Multilingual Information Retrieval Doug Oard College of Information Studies and UMIACS University of Maryland, College Park USA January 14, 2019 AFIRM Global Trade 2.5 USA 2.0 EU China 1.5 1.0 Exports (Trillions of USD) Exports (Trillions Hong Kong Japan 0.5 South Korea 0.0 0.0 0.5 1.0 1.5 2.0 2.5 Imports (Trillions of USD) Source: Wikipedia (mostly 2017 estimates) Most Widely-Spoken Languages English Mandarin Chinese Hindi Spanish French Modern Std Arabic Russian Bengali Portuguese Indonesian Urdu German Japanese Swahili Western Punjabi Javanese Wu Chinese L1 speakers Telugu Turkish Korean L2 speakers Marathi Tamil Yue Chinese Vietnamese Italian Hausa Thai Persian Southern Min 0 200 400 600 800 1,000 1,200 Billions of Speakers Source: Ethnologue (SIL), 2018 Global Internet Users 2% 4% 4% 4% 5% 0% 4% 2% 5% 33% English 8% Chinese Spanish 5% 2% Japanese 6% Portuguese German 6% 4% Arabic French 64% 5% Russian Korean 9% 28% What Does “Multilingual” Mean? • Mixed-language document – Document containing more than one language • Mixed-language collection – Collection of documents in different languages • Multi-monolingual systems – Can retrieve from a mixed-language collection • Cross-language system – Query in one language finds document in another • (Truly) multingual system – Queries can find documents in any language A Story in Two Parts • IR from the ground up in any language – Focusing on document representation • Cross-Language IR – To the extent time allows Query Documents Representation Representation Function Function Query Representation Document Representation Comparison Function Index Hits | 0 NUL | 32 SPACE | 64 @ | 96 ` | | 1 SOH | 33 ! | 65 A | 97 a | | 2 STX | 34 " | 66 B | 98 b | | 3 ETX | 35 # | 67 C | 99 c | ASCII | 4 EOT | 36 $ | 68 D | 100 d | | 5 ENQ | 37 % | 69 E | 101 e | | 6 ACK | 38 & | 70 F | 102 f | • American Standard | 7 BEL | 39 ' | 71 G | 103 g | | 8 BS | 40 ( | 72 H | 104 h | | 9 HT | 41 ) | 73 I | 105 i | Code for Information | 10 LF | 42 * | 74 J | 106 j | | 11 VT | 43 + | 75 K | 107 k | Interchange | 12 FF | 44 , | 76 L | 108 l | | 13 CR | 45 - | 77 M | 109 m | | 14 SO | 46 . | 78 N | 110 n | | 15 SI | 47 / | 79 O | 111 o | | 16 DLE | 48 0 | 80 P | 112 p | • ANSI X3.4-1968 | 17 DC1 | 49 1 | 81 Q | 113 q | | 18 DC2 | 50 2 | 82 R | 114 r | | 19 DC3 | 51 3 | 83 S | 115 s | | 20 DC4 | 52 4 | 84 T | 116 t | | 21 NAK | 53 5 | 85 U | 117 u | | 22 SYN | 54 6 | 86 V | 118 v | | 23 ETB | 55 7 | 87 W | 119 w | | 24 CAN | 56 8 | 88 X | 120 x | | 25 EM | 57 9 | 89 Y | 121 y | | 26 SUB | 58 : | 90 Z | 122 z | | 27 ESC | 59 ; | 91 [ | 123 { | | 28 FS | 60 < | 92 \ | 124 | | | 29 GS | 61 = | 93 ] | 125 } | | 30 RS | 62 > | 94 ^ | 126 ~ | | 31 US | 64 ? | 95 _ | 127 DEL | The Latin-1 Character Set • ISO 8859-1 8-bit characters for Western Europe – French, Spanish, Catalan, Galician, Basque, Portuguese, Italian, Albanian, Afrikaans, Dutch, German, Danish, Swedish, Norwegian, Finnish, Faroese, Icelandic, Irish, Scottish, and English Printable Characters, 7-bit ASCII Additional Defined Characters, ISO 8859-1 Other ISO-8859 Character Sets -2 -6 -3 -7 -4 -8 -5 -9 East Asian Character Sets • More than 256 characters are needed – Two-byte encoding schemes (e.g., EUC) are used • Several countries have unique character sets – GB in Peoples Republic of China, BIG5 in Taiwan, JIS in Japan, KS in Korea, TCVN in Vietnam • Many characters appear in several languages – Research Libraries Group developed EACC • Unified “CJK” character set for USMARC records Unicode • Single code for all the world’s characters – ISO Standard 10646 • Separates “code space” from “encoding” – Code space extends Latin-1 • The first 256 positions are identical – UTF-7 encoding will pass through email • Uses only the 64 printable ASCII characters – UTF-8 encoding is designed for disk file systems Limitations of Unicode • Produces larger files than Latin-1 • Fonts may be hard to obtain for some characters • Some characters have multiple representations – e.g., accents can be part of a character or separate • Some characters look identical when printed – But they come from unrelated languages • Encoding does not define the “sort order” Strings and Segments • Retrieval is (often) a search for concepts – But what we actually search are character strings • What strings best represent concepts? – In English, words are often a good choice • Well-chosen phrases might also be helpful – In German, compounds may need to be split • Otherwise queries using constituent words would fail – In Chinese, word boundaries are not marked • Thissegmentationproblemissimilartothatofspeech Tokenization • Words (from linguistics): – Morphemes are the units of meaning – Combined to make words • Anti (disestablishmentarian) ism • Tokens (from computer science) – Doug ’s