emnlp emnlp2013 emnlp2013-9 knowledge-graph by maker-knowledge-mining

9 emnlp-2013-A Log-Linear Model for Unsupervised Text Normalization

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Author: Yi Yang ; Jacob Eisenstein

Abstract: We present a unified unsupervised statistical model for text normalization. The relationship between standard and non-standard tokens is characterized by a log-linear model, permitting arbitrary features. The weights of these features are trained in a maximumlikelihood framework, employing a novel sequential Monte Carlo training algorithm to overcome the large label space, which would be impractical for traditional dynamic programming solutions. This model is implemented in a normalization system called UNLOL, which achieves the best known results on two normalization datasets, outperforming more complex systems. We use the output of UNLOL to automatically normalize a large corpus of social media text, revealing a set of coherent orthographic styles that underlie online language variation.

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Summary: the most important sentenses genereted by tfidf model

sentIndex sentText sentNum sentScore

1 The weights of these features are trained in a maximumlikelihood framework, employing a novel sequential Monte Carlo training algorithm to overcome the large label space, which would be impractical for traditional dynamic programming solutions. [sent-4, score-0.209]

2 This model is implemented in a normalization system called UNLOL, which achieves the best known results on two normalization datasets, outperforming more complex systems. [sent-5, score-0.652]

3 We use the output of UNLOL to automatically normalize a large corpus of social media text, revealing a set of coherent orthographic styles that underlie online language variation. [sent-6, score-0.442]

4 Many ofthe attempts to characterize and overcome this variation have focused on normalization: transforming social media language into text that better matches standard datasets (Sproat et al. [sent-8, score-0.208]

5 Because there is little available training data, and because social media language changes rapidly (Eisenstein, 2013b), fully supervised training is generally not considered appropriate for this task. [sent-11, score-0.174]

6 We propose a different approach, performing normalization in a maximum-likelihood framework. [sent-17, score-0.326]

7 There are two main sources of information to be exploited: local context, and surface similarity between the observed strings and normalization candidates. [sent-18, score-0.371]

8 Because labeled examples of normalized text are not available, this model cannot be trained in the standard supervised fashion. [sent-20, score-0.129]

9 , 2010), as their complexity is quadratic in the size of label space; in normalization, the label space is the vocabulary itself, with at least 104 elements. [sent-22, score-0.111]

10 This training method may be applicable in other unsupervised learning problems with a large label space. [sent-24, score-0.087]

11 This model is implemented in a normalization system called UNLOL (unsupervised normalization in a LOg-Linear model). [sent-25, score-0.652]

12 Our evaluations show that UNLOL outperforms the state-of-the-art on standard normalization datasets. [sent-29, score-0.326]

13 In addition, we demonstrate the linguistic insights that can be obtained from normalization, using UNLOL to identify classes of orthographic transformations that form coherent linguistic styles. [sent-30, score-0.105]

14 2 Background The text normalization task was introduced by Sproat et al. [sent-31, score-0.326]

15 It has become still more salient in the era of widespread social media, particularly Twitter. [sent-34, score-0.096]

16 Han and Baldwin (201 1) formally define a normalization task for Twitter, focusing on normalizations between single tokens, and excluding multi-word tokens like lol (laugh out loud). [sent-35, score-0.455]

17 The normalization task has been criticized by Eisenstein (2013b), who argues that it strips away important social meanings. [sent-36, score-0.422]

18 In recent work, normalization has been shown to yield improvements for part-of-speech tagging (Han et al. [sent-37, score-0.326]

19 As we will show in Section 7, accurate automated normalization can also improve our understanding of the nature of social media language. [sent-40, score-0.5]

20 Supervised methods Early work on normalization focused on labeled SMS datasets, using approaches such as noisy-channel modeling (Choudhury et al. [sent-41, score-0.361]

21 The scalar parameters are then estimated using expectation maximization. [sent-50, score-0.084]

22 (2010) use string edit distance to identify closely-related candidate orthographic forms and then decode the message using a language model. [sent-53, score-0.261]

23 (2010), we apply string edit distance, and like Gouws et al. [sent-57, score-0.119]

