Word and Sentence Segmentation in German: Overcoming Idiosyncrasies in the Use of Punctuation in Private Communication
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In this paper, we present a segmentation system for German texts. We apply conditional random fields (CRF), a statistical sequential model, to a type of text used in private communication. We show that by segmenting individual punctuation, and by taking into account freestanding lines and that using unsupervised word representation (i. e., Brown clustering, Word2Vec and Fasttext) achieved a label accuracy of 96% in a corpus of postcards used in private communication.
In tokenisation and sentence segmentation, a text is segmented into tokens and sentences. Word and sentence segmentation are the core components of NLP pipelines. Based on text segmentation, part of speech (POS) tagging and parsing, among other tasks, are performed.
In German texts, segmentation is de facto to classify sentence punctuation, such as periods, question marks and exclamation marks, into two categories: (A) the ends of sentences, and (B) others, such as components of abbreviations (e. g., evtl., eventuell ‘possibly’), proper names (e. g., Sat.1), numbers (e. g., 13.000) and so on. In the case of (A), the punctuation is separated from space-delimited tokens and analysed as individual tokens. In the case of (B), the punctuation constitutes a token with the preceding characters. Therefore, space-delimited tokens are not segmented further. In rare cases, punctuation that is used to mark the end of a sentence (i. e., category [A]) is a part of the token (i. e., category [B]) at the end of a sentence.1
Use of punctuations (TüBa10)
Last tokens of sentences ranking:tokens (frequency, %)
1:Period (73904, 77.31%), 2:double quotation (3849, 4.03%), 3:question mark (2921, 3.06%), 4:colon (2369, 2.48%), 5:exclamation mark (682, 0.71%), 6:semicolon (634, 0.66%), 7:parentheses(393, 0.41%), 8:ellipsis (329, 0.34%), 11:guillemet (59, 0.06%), 21:comma (26, 0.03%), 22:square bracket(26, 0.03%), 25:hypen (24, 0.03%), 37:single quote (18, 0.02%), 212:slash (6, 0.01%), else (10355, 10.83%)
Ambiguity of punctuations tokens (A: frequency of case (A), the total number of the character, AMB(iguity):A/total(%)
Period (A:73904 + 329*3, total:88938, AMB:84.20%), double quotation (A:3849, total:42468, AMB:9.06%), question mark (A:2921, total:3536, AMB:82.60%), colon (A:2369, total:11522, AMB:20.56%), exclamation mark (A:682, total:1424, AMB:47.89%), semicolon (A:634, total:, AMB:%), parentheses (A:393, total:5999, AMB:6.55%), guillemets (A:59, total:369, AMB:15.98%), comma (A:26, total:102425, AMB:0.02%), square bracket (A:26, total:75, AMB:34.66%), hypen (A:24, total:29863, AMB:0.08%), single quote (A:18, total:730, AMB:2.46%), slash (A:6, total:2065, AMB:0.29%)
However, the task of text segmentation is not trivial if we address the following dependencies (Palmer 2000): (1) language dependence, (2) corpus dependence and (3) application dependence. Thus, the segmentation of multi-lingual texts (Jurish and Würzner 2013; Kiss and Strunk 2006) is not rule-based but statistical. Corpus dependence involves a wide range of text types that have various linguistic features. Lastly, the definitions of words and sentences depend on the NLP application: for example, in a machine translation, a German compound is better split into individual morphemes (El-Kahlout and Yvon 2010).
In this work, we focus on the development of a German text segmentation system that deals with the issue of corpus dependence in Palmer’s term. More specifically, it has been observed – e. g., by Giesbrecht and Evert (2009) for part-of-speech-tagging and by Gildea (2001) for parsing – that a statistical model usually works for the domain and text types it has been trained for, but leaves to desire when applied to other domains and text types. In this work, we undertake domain adaptation in text segmentation, in particular, with a target domain – texts written in private communication. Typically, these texts contain many deviations from standard orthography, including idiosyncrasies in capitalisation and punctuation.
In this paper, we train text segmentation models (conditional random fields) on TüBa10 (Sect. 4) and test them on an example of a text used in private communication: a postcard corpus4 (Ansichtskartenkorpus, ‘picture postcard corpus’, henceforth ANKO) (Sect. 5). Sections 2 and 3 provide the analysis of the use of punctuation in private communication, and describe our text segmentation system.
