![Stemming
• The final output from a conflation(reducing to the same
token) algorithm is a set of classes, one for each stem
detected.
–A Stem: the portion of a word which is left after the removal of
its affixes (i.e., prefixes and/or suffixes).
–Example: ‘connect’ is the stem for {connected, connecting
connection, connections}
–Thus, [automate, automatic, automation] all reduce to
automat
• A class name is assigned to a document if and only if one of
its members occurs as a significant word in the text of the
document.
–A document representative then becomes a list of class names,
which are often referred as the documents index
terms/keywords.
• Queries : Queries are handled in the same way.](https://image.slidesharecdn.com/chapter2-240412102210-24bfeb9d/85/Chapter-2-Text-Operation-in-information-stroage-and-retrieval-31-320.jpg)
Chapter 2: Text Operation in information stroage and retrieval
- 1. Statistical Properties of Text How is the frequency of different words distributed? How fast does vocabulary size grow with the size of a corpus? Such factors affect the performance of IR system & can be used to select suitable term weights & other aspects of the system. A few words are very common. 2 most frequent words (e.g. “the”, “of”) can account for about 10% of word occurrences. Most words are very rare. Half the words in a corpus appear only once, called “read only once” Called a “heavy tailed” distribution, since most of the probability mass is in the “tail”
- 2. Sample Word Frequency Data
- 3. Zipf’s distributions Rank Frequency Distribution For all the words in a collection of documents, for each word w • f : is the frequency that w appears • r : is rank of w in order of frequency. (The most commonly occurring word has rank 1, etc.) f r w has rank r and frequency f Distribution of sorted word frequencies, according to Zipf’s law
- 4. Word distribution: Zipf's Law Zipf's Law- named after the Harvard linguistic professor George Kingsley Zipf (1902-1950), attempts to capture the distribution of the frequencies (i.e. , number of occurances ) of the words within a text. Zipf's Law states that when the distinct words in a text are arranged in decreasing order of their frequency of occuerence (most frequent words first), the occurence characterstics of the vocabulary can be characterized by the constant rank-frequency law of Zipf: Frequency * Rank = constant If the words, w, in a collection are ranked, r, by their frequency, f, they roughly fit the relation: r * f = c Different collections have different constants c.
- 5. Example: Zipf's Law The table shows the most frequently occurring words from 336,310 document collection containing 125, 720, 891 total words; out of which 508, 209 unique words
- 6. Zipf’s law: modeling word distribution The collection frequency of the ith most common term is proportional to 1/I If the most frequent term occurs f1 times then the second most frequent term has half as many occurrences, the third most frequent term has a third as many, etc Zipf's Law states that the frequency of the ith most frequent word is 1/iӨ times that of the most frequent word occurrence of some event (P), as a function of the rank (i) where the rank is determined by the frequency of occurrence, is a power-law function Pi ~ 1/i Ө with the exponent Ө close to unity. i fi 1
- 7. More Example: Zipf’s Law Illustration of Rank-Frequency Law. Let the total number of word occurrences in the sample N = 1, 000, 000 Rank (R) Term Frequency (F) R.(F/N) 1 the 69 971 0.070 2 of 36 411 0.073 3 and 28 852 0.086 4 to 26 149 0.104 5 a 23237 0.116 6 in 21341 0.128 7 that 10595 0.074 8 is 10099 0.081 9 was 9816 0.088 10 he 9543 0.095
- 8. Methods that Build on Zipf's Law • Stop lists: Ignore the most frequent words (upper cut-off). Used by almost all systems. • Significant words: Take words in between the most frequent (upper cut-off) and least frequent words (lower cut-off). • Term weighting: Give differing weights to terms based on their frequency, with most frequent words weighed less. Used by almost all ranking methods.
- 9. Explanations for Zipf’s Law • The law has been explained by “principle of least effort” which makes it easier for a speaker or writer of a language to repeat certain words instead of coining new and different words. –Zipf’s explanation was his “principle of least effort” which balance between speaker’s desire for a small vocabulary and hearer’s desire for a large one. • Zipf’s Law Impact on IR – Good News: Stopwords will account for a large fraction of text so eliminating them greatly reduces inverted-index storage costs. – Bad News: For most words, gathering sufficient data for meaningful statistical analysis (e.g. for correlation analysis for query expansion) is difficult since they are extremely rare.