24 At present, labeled data for Twitter normalization is available only in small quantities. [sent-72, score-0.361]

25 Moreover, as social media language is undergoing rapid change (Eisenstein, 2013b), labeled datasets may become stale and increasingly ill-suited to new spellings and words. [sent-73, score-0.243]

26 Resources that characterize the current state of internet language risk becoming outdated; in this paper we investigate whether high-quality normalization is possible without any such resources. [sent-77, score-0.326]

27 These features may include simple string edit distance metrics, as well as lexical features that memorize specific pairs of standard and nonstandard words. [sent-87, score-0.217]

28 For example, in the phrase give me sutt in t o be l ieve in, even a reader who has never before seen the word sutt in may recognize it as a phonetic transcription of something. [sent-91, score-0.164]

29 The relatively high string edit distance is overcome by the strong contextual preference for the word something over orthographically closer alternatives such as button or suiting. [sent-92, score-0.119]

30 We can apply an arbitrary target language model, leveraging large amounts of unlabeled data and catering to the desired linguistic characteristics of the normalized content. [sent-93, score-0.154]

31 — — 63 None of these tokens are standard (except 2, which appears in a nonstandard sense here), so without joint inference, it would not be possible to use context to help normalize sutt in. [sent-98, score-0.302]

32 However, unlike the supervised case, here both terms are expectations: the outer expectation is over all target sequences (given the observed source sequence), and the nested expectation is over all source sequences, given the target sequence. [sent-131, score-0.369]

33 As the space of possible target sequences t grows exponentially in the length of the source sequence, it will not be practical to compute this expectation directly. [sent-132, score-0.144]

34 First, while the forward- backward algorithm would enable us to compute Et|s, it would not give us the nested expectation Et|s [Es0|t] ; this is the classic challenge in training globally-normalized log-linear models without labeled data (Smith and Eisner, 2005). [sent-135, score-0.162]

35 SMC algorithms maintain a set of weighted hypotheses; the weights correspond to probabilities, and in our case, the hypotheses correspond to target language word sequences. [sent-142, score-0.16]

36 Specifically, we approximate the conditional probability, XK P(t1:n|s1:n) ≈Xk=1ωnkδt1k:n(t1:n), where ωnk is the normalized weight of sample k at word n ( ω˜nkis the unnormalized weight), and δt1k:n is a delta function centered at t1k:n. [sent-143, score-0.208]

37 At each step, and for each hypothesis k, a new target word is sampled from a proposal distribution, and the weight of the hypothesis is then updated. [sent-144, score-0.338]

38 We maintain feature counts for each hypothesis, and approximate the expectation by taking a weighted average using the hypothesis weights. [sent-145, score-0.162]

39 The proposal distribution will be described in detail later. [sent-146, score-0.242]

40 With these assumptionsQ, we can view normalization as a finite state-space model in which the target language model defines the prior distribution ofthe process and Equation 3 defines the likelihood function. [sent-158, score-0.468]

41 We are able to compute the the posterior probability P(t |s) using sequential importance sampling, a imtyem Pb(etr|s o)f u uthsien gSM seCq family. [sent-159, score-0.158]

42 The crucial idea in sequential importance sampling is to update the hypotheses t1k:n and their weights ωnk so that they approximate the posterior distribution at the next time step, P(t1:n+1 |s1:n+1). [sent-160, score-0.374]

44 (10) where we sample tnk and update ωnk while moving from left-to-right, and sample s‘n,k at each n. [sent-165, score-0.329]

45 Note that although the sequential importance sampler moves left-to-right like a filter, we use only the final weights ωN to compute the expectation. [sent-166, score-0.213]

46 Thus, the resulting expectation is based on the distribution P(s1:N |t1:N), so that no backwards “smoothing” pass (Godsill et al. [sent-167, score-0.128]

47 Other applications of sequential Monte Carlo make use of resampling (Cappe et al. [sent-169, score-0.119]

48 2 Proposal distribution The major computational challenge for dynamic programming approaches to normalization is the large label space, equal to the size of the target vocabulary. [sent-172, score-0.465]

49 It may appear that all we have gained by applying sequential Monte Carlo is to convert a computational problem into a statistical one: a naive sampling approach will have little hope of finding the small high-probability region ofthe highdimensional label space. [sent-173, score-0.188]

