple
languages at the same time
45
Internationalization
Many aspects of search engines are language-neutral
Major differences: Text encoding (converting to Unicode) Tokenizing (many languages have no word
separators) Stemming
Cultural differences may also impact interface design and features provided