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Microsoft word - eacl2012_leijten_macke_et al_definitive.docx

From character to word level:
Enabling the linguistic analyses of Inputlog process data
Mariëlle Leijten
Lieve Macken
LT3, Language and Translation Technology Team, University College Ghent and Ghent Veronique Hoste
Eric Van Horenbeeck
LT3, Language and Translation Technology Team, University College Ghent and Ghent Luuk Van Waes
1 Introduction
Keystroke logging is a popular method in writing Keystroke logging tools are widely used in writing research (Sullivan & Lindgren, 2006) to study the process research. These applications are designed underlying cognitive processes (Berninger, 2012). to capture each character and mouse movement as Various keystroke logging programs have been isolated events as an indicator of cognitive processes. The current research project explores developed, each with a different focus1. The programs the possibilities of aggregating the logged process differ in the events that are logged (keyboard and/or data from the letter level (keystroke) to the word mouse, speech recognition), in the environment that is level by merging them with existing lexica and logged (a program-specific text editor, MS Word or using NLP tools. Linking writing process data to all Windows-based applications), in their combination lexica and using NLP tools enables researchers to with other logging tools (e.g. eye tracking and analyze the data on a higher, more complex level. usability tools like Morae) and the analytic detail of In this project the output data of Inputlog are the output files. Examples of keystroke logging tools segmented on the sentence level and then tokenized. However, by definition writing process Scriptlog: Text editor, Eyetracking (Strömqvist, grammatical text. Coping with this problem was Holmqvist, Johansson, Karlsson, & Wengelin, one of the main challenges in the current project. Therefore, a parser has been developed that  Inputlog: Windows environment, speech extracts three types of data from the S-notation: recognition (Leijten & Van Waes, 2006), word-level revisions, deleted fragments, and the  Translog: Text editor, integration of dictionaries final writing product. The within-word typing (Jakobsen, 2006) (Wengelin et al., 2009). errors are identified and excluded from further analyses. At this stage the Inputlog process data are enriched with the following linguistic information: part-of-speech tags, lemmas, chunks, A detailed overview of available keystroke logging syllable boundaries and word frequencies. programs can be found on logging_programs.php. Keystroke loggers' data output is mainly based on The remainder of this paper is structured as follows. capturing each character and mouse movement as Section 2 describes the output of Inputlog, and section isolated events. In the current research project 2 we 3 describes an intermediate level of analysis. Section explore the possibilities of aggregating the logged 4 describes the flow of the linguistic analyses and the process data from the letter level (keystroke) to the various linguistic annotations. Section 5 wraps up word level by merging them with existing lexica and with some concluding remarks and suggestions for Linking writing process data to lexica and using NLP tools enables us to analyze the data on a higher, more 2 Inputlog
complex level. By doing so we would like to stimulate interdisciplinary research, and relate findings in the Inputlog is a word-processor independent keystroke domain of writing research to other domains (e.g., logging program that not only registers keystrokes, Pragmatics, CALL, Translation studies, Psycho- mouse movements, clicks and pauses in MS Word, but also in any other Windows-based software We argue that the enriched process data combined with temporal information (time stamps, action times Keystroke logging programs store the complete and pauses) will further facilitate the analysis of the sequence of keyboard and/or mouse events in logged data and address innovative research chronological order. Figure 1 represents "Volgend questions. For instance, Is there a developmental shift jaar (Next Year)" at the character and mouse action in the pausing behaviors of writers related to word classes, e.g., before adjectives as opposed to before The keyboard strokes, mouse movements and mouse nouns (cf. cognitive development in language clicks are represented in a readable output for each production)? Do translation segments correspond to action (e.g., 'SPACE' refers to the spacebar, LEFT linguistic units (e.g., comparing speech recognition Click is a left mouse click, and 'Movement' is a and keyboarding)? Which linguistic shifts synthesized representation of a continuous mouse characterize substitutions as a sub type of revisions movement). Additionally, timestamps indicate when (e.g., linguistic categories, frequency)? keys are pressed and released, and when mouse movements are made. For each keystroke in MSWord A more elaborate example of a research question in the position of the character in the document is which the linguistic information has added value is: Is represented as well as the total length of the document the text prodcution of causal markers more cognitive at that specific moment. This enables researchers to demanding than the production of temporal markers? take the