running late ! Morphological Segmentation Swahili Example a + li + ni + andik + ish + a he + past-tense + me + write + causer-effect + Declarative-mode Credit: Ramy Eskander Morphological Segmentation Somali Example cun + t + aa eat + sh + present- e tense Credit: Ramy Eskander Stemming • Conflates words, usually preserving meaning – Rule-based suffix-stripping helps for English • {destroy, destroyed, destruction}: destr – Prefix-stripping is needed in some languages • Arabic: {alselam}: selam [Root: SLM (peace)] • Imperfect: goal is to usually be helpful – Overstemming • {centennial,century,center}: cent – Understamming: • {acquire,acquiring,acquired}: acquir • {acquisition}: acquis • Snowball: rule-based system for making stemmers Longest Substring Segmentation • Greedy algorithm based on a lexicon • Start with a list of every possible term • For each unsegmented string – Remove the longest single substring in the list – Repeat until no substrings are found in the list Longest Substring Example • Possible German compound term (!): – washington • List of German words: – ach, hin, hing, sei, ton, was, wasch • Longest substring segmentation – was-hing-ton – Roughly translates as “What tone is attached?” oil probe petroleum survey take samples cymbidium probe survey goeringii oil take samples restrain petroleum Probabilistic Segmentation • For an input string c1 c2 c3 … cn • Try all possible partitions into w1 w2 w3 … – c1 c2 c3 … cn – c1 c2 c3 c3 … cn – c1 c2 c3 … cn – etc. • Choose the highest probability partition – Compute Pr(w1 w2 w3 ) using a language model • Challenges: search, probability estimation Non-Segmentation: N-gram Indexing • Consider a Chinese document c1 c2 c3 … cn • Don’t segment (you could be wrong!) • Instead, treat every character bigram as a term c1 c2 , c2 c3 , c3 c4 , … , cn-1 cn • Break up queries the same way A “Term” is Whatever You Index • Word sense • Token • Word • Stem • Character n-gram • Phrase Summary • A term is whatever you index – So the key is to index the right kind of terms! • Start by finding fundamental features – We have focused on character coded text – Same ideas apply to handwriting, OCR, and speech • Combine characters into easily recognized units – Words where possible, character n-grams otherwise • Apply further processing to optimize results – Stemming, phrases, … A Story in Two Parts • IR from the ground up in any language – Focusing on document representation Cross-Language IR – To the extent time allows Query-Language CLIR Somali Document Collection Translation Results System select examine Retrieval Engine English queries English Document Collection Document-Language CLIR Somali Document Collection Somali documents Retrieval Translation Results Engine System Somali queries select examine English queries Query vs. Document Translation • Query translation – Efficient for short queries (not relevance feedback) – Limited context for ambiguous query terms • Document translation – Rapid support for interactive selection – Need only be done once (if query language is same) Indexing Time: Statistical Document Translation 500 monolingual cross-language 400 300 200 100 Indexing time (sec) 0 0 10 15 20 25 35 40 45 Thousands of documents Language-Neutral Retrieval Somali Query Terms Query “Translation” English 1: 0.91 Document “Interlingual” Document 2: 0.57 “Translation” Retrieval Terms 3: 0.36 Translation Evidence • Lexical Resources – Phrase books, bilingual dictionaries, … • Large text collections – Translations (“parallel”) – Similar topics (“comparable”) • Similarity – Similar writing (if the character set is the same) – Similar pronunciation • People – May be able to guess topic from lousy translations Types of Lexical Resources • Ontology – Organization of knowledge • Thesaurus – Ontology specialized to support search • Dictionary – Rich word list, designed for use by people • Lexicon – Rich word list, designed for use by a machine • Bilingual term list – Pairs of translation-equivalent terms Full Query Named entities added Named entities from term list Named entities removed Backoff Translation • Lexicon might contain stems, surface forms, or some combination of the two. Document Translation Lexicon mangez mangez - eat surface form surface form mangez mange mange - eats eat stem surface form mange mangez mange - eat surface form stem mangez mange mangent mange - eat stem stem Hieroglyphic Egyptian Demotic Greek Types of Bilingual Corpora • Parallel corpora: translation-equivalent pairs – Document pairs – Sentence pairs – Term pairs • Comparable corpora: topically related – Collection pairs – Document pairs Some Modern Rosetta Stones • News: – DE-News (German-English) – Hong-Kong News, Xinhua News (Chinese-English) • Government: – Canadian Hansards (French-English)