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Unknown Word 08 Unknown Word 08 Presentation Transcript

  • Pattern Mining to Chinese Unknown word Extraction 資工碩二 955202037 楊傑程 2008/08/12
  • Outline
    • Introduction
    • Related Works
    • Unknown Word Detection
    • Unknown Word Extraction
    • Experiments
    • Conclusions
  • Introduction
    • Since the growing popularity of Chinese, Chinese Text Processing has become a popular research task in recent years.
    • Before utilizing knowledge of Chinese texts, some preprocessing work should be done, such as Chinese Word Segmentation.
      • There is no blank to mark word boundaries in Chinese texts.
  • Introduction
    • Chinese Word Segmentation encounters two major problems: Ambiguity and Unknown Words.
    • Ambiguity
      • One un-segmented Chinese character string has different segmentations according to different context information.
        • Ex: the sentence “ 研究生命起源” can be segmented into
          • “ 研究 生命 起源” or
          • “ 研究生 命 起源”。
    • Unknown Words
      • Also known as Out-Of-Vocabulary words (OOV words), mostly unfamiliar proper nouns or new-born words.
        • Ex: the sentence “ 王義氣熱衷於研究生命” would be segmented into
          • “ 王 義氣 熱衷 於 研究 生命”
          • because “ 王義氣” is a uncommon personal name, which is not in vocabularies.
  • Introduction- types of unknown words
    • In this paper, we focus on Chinese unknown word problem.
    Types of Chinese unknown words Organization names Ex: 華碩電腦 Ex: 總經理、電腦化 Abbreviation Proper Names Ex: 中油、中大 Personal names Ex: 王小明 Derived Words Compounds Ex: 電腦桌、搜尋法 Numeric type compounds Ex: 1986 年、 19 巷
  • Introduction- unknown word identification
    • Chinese Word Segmentation Process:
    • Initial Segmentation (Dictionary assisted)
      • Correctly identified words are called known words.
      • Unknown words are wrongly segmented into two or more parts.
        • Ex: personal name 王小明 after initial segmentation,
        • become 王 小 明
    • Unknown word identification
      • Characters belong to one unknown word should combine together.
        • Ex: combine 王 小 明 together as 王小明
  • Introduction- unknown word identification
    • How does unknown word identification work?
      • A character can be a word ( 馬 ) or part of unknown word ( 馬 + 英 + 九 ).
    • Unknown Word Detection Rules
      • With help of syntactic information 、 context information
    • Then just focus on detected morphemes and combine them.
  • Introduction- detection and extraction
    • In this paper, we apply continuity pattern mining to discover unknown word detection rules.
    • Then, we utilize syntactic information 、 context information and heuristic statistical information to correctly extract unknown words.
  • Introduction- applied techniques
    • We adopt Sequential Data Learning methods and Machine Learning Algorithms to carry out unknown word extraction.
    • Our unknown word extraction method is a general method
      • not limit extraction on specific types of unknown words based on artificial rules.
  • Related Works- particular methods
    • So far, research on Chinese word segmentation has lasted for a decade.
    • First, researchers apply different kinds of information to discover different kinds of unknown words (particular).
      • Patterns, Frequency, Context Information
        • Proper nouns ([Chen & Li, 1996] 、 [Chen & Chen, 2000])
  • Related Works- general methods (Rule-based)
    • Then, researchers start to figure out methods extracting whole kinds of unknown words.
    • Rule-based Detection:
      • Distinguish monosyllabic words and monosyllabic morphemes ([ Chen et al., 1998 ])
      • Combine Morphological rules with Statistical rules to extract personal names 、 transliteration names and compound nouns. ( [Chen et al., 2002] ) <Precision: 89%, Recall: 68%>
      • Utilize context free grammar concept and propose a bottom-up merging algorithm
        • Adopt morphological rules and general rules to extract all kinds of unknown words. ([Ma et al., 2003] ) < Precision: 76%, Recall: 57%>
  • Related Works- general methods (Statistical Model-based)
    • Statistical Model-based Detection:
      • Apply Machine Learning algorithms and Sequential Supervised Learning.
      • Direct method:
        • Generate one corresponding statistical model
        • Initial Segmentation and role tagging (HMM 、 CRF)
        • Chunking (SVM)
        • [Goh et. al, 2006]: HMM+SVM, <Precision: 63.8%, Recall: 58.3%>
        • [Tsai et. al, 2006]: CRF, < Recall: 73% >
  • Related Works – Data
    • Sequential Supervised Learning:
      • Direct method, like HMM 、 CRF
      • Indirect method, like Sliding Window 、 Recurrent Sliding Windows
        • Transform sequential learning problem into classification problem <[T. G. Dietterich, 2002]>
    • Imbalance Data Problem
      • <[Seyda et. al, 2007]>
        • Select the most informative instances.
