acl acl2012 acl2012-216 knowledge-graph by maker-knowledge-mining
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Author: Rada Mihalcea ; Vivi Nastase
Abstract: In this paper we introduce the novel task of “word epoch disambiguation,” defined as the problem of identifying changes in word usage over time. Through experiments run using word usage examples collected from three major periods of time (1800, 1900, 2000), we show that the task is feasible, and significant differences can be observed between occurrences of words in different periods of time.
Reference: text
sentIndex sentText sentNum sentScore
1 edu Abstract In this paper we introduce the novel task of “word epoch disambiguation,” defined as the problem of identifying changes in word usage over time. [sent-3, score-0.902]
2 Through experiments run using word usage examples collected from three major periods of time (1800, 1900, 2000), we show that the task is feasible, and significant differences can be observed between occurrences of words in different periods of time. [sent-4, score-0.603]
3 Language is continually changing: we discard or coin new senses for old words; metaphoric and metonymic usages become so engrained that at some point they are considered literal; and we constantly add new words to our vocabulary. [sent-7, score-0.197]
4 The purpose of the current work is to look at language as an evolutionary phenomenon, which we can investigate and analyze and use when working with text collections that span a wide time frame. [sent-8, score-0.153]
5 1 This has changed thanks to the Google books and Google Ngrams historical projects. [sent-10, score-0.181]
6 They make available in electronic format a large amount of textual data starting from the 17th century, as well as statistics on word usage. [sent-11, score-0.058]
7 We will exploit this data to find differences in word usage across wide periods of time. [sent-12, score-0.275]
8 1While the Brown corpus does include documents from different years, it is far from the scale and time range of Google books. [sent-13, score-0.061]
9 Vivi Nastase Institute for Computational Linguistics University of Heidelberg nastase @ cl . [sent-14, score-0.052]
10 de The phenomena involved in language change are numerous, and for now we focus on word usage in different time epochs. [sent-16, score-0.362]
11 As an example, the word gay, currently most frequently used to refer to a sexual orientation, was in the previous century used to express an emotion. [sent-17, score-0.235]
12 The word run, in the past used intransitively, has acquired a transitive sense, common in computational circles where we run processes, programs and such. [sent-18, score-0.058]
13 The purpose of the current research is to quantify changes in word usage, which can be the effect of various factors: changes in meaning (addition/removal of senses), changes in distribution, change in topics that co-occur more frequently with a given word, changes in word spelling, etc. [sent-19, score-1.079]
14 For now we test whether we can identify the epoch to which a word occurrence belongs. [sent-20, score-0.616]
15 We use two sets of words one with monosemous words, the other with polysemous ones to try and separate the effect of topic change over time from the effect of sense change. [sent-21, score-0.842]
16 We use examples from Google books, split into three epochs: 1800+/-25 years, 1900+/-25, 2000+/25. [sent-22, score-0.054]
17 We select open-class words that occur frequently in all these epochs, and words that occur frequently only in one of them. [sent-23, score-0.286]
18 We then treat each epoch as – – a “class,” and verify whether we can correctly predict this class for test instances from each epoch for the words in our lists. [sent-24, score-1.324]
19 To test whether word usage frequency or sense variation have an impact on this disambiguation task, we use lists of words that have different frequencies in different epochs as well as different polysemies. [sent-25, score-1.09]
20 As mentioned before, we also compare the performance ofmonosemous and thus (sensewise) unchanged through time and polysemous words, to verify whether we can in fact predict sense change as opposed to contextual variation. [sent-26, score-0.546]
21 c so2c0ia1t2io Ans fso rc Ciatoiomnp fuotart Cio nmaplu Ltiantgiounisatlic Lsi,n pgaugiestsi2c 5s9–263, 2 Related Work The purpose of this paper is to look at words and how they change in time. [sent-29, score-0.265]
22 Previous work that looks at diachronic language change works at a higher language level, and is not specifically concerned with how words themselves change. [sent-30, score-0.268]
23 The historical data provided by Google has quickly attracted researchers in various fields, and started the new field of culturomics (Michel et al. [sent-31, score-0.058]
24 The purpose of such research is to analyse changes in human culture, as evidenced by the rise and fall in usage of various terms. [sent-33, score-0.382]
25 Reali and Griffiths (2010) analyse the similarities between language and genetic evolution, with the transmission of frequency distributions over linguistic forms functioning as the mechanism behind the phenomenon of language change. [sent-34, score-0.228]
26 Blei and Lafferty (2006) and Blei and Lafferty (2007) track changes in scientific topics through a discrete dynamic topic model (dDTM) both as types of scientific topics at different time points, and as changing word probability distributions within these topics. [sent-35, score-0.549]