non-linearity of the writing process into In reading research, evidence is found that it takes account, which is the result of the execution of readers longer to process sentences or paragraphs that contain causal markers than temporal markers. Does the same hold for the production of these linguistic markers? Based on the linguistic information added to the writing process data researchers are now able to easily select causal and temporal markers and compare the process data from various perspectives. (cf. step 4 - linguistic analyses). The work described in this paper is based on the output of Inputlog3, but it can also be applied to the output of other keystroke logging programs. To promote more linguistically-oriented writing process research, Inputlog aggregates the logged process data from the character level (keystroke) to the word level. In a subsequent step, we use various Natural Language Processing (NLP) tools to further annotate Figure 1. Example of general analysis Inputlog. the logged process data with different kinds of linguistic information: part-of-speech tags, lemmata, To represent the non-linearity of the writing process chunk boundaries, syllable boundaries, and word the S-notation is used. The S-notation (Kollberg & Severinson Eklundh, 2002) contains information about the revision types (insertion or deletion), the order of the revisions and the place in the text where 2 FWO-Merging writing process data with lexica - the writing process was interrupted. The S-notation can be automatically generated from the keystroke logging data and has become a standard in the representation of the non-linearity in writing to be the most stable characteristic of a keyboard user. processes. Another example is the work by Nottbush and his colleagues. Focusing on linguistic aspects of interkey Figure 2 shows an example of the S-notation. The text intervals, their research (Nottbusch, 2010; Sahel, is taken from an experiment with master students Nottbusch, Grimm, & Weingarten, 2008) shows that Multilingual Professional Communication who were the syllable boundaries within words have an effect on asked to write a (Dutch) tweet about a conference the temporal keystroke succession. Syllable (VWEC). The S-notation show the final product and boundaries lead to increased interkey intervals at the In recent research Inputlog data has also been used to ngres·[over·']1|1[met·als·thema|10]9{over}10·'Corporate·Com analyze typing errors at this level (Van Waes & munication{'|8}7.[.]2|2[·Wat·levert·het·op?'.|7]6·Blijf·[ons·vo Leijten, 2010). As will be demonstrated in the next lgen·op|5]4{op·de·hoogte·via|6}5·|3· section, typing errors complicate the analysis of logging data at the word and sentence level because the linear reconstruction is disrupted. For this purpose a large experimental corpus based on a controled The following conventions are used in the S-notation: copying task was analyzed, focusing on five digraphs with different characteristics (frequency, keyboard distribution, left-right coordination). The results of a {insertion}i An insertion occurring after break i multilevel analysis show that there is no correlation [deletion]i A deletion occurring after break i between the frequency of a digraph and the chance that a typing error occurs. However, typing errors The example in Figure 2 can be read as follows: show a limited variation: pressing the adjacent key The writer formulates in one segment "Volgend jaar explains more than 40 % of the errors, both for touch organiseert VWEC een congres over '" (Next year typists and others; the chance that a typing error is VWEC organises a conference on '). She decides to made is related to the characteristics of the digraph, delete "over '" (index 1) and then adds the remainder and the individual typing style. Moreover, the median of her first draft "met als thema 'Corporate pausing time preceding a typing error tends to be Communication. Wat levert het op?. (themed longer than the median interkey transitions of the Corporate Communication. What is in it for us?.)" intended digraph typed correctly. These results She deletes a full stop and ends with "Blijf ons volgen illustrate that further research should make it possible op" (Follow us on to identify and isolate typing errors in logged process The third revision is the addition data and build an algorithm to filter them during data of the hashtag before VWEC. Then she rephrases "ons preparation. This would benefit parsing at a later stage volgen op" into "op de hoogte via". She notices that her tweet is too long (max. 140 characters) and she decides to delete the subtitle of the conference. She 4 Flow of linguistic analyses
adds the adjective "boeiend" (interesting) to conference and ends by deleting "met als thema" As explained above, writing process data gathered via the traditional keystroke logging tools, are represented at the character level and produce non-linear data 3 Intermediate level
(containing sentence fragments, unfinished sentences/words and spelling errors). These two At the intermediate level, Inputlog data can also be characteristics are the main obstacles that we need to used to analyze data at the digraph level, for instance, cope with to analyse writing process data on a higher to study interkey intervals (or digraph latency) in level. In this section we explain the flow of the relation to typing speed, keyboard efficiency of touch typists and others, dyslexia and keyboard fluency, biometric verification etc. For this type of research, Step 1 - aggregate letter to word level