- 10. Word significance: Luhn’s Ideas • Luhn Idea (1958): the frequency of word occurrence in a text furnishes a useful measurement of word significance. • Luhn suggested that both extremely common and extremely uncommon words were not very useful for indexing. • For this, Luhn specifies two cutoff points: an upper and a lower cutoffs based on which non-significant words are excluded –The words exceeding the upper cutoff were considered to be common –The words below the lower cutoff were considered to be rare –Hence they are not contributing significantly to the content of the text –The ability of words to discriminate content, reached a peak at a rank order position half way between the two-cutoffs
- 11. Let f be the frequency of occurrence of words in a text, and r their rank in decreasing order of word frequency, then a plot relating f & r yields the following curve Luhn (1958) suggested that both extremely common and extremely uncommon words were not very useful for document representation & indexing.
- 12. Vocabulary size : Heaps’ Law Dictionaries 600,000+ words But they do not include names of people, locations, products etc Heap’s law: estimates the number of vocabularies in a given corpus
- 13. Vocabulary Growth: Heaps’ Law How does the size of the overall vocabulary (number of unique words) grow with the size of the corpus? This determines how the size of the inverted index will scale with the size of the corpus. Heap’s law: estimates the number of vocabularies in a given corpus The vocabulary size grows by O(nβ), where β is a constant between 0 – 1. If V is the size of the vocabulary and n is the length of the corpus in words, Heap’s provides the following equation: Where constants: K 10100 0.40.6 (approx. square-root) Kn V
- 14. Heap’s distributions • Distribution of size of the vocabulary: there is a linear relationship between vocabulary size and number of tokens Example: from 1,000,000,000 documents, there may be 1,000,000 distinct words. Can you agree?
- 15. Example We want to estimate the size of the vocabulary for a corpus of 1,000,000 words. However, we only know statistics computed on smaller corpora sizes: For 100,000 words, there are 50,000 unique words For 500,000 words, there are 150,000 unique words Estimate the vocabulary size for the 1,000,000 words corpus? How about for a corpus of 1,000,000,000 words?
- 16. Text Operations Not all words in a document are equally significant to represent the contents/meanings of a document Some word carry more meaning than others Noun words are the most representative of a document content Therefore, need to preprocess the text of a document in a collection to be used as index terms Using the set of all words in a collection to index documents creates too much noise for the retrieval task Reduce noise means reduce words which can be used to refer to the document Preprocessing is the process of controlling the size of the vocabulary or the number of distinct words used as index terms Preprocessing will lead to an improvement in the information retrieval performance However, some search engines on the Web omit preprocessing Every word in the document is an index term
- 17. Text Operations • Text operations is the process of text transformations in to logical representations • 5 main operations for selecting index terms, i.e. to choose words/stems (or groups of words) to be used as indexing terms: – Lexical analysis/Tokenization of the text - digits, hyphens, punctuations marks, and the case of letters – Elimination of stop words - filter out words which are not useful in the retrieval process – Stemming words - remove affixes (prefixes and suffixes) – Construction of term categorization structures such as thesaurus, to capture relationship for allowing the expansion of the original query with related terms
- 18. Generating Document Representatives • Text Processing System – Input text – full text, abstract or title – Output – a document representative adequate for use in an automatic retrieval system • The document representative consists of a list of class names, each name representing a class of words occurring in the total input text. A document will be indexed by a name if one of its significant words occurs as a member of that class. Tokenization stemming Thesaurus Index terms stop words documents
- 19. Lexical Analysis/Tokenization of Text Change text of the documents into words to be adopted as index terms Objective - identify words in the text Digits, hyphens, punctuation marks, case of letters Numbers are not good index terms (like 1910, 1999); but 510 B.C. – unique Hyphen – break up the words (e.g. state-of-the-art = state of the art)- but some words, e.g. gilt-edged, B-49 - unique words which require hyphens Punctuation marks – remove totally unless significant, e.g. program code: x.exe and xexe Case of letters – not important and can convert all to upper or lower
- 20. Tokenization Analyze text into a sequence of discrete tokens (words). Input: “Friends, Romans and Countrymen” Output: Tokens (an instance of a sequence of characters that are grouped together as a useful semantic unit for processing) Friends Romans and Countrymen Each such token is now a candidate for an index entry, after further processing But what are valid tokens to use?
- 21. Issues in Tokenization • One word or multiple: How do you decide it is one token or two or more? –Hewlett-Packard Hewlett and Packard as two tokens? • state-of-the-art: break up hyphenated sequence. • San Francisco, Los Angeles –lowercase, lower-case, lower case ? • data base, database, data-base • Numbers: • dates (3/12/91 vs. Mar. 12, 1991); • phone numbers, • IP addresses (100.2.86.144)
- 22. Issues in Tokenization • How to handle special cases involving apostrophes, hyphens etc? C++, C#, URLs, emails, … – Sometimes punctuation (e-mail), numbers (1999), and case (Republican vs. republican) can be a meaningful part of a token. – However, frequently they are not. • Simplest approach is to ignore all numbers and punctuation and use only case-insensitive unbroken strings of alphabetic characters as tokens. –Generally, systems do not index numbers as text, but often very useful for search. Will often index “meta-data” , including creation date, format, etc. separately • Issues of tokenization are language specific – Requires the language to be known – What works for one language doesn’t fork for the other.