        • Random sampling 59 instances in each iteration, then pick the closest instance to the hyper-plane.
  • Unknown Word Detection & Extraction
    • Our idea is similar to [Chen et al, 2002]:
      • Unknown word detection
        • Continuity pattern mining to derive detection rules.
      • Unknown word extraction
        • utilize natural language information 、 content & context information and statistical information to extract unknown words.
    • Sequential supervised learning methods (indirect) and machine-learning based models are used.
  • Unknown Word Detection
    • We call unknown word detection as “Phase 1 process”, and unknown word extraction as “Phase 2 process”.
    • The following graph is the flow chart of unknown word detection (Phase 1).
  • Initial segmentation Dictionary (Libtabe lexicon ) POS tagging -TnT Unknown word detection Detection rules Pattern Mining to derive detection rules Training data (8/10 balanced corpus) Phase2 training data label Testing 2 ( un-segmented ) (1/10 balanced corpus) Initial segmentation POS tagging -TnT Phase1 Training Phase1 Testing
  • Unknown word detection- Pattern Mining
    • Pattern Mining:
      • Sequential Pattern:
        • “ 因為… , 所以…”
        • Required items match pattern order
        • Allow noise in the middle of required items.
      • Continuity Pattern:
        • “ 打球” => “ 打球” : match, “ 打籃球” : not match
        • Strict definition to each items and order.
        • Efficient pattern mining
  • Unknown word detection- Continuity Pattern Mining
    • Prowl
      • <[Huang et. al, 2004]>
      • Starts with 1-frequent pattern
      • Extend to 2 pattern by two adjacent 1-frequent patterns, then evaluate its frequency.
  • Encoding
    • Original segmentation label the words based on lexicon matching : known (Y) or unknown (N)
      • “ 葡萄” , in the lexicon => “ 葡萄” labels as known word (Y)
      • “ 葡萄皮” , not in the lexicon => “ 葡萄皮” labels as unknown word (N)
    • Encoding examples:
      • 葡萄 (Na)  葡 (Na) Y + 萄 (Na) Y
      • 葡萄皮 (Na)  葡 (Na) N + 萄 (Na) N+ 皮 (Na) N
  • Create detection rules
    • This pattern rule means: when “ 葡 (Na), 萄 (Na)” appears, the probability that “ 葡 (Na)” being a known word (unknown word) is 0.5.
    ( 葡 (Na) , 萄 Y) : 1 ( 葡 (Na) , 萄 Y) : 1
  • Store data (term + term_attribute + POS) Phase2 training data Sliding Window Positive example: Find BIES Negative example: Learn and drop SVM model 2-gram SVM model 3-gram SVM model 4-gram Calculate term frequency per docs SVM training Models (3) Calculate Precision /Recall Correct segmentation 1/10 balanced corpus Merging evaluation Solve overlap and conflict (SVM) Sequential data
  • Unknown Word Extraction
    • After initial segmentation and applying detection rules, each term will have a “ term_attribute ” label itself.
    • Six different “term_attributes” are as follows :
      • ms() mornosyllabic word , Ex: 你、我、他
      • ms(?) morphemes of unknown word , Ex: “ 王 ”、“ 小 ”、“ 明 ” on “ 王小明 ”
      • ds() double-syllabic word , Ex: 學校
      • ps() poly-syllabic word , Ex: 筆記型電腦
      • dot() punctuation , Ex: “ ,”、 “。”…
      • none() no above information or new term
    • The target of unknown word are those whose “term_attribute” labeled as “ms(?)”.
  • Positive / Negative Judgment
    • A term should be a word or part of unknown word. Based upon the position of a word in the sentence, we have the following four types of position labels :
      • B Begin ex: “ 王” of “ 王姿分”
      • I Intermediate ex: “ 姿” of “ 王姿分”
      • E End ex: “ 分” of “ 王姿分”
      • S Singular ex: “ 我”、“你”
    • Find B + I * (zero to more) + E combination (positive)
      • 王 (?) B
      • 姿 (?) I
      • 分 (?) E
    • Combine as a new word ( 王姿分 )
    • Random pick the same number of positive examples as number of negative ones in the training model.
  • Data Processing- Sliding Window
    • Sequential Supervised Learning
      • Indirect method: transform sequential learning to classification learning
      • Sliding Window
    • Each time we choose n+2 (+prefix & suffix) terms as one data, then we shift one token to right to generate another one, and so on.
      • Ps. must exist at least one ms(?) in n terms.
    • We offer three choices of n, e.g. 2.3.4. Namely, we offer three SVM models to extract different lengths of unknown words.
    • We call them as N-gram data (model).