2 Use of Punctuation
In German, punctuation segments a text into sentences, and a sentence is segmented into words by spaces. However, these rules of thumb are not applicable in the following cases of sentence segmentation: (1) punctuation that is a part of a token with preceding characters (e. g., abbreviations); and (2) punctuation is absent. Case (1) was discussed in Sect. 1. Case (2) occurs because of freestanding lines. Freestanding lines typically end with a line break with a wide blank space or extra line spacing, and often do not end with a punctuation. Examples are titles, subtitles, addresses, dates, greetings, salutations and signatures (Official German Orthography 2006). In private communication, the rules for freestanding lines are also applied to the end of paragraphs. In addition, the following usage is common to the punctuation in a private communication: (a) repeated punctuation (e. g., !!!, ???,......) in order to emphasise words, phrases and sentences; and (b) the use of emotional pictograms that are typically composed of punctuation (e. g., :), ;-)) (cf. Bartz et al. (2013)).
3 Conditional Random Fields (CRF)-Based Text Segmentation
We develop a CRF-based German text segmentation system that can be applied to the types of texts used in private communication (cf. Section 2). We focus on tokenisation with punctuation and sentence segmentation. In this section, we briefly introduce CRF and define the notion of a sequence and a set of features used in the task.
3.1 Conditional Random Fields
The CRF model learns the parameters and decodes the output based on a given sequence of input units. In our classification task, a text is a sequence of input units. We use the term unit to denote each atomic element in the CRF in order to differentiate it from the term token or word. We create units by splitting texts using white spaces and by separating punctuation from the attached characters. We then classify the input units into three categories: the beginning (B), intermediate (I) and end (E) of sentences. Using this notation, the chunk of a token is also marked.
Punctuation before a white space is regarded as a unit. For example, z.B. (abbreviation of zum Beispiel ‘for example’) consists of two units: z.B and .
Punctuation is regarded as a unit regardless of a white space. Accordingly, z.B. consists of four units: z, ., B and .
All punctuation is removed if it is followed by a white space. Accordingly, z.B. consists of one unit: z.B
Variant (a) is a setting in which the white space is well placed, and it follows standard German orthographic rules. In Variant (b), every punctuation mark is individually handled, which is expected to provide flexibility in orthographical deviations. In Variant (c), punctuation is missing in the input text.
Features are key linguistic indicators that may be useful in the segmentation of sentences. In this work, we use three types of features to handle orthographic variations and unknown words in variations of types of text: forms, POS and unsupervised word representations. The following subsections describe each feature in detail (Table 2 in Appendix).
Form. Forms of units are integrated as three features: (1) unit, (2) character types of unit and (3) normalised unit. For the first feature, units are extracted as they are. In the second feature, units are categorised into alphabetic, numeric, types of special characters, and their combinations. For the third feature, units are changed to lower case.
Part of speech. POS is used as a feature in two ways: fine-grained original Stuttgart-Tübingen-TagSet (STTS, Schiller et al. (1999)) or coarse POS (CPOS), which are shown in Table 2. These two features are extracted automatically using the TreeTagger (Schmid 1999).
Brown clustering. For the first feature of unsupervised word clustering, the hierarchical classes of Brown clustering (Brown et al. 1992) are exploited. Brown clustering is a bigram-based word clustering that has been successfully integrated to improve parsing (Koo et al. 2008), domain adaptation (Candito et al. 2011) and named entity recognition (Miller et al. 2004). We ran Brown clustering on the normalised tokens (i. e., all lower case) of TüBa10 to build 100 clusters.5 For the features, we used the first four digits and all digits in the clustering hierarchy. In the data, the grouping of named entity such as person and organization and the part of speech such as noun and verb were captured the most clearly in the first four digits of the clustering.
Word2Vec. For the second feature of unsupervised methods, we used k-means clustering in Word2Vec. Word2vec (Mikolov et al. 2013) is another kind of word representation. We ran the Word2Vec on the normalised tokens of TüBa10 to build the models.6 To operationalise the word-embedding vectors, we further grouped them into 50 K-means clusters.7 The resulting clusters contained a great deal of named entities.
Fasttext. For the third feature of unsupervised methods, we used k-means clustering in Fasttext. Fasttext (Bojanowski et al. 2016) is yet another kind of word representation that takes into account character n-grams (morpheme). We ran Fasttext on the normalised tokens of TüBa10 to build the models.8 We further grouped them into 200 K-means clusters that contained a large number of German compounds.
In this experiment, our goal was to develop a text segmentation model that could robustly be applied to domain difference. For the experiment, we used the TüBa10 in form of (a), (b) and (c) (cf. Sect. 3.2) with various feature configurations, and we trained and tested the CFG models using five-fold cross-validation. In the next section, we evaluate the models by applying them to a postcard corpus to test their robustness for texts generated in private communications.