27 The “Photography” topic for example has changed dramatically since the beginning of the 20th century, with words related to digital photography appearing recently, and dominating the most current version of the topic. [sent-36, score-0.258]
28 (2008) develop time-specific topic models, where topics, as patterns of word use, are tracked across a time changing text collection, and address the task of (fine-grained) time stamp prediction. [sent-38, score-0.306]
29 Wijaya and Yeniterzi (201 1) investigate through topic models the change in context of a specific entity over time, based on the Google Ngram corpus. [sent-39, score-0.211]
30 They determine that changes in this context reflect events occurring in the same period of time. [sent-40, score-0.2]
31 – 3 Word Epoch Disambiguation We formulate the task as a disambiguation problem, where we automatically classify the period of time when a word was used, based on its surrounding context. [sent-41, score-0.394]
32 We use a data-driven formulation, and draw examples from word occurrences over three different epochs. [sent-42, score-0.143]
33 For the purpose of this work, we consider an epoch to be a period of 50 years surrounding the beginning of a new century (1800+/25 years, 1900+/-25, 2000+/-25). [sent-43, score-0.834]
34 The word usage examples are gathered from books, where the publi260 cation year of a book is judged to be representative for the time when that word was used. [sent-44, score-0.357]
35 We select words with different characteristics to allow us to investigate whether there is an effect caused by sense change, or the disambiguation performance comes from the change of topics and vocabulary over time. [sent-45, score-0.646]
36 The choice of target words for our experiments is driven by the phenomena we aim to analyze. [sent-47, score-0.132]
37 According to these criteria, for each open class (nouns, verbs, adjectives, adverbs) we select 50 words, 25 of which have multiple senses, 25 with one sense only. [sent-49, score-0.191]
38 Each of these two sets has a 105-5-5 distribution: 10 words that are frequent in all three epochs, and 5 per each epoch such that these words are only frequent in one epoch. [sent-50, score-0.878]
39 To avoid partof-speech ambiguity we also choose words that are unambiguous from this point of view. [sent-51, score-0.124]
40 This selection process was done based on Google 1gram historical data, used for computing the probability distribution of open-class words for each epoch. [sent-52, score-0.149]
41 2 The set of target words consists thus of 200 open class words, uniformly distributed over the 4 parts of speech, uniformly distributed over multiple- sense/unique sense words, and with the frequency based sample as described above. [sent-53, score-0.473]
42 From this initial set of words, we could not identify enough examples in the three epochs considered for which left us with a final set of 165 words. [sent-54, score-0.505]
43 For each target word in our dataset, we collect the top 100 snippets returned by a search on Google Books for each of the three epochs we consider. [sent-56, score-0.552]
44 35,3 2For each open class word we create ranked lists of words, where the ranking score is an adjusted tfidf score – the epochs correspond to documents. [sent-57, score-0.569]
45 To choose words frequent only in one epoch, we choose the top words in the list, for words frequent in all epochs we choose the bottom words in this list. [sent-58, score-1.017]
46 3A minimum of 30 total examples was required for a word to be considered in the dataset. [sent-59, score-0.147]
47 All the extracted snippets are then processed: the text is tokenized and part-of-speech tagged using the Stanford tagger (Toutanova et al. [sent-60, score-0.037]
48 , 2003), and contexts that do not include the target word with the specified part-of-speech are removed. [sent-61, score-0.099]
49 The position of the target word is also identified and recorded as an offset along with the example. [sent-62, score-0.099]
50 For illustration, we show below an example drawn from each epoch for two different words, dinner: 1800: On reaching Mr. [sent-63, score-0.558]
51 Crane’s house, dinner was set before us ; but as is usual here in many places on the Sabbath, it was both dinner and tea combined into a single meal. [sent-64, score-0.26]
52 1900: The average dinner of today consists of relishes; of soup, either a consomme (clear soup) or a thick soup. [sent-65, score-0.13]
53 2000: Preparing dinner in a slow cooker is easy and convenient because the meal you’re making requires little to no attention while it cooks. [sent-66, score-0.186]
54 and surgeon: 1800: The apothecaries must instantly dispense what medicines the surgeons require for the use of the regiments. [sent-67, score-0.028]
55 1900: The surgeon operates, collects a fee, and sends to the physician one-third or onehalf of the fee, this last transaction being unknown to the patient. [sent-68, score-0.154]
56 2000: From a New York plastic surgeon comes all anyone ever wanted to know–and never imagined–about what goes on behind the scenes at the office of one of the world’s most prestigious plastic surgeons. [sent-69, score-0.312]
57 The classification algorithm we use is inspired by previous work on datadriven word sense disambiguation. [sent-71, score-0.182]
58 Specifically, we use a system that integrates both local and topical features. [sent-72, score-0.044]
59 The local features include: the current word and its part-of-speech; a local context of three words to the left and right of the ambiguous word; the parts-of-speech of the surrounding words; the first noun before and after the target word; the first verb before and after the target word. [sent-73, score-0.339]