  • EX: 3-gram Model discard negative negative negative positive 運動會 ()  ‧ ()  四年 ()  甲班 ()  王 (?)  姿 (?)  分 (?)  ‧ ()  本校 ()  為 ()  響 ()  應 () 運動會 ‧ 四年 甲班 王 (?) ‧ 四年 甲班 王 (?) 姿 (?) 四年 甲班 王 (?) 姿 (?) 分 (?) 甲班 王 (?) B 姿 (?) I 分 (?) E ‧ 王 (?) 姿 (?) 分 (?) ‧ 本校
  • Statistical Information
    • For each n-gram data, we calculate subsequent records:
      • pos tag of each term
      • Term_attribute (ms() 、 ms(?) 、 ds()…)
      • Statistical information: (examplfied by 3-gram Model),
        • Frequency of 3-gram.
        • p( prefix | 3-gram), e.g. p( prefix | t1~t3)
        • p( suffix | 3-gram), e.g. p( suffix | t1~t3)
        • p( first term of n | other n-1 consecutive terms), e.g. p( t1 | t2~t3)
        • p( last term of n | other n-1 preceding terms), e.g. p( t3 | t1~t2)
        • p( pos_freq(prefix) / pos_freq(prefix in training positive))
        • p( pos_freq(suffix) / pos_freq(suffix in training positive))
    prefix (0) t1 t2 t3 suffix (4)
  • Experiments
    • Unknown word detection.
    • Unknown word extraction.
  • Unknown Word Detection
    • 8/10 balanced corpus (575m words) as training data.
    • Use Pattern mining tool: Prowl [Huang et al., 2004]
    • Random pick 1/10 balanced corpus (uncovered in training data) as testing data.
    • Use accuracy as threshold of detection rules.
    Threshold (Accuracy) Precision Recall F-measure (our system) F-measure (AS system) 0.7 0.9324 0.4305 0.589035 0.71250 0.8 0.9008 0.5289 0.66648 0.752447 0.9 0.8343 0.7148 0.769941 0.76955 0.95 0.764 0.8288 0.795082 0.76553 0.98 0.686 0.8786 0.770446 0.744036
  • Unknown Word Extraction
    • The rest of Sinica corpus data will be used as testing data in Phase 2.
    • [Chen et al., 2002] evaluates unknown word extraction mainly on Chinese personal names 、 foreign transliteration names and compound nouns.
    • We utilize our extraction method on all kinds of unknown word types.
  • Unknown Word Extraction
    • In judging overlap and conflict problem of different combination of unknown words :
      • [Chen et al., 2002] : frequency (w) * length (w) .
        • Ex: “ 律師 班 奈 特” , => freq( 律師 + 班 )*2 : freq( 班 + 奈 + 特 )*3
      • Our method:
      • First solve overlap problem for identical N-gram data:
        • P( prefix | overlap) : P( suffix | overlap)
          • Ex: “ 義民 廟 中” : P( 義民 | 廟 ) : P( 中 | 廟 )
      • Then solve conflict problem by comparing different N-gram data by:
        • Real frequency
          • freq (X)-freq (Y) , if X is included in Y ex: X=“ 醫學”、“學院” , Y=“ 醫學院”
        • Freq( N-gram) * Freq( POS_N-gram*), N: 2~4
  • Testing result
    • We also evaluate three kinds of unknown word in [Chen et al., 2002]:
      • 3-gram unknown words: recall=0.73
      • 2-gram unknown words: recall=0.7
      • 3-gram and 2-gram combined: recall=0.68
    • [Chen et al., 2002] :
      • Only morphological rules: F1 score= 0.62 (precision=0.92,recall=0.47)
      • Only statistical rules: F1 score= 0.52 (precision=0.78,recall=0.39)
      • Combination: F1 score= 0.77 (precision=0.89,recall=0.68)
  • SVM testing result
    • For general purpose:
    N-gram F1 score Precision Recall Only 4-gram 0.164 0.1 0.57 Only 3-gram 0.377 0.257 0.70 Only 2-gram 0.587 0.492 0.73 Three n-gram models combined 0.524 0.457 0.614
  • Ongoing Experiments
    • Two experimental directions:
    • Sampling policy
      • <[Seyda et. al, 2007]>:
          • In SVM, the instances close to the hyper-plane are informative for learning.
          • Weka classification confidence
          • Spilt whole training data to get confidence
    • Ensemble Methods
      • Bagging 、 AdaBoost
    inst# actual predicted error prediction 1 2:-1 2:-1 - 0.984 2 1:1 1:1 - 0.933 …………………………………………… .. 116 2:-1 1:1 + 0.505
  • 0.75 0.688 0.825 Bagging (SMO) Confidence=0.97 + all p 3 0.743 0.674 0.829 Libsvm Confidence=0.97 + all p 3 0.72 0.722 0.717 Libsvm P:N= 1:4 3 0.678 0.674 F-Measure 0.612 0.716 Recall Precision 0.759 0.637 Result Libsvm Libsvm Algorithm (inside) Confidence=0.95 + error + all p P:N = 1:2 Sample By 2 2 Gram