- 23. Exercise: Tokenization The cat slept peacefully in the living room. It’s a very old cat. Mr. O’Neill thinks that the boys’ stories about Chile’s capital aren’t amusing.
- 24. Elimination of STOPWORD • Stopwords are extremely common words across document collections that have no discriminatory power – They may occur in 80% of the documents in a collection. – They would appear to be of little value in helping select documents matching a user need and needs to be filtered out as potential index terms • Examples of stopwords are articles, prepositions, conjunctions, etc.: –articles (a, an, the); pronouns: (I, he, she, it, their, his) –Some prepositions (on, of, in, about, besides, against), conjunctions/ connectors (and, but, for, nor, or, so, yet), verbs (is, are, was, were), adverbs (here, there, out, because, soon, after) and adjectives (all, any, each, every, few, many, some) can also be treated as stopwords • Stopwords are language dependent.
- 25. Stopwords • Intuition: –Stopwords have little semantic content; It is typical to remove such high-frequency words –Stopwords take up 50% of the text. Hence, document size reduces by 30-50% • Smaller indices for information retrieval – Good compression techniques for indices: The 30 most common words account for 30% of the tokens in written text • Better approximation of importance for classification, summarization, etc.
- 26. How to determine a list of stopwords? • One method: Sort terms (in decreasing order) by collection frequency and take the most frequent ones –In a collection about insurance practices, “insurance” would be a stop word • Another method: Build a stop word list that contains a set of articles, pronouns, etc. – Why do we need stop lists: With a stop list, we can compare and exclude from index terms entirely the commonest words. • With the removal of stopwords, we can measure better approximation of importance for classification, summarization, etc.
- 27. Stop words • Stop word elimination used to be standard in older IR systems. • But the trend is getting away from doing this. Most web search engines index stop words: –Good query optimization techniques mean you pay little at query time for including stop words. –You need stopwords for: • Phrase queries: “King of Denmark” • Various song titles, etc.: “Let it be”, “To be or not to be” • “Relational” queries: “flights to London” – Elimination of stopwords might reduce recall (e.g. “To be or not to be” – all eliminated except “be” – will produce no or irrelevant retrieval)
- 28. Normalization • It is Canonicalizing (change to simplest form) tokens so that matches occur despite superficial differences in the character sequences of the tokens – Need to “normalize” terms in indexed text as well as query terms into the same form – Example: We want to match U.S.A. and USA, by deleting periods in a term • Case Folding: Often best to lower case everything, since users will use lowercase regardless of ‘correct’ capitalization… – Republican vs. republican – Fasil vs. fasil vs. FASIL – Anti-discriminatory vs. antidiscriminatory – Car vs. automobile?
- 29. Normalization issues • Case folding is good for – Allowing instances of Automobile at the beginning of a sentence to match with a query of automobile – Helping a search engine when most users type ferrari when they are interested in a Ferrari car • Bad for – Proper names vs. common nouns • E.g. General Motors, Associated Press, … • Solution: – lowercase only words at the beginning of the sentence • In IR, lowercasing is most practical because of the way users issue their queries
- 30. Stemming/Morphological analysis • Stemming reduces tokens to their “root” form of words to recognize morphological variation . – The process involves removal of affixes (i.e. prefixes and suffixes) with the aim of reducing variants to the same stem – Often removes inflectional and derivational morphology of a word • Inflectional morphology: vary the form of words in order to express grammatical features, such as singular/plural or past/present tense. E.g. Boy → boys, cut → cutting. • Derivational morphology: makes new words from old ones. E.g. creation is formed from create , but they are two separate words. And also, destruction → destroy • Stemming is language dependent – Correct stemming is language specific and can be complex. for example compressed and compression are both accepted. for example compress and compress are both accept
- 31. Stemming • The final output from a conflation(reducing to the same token) algorithm is a set of classes, one for each stem detected. –A Stem: the portion of a word which is left after the removal of its affixes (i.e., prefixes and/or suffixes). –Example: ‘connect’ is the stem for {connected, connecting connection, connections} –Thus, [automate, automatic, automation] all reduce to automat • A class name is assigned to a document if and only if one of its members occurs as a significant word in the text of the document. –A document representative then becomes a list of class names, which are often referred as the documents index terms/keywords. • Queries : Queries are handled in the same way.