60 The topical features are determined from the global context and are implemented through class-specific keywords, which are determined as a list of at most five words occurring at least three times in the contexts defining a certain word class (or epoch). [sent-74, score-0.26]
61 To evaluate word epoch disambiguation, we calculate the average accuracy obtained through ten-fold cross-validations applied on the data collected for each word. [sent-92, score-0.616]
62 To place results in perspective, we also calculate a simple baseline, which assigns the most frequent class by default. [sent-93, score-0.136]
63 Overall, the task appears to be feasible, as absolute improvements of 18. [sent-95, score-0.072]
64 While improvements are obtained for all parts-ofspeech, the nouns lead to the highest disambiguation results, with the largest improvement over the baseline, which interestingly aligns with previous observations from work on word sense disambiguation (Mihalcea and Edmonds, 2004; Agirre et al. [sent-97, score-0.606]
65 There are also words that experience very small improvements, such as “again” (3%) or “captivate” (7%), which are words that are frequently used in all three epochs. [sent-100, score-0.264]
66 There are even a few words (seven) for which the disambiguation accuracy is below the baseline, such as “oblige” (-1%) or “cruel” (-15%). [sent-101, score-0.284]
67 To understand to what extent the change in frequency over time has an impact on word epoch disambiguation, in Table 2 we report results for words that have high frequency in all three epochs considered, or in only one epoch at a time. [sent-102, score-2.023]
68 As expected, the words that are used more often in an epoch are also easier to disambiguate. [sent-103, score-0.649]
69 4 For instance, the 4The difference in results does not come from difference in verb “reassert” has higher frequency in 2000, and it has a disambiguation accuracy of 67. [sent-104, score-0.341]
70 Instead, the verb “conceal,” which appears with high frequency in all three epochs, has a disambiguation accuracy of 44. [sent-107, score-0.341]
71 70%, which is a relatively small improvement over the baseline of 38. [sent-108, score-0.054]
72 POS words examples Baseline WED High frequency in all epochs Noun1818042. [sent-113, score-0.643]
73 86% Table 2: Results for words that have high frequency in all epochs, or in one epoch at a time The second analysis that we perform is concerned with the accuracy observed for polysemous words as compared to monosemous words. [sent-133, score-1.305]
74 Monosemous words do not have sense changes over time, so being able to classify them in different epochs relies exclusively on variations in their context over time. [sent-135, score-0.791]
75 Polysemous words’s context change because of both changes in topics/vocabulary over time, and changes in word senses. [sent-136, score-0.495]
76 The fact that we see a difference in accuracy between disambiguation results for monosemous and polysemous words is an indication that word sense change is reflected and can be captured in the context. [sent-137, score-0.973]
77 To better visualize the improvements obtained with word epoch disambiguation with respect to the baseline, Figure 1plots the results. [sent-138, score-0.847]
78 6 Conclusions In this paper, we introduced the novel task of word epoch disambiguation, which aims to quantify the changes in word usage over time. [sent-139, score-0.999]
79 Using examples collected from three major periods of time, for 165 words, we showed that the word epoch disambiguation algorithm can lead to an overall absolute imsize in the data, as the number of examples extracted for words of high or low frequency is approximately the same. [sent-140, score-1.215]
80 262 By epoch frequency By number of senses Figure 1: Word epoch disambiguation compared to the baseline, for words that are frequent/not frequent (in a given epoch), and monosemous/polysemous. [sent-141, score-1.622]
81 POS words examples Baseline WED Polysemous words Noun2419141. [sent-145, score-0.236]
82 96% Table 3: Results for words that are polysemous monosemous. [sent-165, score-0.285]
83 5%, as compared to a baseline that picks the most frequent class by default. [sent-167, score-0.19]
84 These results indicate that there are significant differences between occurrences of words in different periods of time. [sent-168, score-0.213]
85 Moreover, additional analyses suggest that changes in usage frequency and word senses contribute to these differences. [sent-169, score-0.497]
86 In future work, we plan to do an in-depth analysis of the features that best characterize the changes in word usage over time, and develop representations that allow us to track sense changes. [sent-170, score-0.468]
87 Acknowledgments This material is based in part upon work supported by the National Science Foundation CAREER award #0747340. [sent-171, score-0.028]
88 Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. [sent-172, score-0.028]
89 An empirical evaluation of knowledge sources and learning algorithms for word sense disambiguation. [sent-197, score-0.182]
90 Quantitative analysis of culture using millions of digitized books. [sent-221, score-0.046]
91 Integrating multiple knowledge sources to disambiguate word sense: An examplar-based approach. [sent-234, score-0.058]
92 Words as alleles: connecting language evolution with bayesian learners to models of genetic drift. [sent-240, score-0.085]
93 Topics over time: A non-Markov continuous-time model of topical trends. [sent-254, score-0.044]
wordName wordTfidf (topN-words)
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