50 However, sequential importance sampling allows us to address this issue through the proposal distribution, from which we sample the candidate words tn. [sent-174, score-0.39]

51 Careful design ofthe proposal distribution can guide sampling towards the high-probability space. [sent-175, score-0.276]

52 In the asymptotic limit of an infinite number of samples, any non-pathological proposal distribution will ultimately arrive at the desired estimate, but a good proposal distribution can greatly reduce the number of samples needed. [sent-176, score-0.524]

53 In low-dimensional settings, a convenient solution is to set the proposal distribution equal to the transition distribution, Q(tnk |sn, tnk−1) = P(tnk|tnk−1, . [sent-180, score-0.242]

54 We strike a middle ground between efficiency and accuracy, using a proposal distribution that is closely related to the overall likelihood, yet is tractable to sample and compute: ω(k) Q(tnk|sn,tk) d=ef P(sn|tkn)Z(tnk)P(tnk|tk) Pt0P(sn|t0)Z(t0)P(t0|tk) (12) exp ? [sent-188, score-0.242]

55 To update the unnormalized hypothesis weights ω˜kn, we have ω˜kn=ωkn−1Pt0exp? [sent-197, score-0.171]

56 3 Decoding Given an input source sentence s, the decoding problem is to find a target sentence t that maximizes P(t|s) ∝ P(s|t)P(t) = P(sn|tn)P(tn|tn−1). [sent-201, score-0.105]

57 This must be multiplied by the cost of computing the normalized probability P(sn |tn), resulting in a prohibitive time complexity of O(#|t|νS|#|νT|2N). [sent-203, score-0.129]

58 The first is to simply apply the proposal distribution, with linear complexity in the size of the two vocabularies. [sent-205, score-0.198]

59 Alternatively, we can apply t(hte) proposal distribution for selecting target word candidates, then apply the Viterbi algorithm only within these candidates. [sent-207, score-0.302]

60 (2010), which has proven effective for normalization in prior work. [sent-218, score-0.364]

61 We bin this similarity to create binary features indicating whether a string s is in the top-N most similar strings to t; this binning yields substantial speed improvements without negatively impacting accuracy. [sent-219, score-0.106]

62 5 Implementation and data The model and inference described in the previous section are implemented in a software system for normalizing text on twitter, called UNLOL: unsupervised normalization in a LOgLinear model. [sent-220, score-0.427]

63 1 Normalization candidates Most tokens in tweets do not require normalization. [sent-224, score-0.117]

64 The question of how to identify which words are to be normalized is still an open problem. [sent-225, score-0.094]

65 Following Han and Baldwin (201 1), we build a dictionary of words which are permissible in the target domain, and make no attempt to normalize source strings that match these words. [sent-226, score-0.18]

66 As with other comparable approaches, we are therefore unable to normalize strings like i l l into I’ll. [sent-227, score-0.12]

67 For all in-vocabulary words, we define P(sn |tn) = δ(sn, tn), taking the value of zero when sn tn. [sent-235, score-0.146]

68 In addition to words that are in the target vocabulary, there are many other strings that should not be normalized, such as names and multiword shortenings (e. [sent-237, score-0.146]

69 1 We follow prior work and assume that the set of normalization candidates is known in advance during test set decoding (Han et al. [sent-240, score-0.409]

70 Thus, during training we attempt to normalize all tokens that (1) are not in our lexicon of IV words, and (2) are composed of letters, numbers and the apostrophe. [sent-243, score-0.122]

71 This set includes contractions like "gonna" and "gotta", which would not appear in the test set, but are nonetheless normalized — = 1Whether multiword shortenings should be normalized is arguable, but they are outside the scope of current normalization datasets (Han and Baldwin, 2011). [sent-244, score-0.589]

72 For each OOV token, we conduct a pre-normalization step by reducing any repetitions of more than two letters in the nonstandard words to exactly two letters (e. [sent-246, score-0.098]

73 3 Parameters The Monte Carlo approximations require two parameters: the number of samples for sequential Monte Carlo (K), and the number of samples for the non-sequential sampler of the nested expectation (L, from Equation 10). [sent-255, score-0.326]