- 32. Ways to implement stemming There are basically two ways to implement stemming. The first approach is to create a big dictionary that maps words to their stems. The advantage of this approach is that it works perfectly (insofar as the stem of a word can be defined perfectly); the disadvantages are the space required by the dictionary and the investment required to maintain the dictionary as new words appear. The second approach is to use a set of rules that extract stems from words. The advantages of this approach are that the code is typically small, and it can gracefully handle new words; the disadvantage is that it occasionally makes mistakes. But, since stemming is imperfectly defined, anyway, occasional mistakes are tolerable, and the rule-based approach is the one that is generally chosen.
- 33. Porter Stemmer • Stemming is the operation of stripping the suffices from a word, leaving its stem. – Google, for instance, uses stemming to search for web pages containing the words connected, connecting, connection and connections when users ask for a web page that contains the word connect. • In 1979, Martin Porter developed a stemming algorithm that uses a set of rules to extract stems from words, and though it makes some mistakes, most common words seem to work out right. – Porter describes his algorithm and provides a reference implementation in C at http://tartarus.org/~martin/PorterStemmer/index.html
- 34. Porter stemmer • Most common algorithm for stemming English words to their common grammatical root • It is simple procedure for removing known affixes in English without using a dictionary. To gets rid of plurals the following rules are used: – SSES SS caresses caress –IES i ponies poni –SS SS caress → caress –S cats cat –EMENT (Delete final ement if what remains is longer than 1 character ) replacement replac cement cement
- 35. While step 1a gets rid of plurals, step 1b removes -ed or - ing. e.g. ;; agreed -> agree ;; disabled -> disable ;; matting -> mat ;; mating -> mate ;; meeting -> meet ;; milling -> mill ;; messing -> mess ;; meetings -> mee ;; feed -> feedt
- 36. Stemming: challenges May produce unusual stems that are not English words: Removing ‘UAL’ from FACTUAL and EQUAL May conflate (reduce to the same token) words that are actually distinct. “computer”, “computational”, “computation” all reduced to same token “comput” Not recognize all morphological derivations.
- 37. Thesauri • Mostly full-text searching cannot be accurate, since different authors may select different words to represent the same concept – Problem: The same meaning can be expressed using different terms that are synonyms, homonyms, and related terms – How can it be achieved such that for the same meaning the identical terms are used in the index and the query?
- 38. …continued • Thesaurus: The vocabulary of a controlled indexing language, formally organized so that a priori relationships between concepts (for example as "broader" and “related") are made explicit. • A thesaurus contains terms and relationships between terms – IR thesauri rely typically upon the use of symbols such as USE/UF (UF=used for), BT, and RT to demonstrate inter-term relationships. –e.g., car = automobile, truck, bus, taxi, motor vehicle -color = colour, paint
- 39. Aim of Thesaurus • Thesaurus tries to control the use of the vocabulary by showing a set of related words to handle synonyms and homonyms • The aim of thesaurus is therefore: – to provide a standard vocabulary for indexing and searching • Thesaurus rewrite to form equivalence classes, and we index such equivalences • When the document contains automobile, index it under car as well (usually, also vice-versa) – to assist users with locating terms for proper query formulation: When the query contains automobile, look under car as well for expanding query – to provide classified hierarchies that allow the broadening and narrowing of the current request according to user needs
- 40. Thesaurus Construction Example: thesaurus built to assist IR for searching cars and vehicles : Term: Motor vehicles UF : Automobiles Cars Trucks BT: Vehicles RT: Road Engineering Road Transport
- 41. More Example Example: thesaurus built to assist IR in the fields of computer science: TERM: natural languages UF natural language processing (UF=used for NLP) BT languages (BT=broader term is languages) TT languages (TT = (top term) is languages) RT artificial intelligence (RT=related term/s) computational linguistic formal languages query languages speech recognition
- 42. Language-specificity Many of the above features embody transformations that are Language-specific and Often, application-specific These are “plug-in” addenda to the indexing process Both open source and commercial plug-ins are available for handling these issues
- 43. INDEX TERM SELECTION Index language is the language used to describe documents and requests Elements of the index language are index terms which may be derived from the text of the document to be described, or may be arrived at independently. If a full text representation of the text is adopted, then all words in the text are used as index terms = full text indexing Otherwise, need to select the words to be used as index terms for reducing the size of the index file which is basic to design an efficient searching IR system