Single features. In the experiment, we used data in the forms of (a), (b) and (c) with single features. First, we trained CRF models in context window 0. The results are shown in columns #1 to #12 of Table 3 in Appendix. Among the features, the character type of unit (#3) – information about capitalisation, and type of punctuation and characters – showed the best performances in sequence types (a) and (b), whereas gold STTS POS tag (#4) showed the best performance in (c). In the unsupervised methods, Brown clustering (#8/9) outperformed Word2Vec (#10) and Fasttext (#11). As expected, the sequence types (a) and (b) achieved higher accuracy than sequence (c) did. For the sequence type (c), that is, the input sequence without punctuation, all individual features did not predict the classes (B) and (E). Thus, punctuation was proven relevant in text segmentation. We extended the set of features in context window 3. However, the accuracy remained the same as in window 0.
Feature combinations. To obtain linguistic information effectively in wide contexts, we combined the features in the following two ways: (1) the same features in context 0 and 1; (2) the combination of two features (1a/1b/1c/2bT/3a4/3b/3c) in context 0. In the first setting, which combined all with all features of previous, current and next tokens, the CRF models improved with regard to class (B) and (E) (#13 in Table 3). In the second feature combination (#14), the overall accuracy was similar to that in the set of all single features (#12).
For the evaluation, we trained all single features in context 0 (#12), the combinations of the same features in context window 1 (#13) and those of various features in context window 0 (#14) without using gold POS and CPOS tags. The CRF models achieved accuracies of 0.99, 0.99 and 0.97 on the TüBa in the sequence types (a), (b) and (c), respectively. In the evaluation, we used the feature set for each input sequence type.
5 Evaluation and Conclusion
F1(B, I, E)
F1(B, I, E)
F1(B, I, E)
.73, .94, .72
.80, .95, .77
.88, .97, .84
.76, .95, .44
.82, .97, .61
.90, .98, .82
.30, .90, .42
.40, .90, .55
.50, .92, .73
Overall, the sequence type (b) achieved better accuracy than sequence type (a) did, which showed that orthographic deviations could be handled more effectively by segmenting punctuation individually. Clearly, the patterns of punctuation were more generally captured in (b). Furthermore, the input text type (III) achieved high accuracy. These results indicate that the annotation of a corpus with paragraphs and freestanding lines is relevant in improving the quality of the segmentation of texts used in private communication. Still, it was difficult to predict text segments without having punctuations (c) on a type of text different from the training data.9
As comparison, we tested a sentence segmentation system PUNKT (Kiss and Strunk 2006).10 PUNKT is based on unsupervised methods and designed for multi-lingual text segmentation. We tested PUNKT on our ANKO test set type (III). PUNKT achieved a F1 score of 0.79 with precision 0.71 and recall 0.9. In contrast, our sentence segmentation system achieved a F1 score of 0.95 with precision 0.94 and recall 0.96, using the input format (b) and (III), that is, the best input format for tokenization.
In conclusion, we presented our German text segmentation system for texts in private communication. In future work, we will extend our text segmentation system on historical German texts.
For instance, 176 (0.18%) in 95.595 sentences belong to the third category in TüBa10. An example is usw. at the end of a sentence.
Helmut Schmid’s tokenizer in TreeTagger: http://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/; Stefanie Dipper’s system: https://www.linguistics.ruhr-uni-bochum.de/~dipper/resources/tokenizer.html.
However, lists of abbreviations are never complete, and need to be extended, when we use out-of-domain data.
The corpus will be released in https://linguistik.zih.tu-dresden.de/ansichtskarten.
We used the Brown clustering implemented by P. Liang.
For word2vec, we used gensim with parameters CBOW, 200 dimensions, context window 5.
For K-means clustering, we used the scikit-learn.
We used the fasttext with parameters, CBOW, 200 dimensions, 5 context window, 5 word ngrams.
Our text segmentation system (GETS) is available: https://sugisaki.ch/tools.
We used the NLTK module PUNKT.
This work is funded under SNSF grant 160238. We thank all the project members, Heiko Hausendorf, Joachim Scharloth, Noah Bubenhofer, Nicolas Wiedmer, Selena Calleri, Maaike Kellenberger, David Koch, Marcel Naef, Josephine Obert, Jan Langenhorst, Michaela Schnick to support our work. We thank two anonymous reviewers for their valuable suggestions.
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