74 , words that are not in the target vocabulary) and their normalized forms. [sent-262, score-0.154]

75 As this corpus does not provide linguistic context, its decoding must use a unigram target language model. [sent-264, score-0.105]

76 1 by its authors Han and Baldwin (201 1) contains 549 complete tweets with — — — — 1,184 nonstandard tokens (558 unique word types). [sent-266, score-0.215]

77 1 revealed some inconsistencies in annotation (for example, y ’ al l and 2 are sometimes normalized to you and to, but are left unnormalized in other cases). [sent-299, score-0.17]

78 For example, smh is normalized to somehow in LexNorm1 . [sent-301, score-0.135]

79 2 in the hope that it will become standard in future work on normalization in English. [sent-317, score-0.326]

80 68 Metrics Prior work on these datasets has assumed perfect detection of words requiring normalization, and has focused on finding the correct normalization for these words (Han and Baldwin, 2011; Han et al. [sent-328, score-0.36]

81 Recall has been defined as the proportion of words requiring normalization which are normalized correctly; precision is defined as the proportion of normalizations which are correct. [sent-330, score-0.502]

82 In the normalization task that we consider, the tokens to be normalized are specified in advance. [sent-337, score-0.467]

83 Regularization One potential concern is that the number of non-zero feature weights will continually increase until the memory cost becomes overwhelming. [sent-343, score-0.09]

84 7 Analysis We apply our normalization system to investigate the orthographic processes underlying language variation in social media. [sent-361, score-0.527]

85 We then treat these normalizations as labeled training data, and examine the Levenshtein alignment between the source and target tokens. [sent-363, score-0.177]

86 ” We apply non-negative matrix factorization (Lee and Seung, 2001), which characterizes each author by a vector of k style loadings, and simultaneously constructs k style dictionaries, which each put weight on different orthographic rules. [sent-367, score-0.266]

87 Because the loadings are constrained to be non-negative, the factorization can be seen as sparsely assigning varying amounts of each style to each author. [sent-368, score-0.141]

88 The resulting styles are shown in Table 3, for k = 10; other values of k give similar overall results with more or less detail. [sent-372, score-0.088]

89 The styles incorporate a number of linguistic phenomena, including: expressive lengthening (styles 7-9; see Brody and Diakopoulos, 2011); g- and t-dropping (style 5, see Eisenstein 2013a) ; th-stopping (style 6); and the dropping of several word-final vowels (styles 1-3). [sent-373, score-0.134]

90 Some of these styles, such as t-dropping and th-stopping, have direct analogues in spoken language varieties (Tagliamonte and Temple, 2005; Green, 2002), while others, like expressive lengthening, seem more unique to social media. [sent-374, score-0.132]

91 The relationships between these orthographic styles and social variables such as geography and demograph2We tried adding these rules as features and retraining the normalization system, but this hurt performance. [sent-375, score-0.615]

92 The tokens ima, outt a, and needa all refer to multi-word expressions in standard English, and are thus outside the scope of the normalization task as defined by Han et al. [sent-392, score-0.455]

93 But while these normalizations are wrong, the resulting style nonetheless captures a coherent orthographic phenomenon. [sent-395, score-0.248]

94 8 Conclusion We have presented a unified, unsupervised statistical model for normalizing social media text, attaining the best reported performance on the two standard normalization datasets. [sent-396, score-0.601]

95 The power of our approach comes from flexible modeling of word-to-word relationships through features, while exploiting contextual regularity to train the corresponding feature 70 weights without labeled data. [sent-397, score-0.09]

96 The primary technical challenge was overcoming the large label space of the normalization task; we accomplish this using sequential Monte Carlo. [sent-398, score-0.48]

97 Future work may consider whether sequential Monte Carlo can offer similar advantages in other unsupervised NLP tasks. [sent-399, score-0.171]

98 A hybrid rule/model-based finite-state framework for normalizing sms messages. [sent-420, score-0.121]

99 An overview of existing methods and recent advances in sequential monte carlo. [sent-453, score-0.337]

100 Joint inference of named entity recognition and normalization for tweets. [sent-576, score-0.326]

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