logging data can be leveled up to an intermediate Natural Language Processing tools, such as part-of- level in which two consecutive events are treated as a speech taggers, lemmatizers and chunkers are trained on (completed) sentences and words. Therefore, to use the standard NLP tools to enrich the process data Grabowski’s research on the internal structure of with linguistic information, in a first step, words, students’ keyboard skills in different writing tasks is a word groups and sentences are extracted from the case in point (Grabowski, 2008). He studied whether there are patterns of overall keyboard behavior and whether such patterns are stable across different The S-notation was used as a basis to further segment (copying) tasks. Across tasks, typing speed turned out the data into sentences and tokenize them. A dedicated sentence segmenting and tokenizer module into three types of revisions and the within-word was developed to conduct this process. This dedicated typing errors are excluded from further analyses. module can cope with the specific S-notation Although the set-up of the Inputlog extension is annotations such as insertion, deletion and break largely language-independent, the NLP tools used are markers. language-dependent. As proof-of-concept, we provide evidence from English and Dutch (See Figure 3). Step 2 – parsing the S-notation
As mentioned before, standard NLP tools are designed to work with clean, grammatically correct text. We thus decided to treat word-level revisions differently than higher level revisions and to distinguish deleted fragments from the final writing product. We developed a parser that extracts three types of data from the S-notation: word-level revisions, deleted fragments and the final writing product. The word-level revisions can be extracted from the S-notation by retaining all words with word-internal square or curly brackets (see excerpt 1). (1 - word level revision) Delet[r]ion incorrect: Deletrion; correct: deletion In{s}ertion incorrect: Inertion; correct: insertion Figure 3. Flow of the linguistic analyses. Step 3 - enriching process data with linguistic
Conceptually, the deleted fragments can be extracted information
from the S-notation by retaining only the words and As standard NLP tools are trained on clean data, these phrases that are surrounded by word-external square tools are not suited for processing input containing brackets (2); and the final product data can be spelling errors. Therefore, we only enrich the final obtained by deleting everything in between square product data and the deleted fragments with different brackets from the S-notation. In practice, the situation kinds of linguistic annotations. As part-of-speech is more complicated as insertions and deletions can be taggers typically use the surrounding local context to determine the proper part-of-speech tag for a given word (typically a window of two to three words An example of the three different data types extracted and/or tags is used), the deletions in context are from the S-notation is presented in the excerpt below. extracted from the S-notation to be processed by the To facilitate the readability of the resulting data, the part-of-speech tagger. The deleted fragments in context consist of the whole text string without the insertions and are only used to optimize the results of Volgend·jaar·organiseert·{#}VWEC·een·{boeiend·}c ongres·[over·'][met·als·thema]{over}·'Corporate·Co mmunication{'}.[.][·Wat·levert·het·op?'.]·Blijf·[ons·v Volgend·jaar·organiseert·{#}VWEC·een·{boeiend·}c olgen·op]{op·de·hoogte·via|}·|· ongres·[over·'][met·als·thema]{over}·'Corporate·Co mmunication{'}.[.][·Wat·levert·het·op?'.]·Blijf·[ons·v olgen·op]{op·de·hoogte·via|}·|· Volgend·jaar·organiseert·{#}VWEC·een·{boeiend·}congres·[over·'][met·als·thema]{over}·'Corporate·Co mmunication{'}.[.][·Wat·levert·het·op?'.]·Blijf·[ons·v Annotations
For the shallow linguistic analysis, we used the LT3
olgen·op]{op·de·hoogte·via|}·|· shallow parsing tools suite consisting of: Next year #VWEC organises an interesting  a chunker (LeTsCHUNK). conference about Corporate Communication. Follow The LT3 tools are platform-independent and hence In sum, the output of Inputlog data is segmented in sentences and tokenized. The S-notation is divided In the IOB-tagging scheme, each token belongs to one The English PoS tagger uses the Penn Treebank tag of the following three types: I (inside), O (outside) set, which contains 45 distinct tags. The Dutch part- and B (begin); the B- en I-tags are followed by the of-speech tagger uses the CGN tag set codes (Van chunk type, e.g. B-VP, I-VP. We adapted the IOB- Eynde, Zavrel, & Daelemans, 2000), which is tagging scheme and added end tag (E) to explicitly characterized by a high level of granularity. Apart mark the end of a chunk. Accuracy sores of part-of- from the word class, the CGN tag set codes a wide speech taggers and lemmatizers typically fluctuate range of morpho-syntactic features as attributes to the around 97-98%; accuracy scores of 95-96% are word class. In total, 316 distinct tags are discerned. After annotation, the final writing product, deleted During lemmatization, for each orthographic token, fragments and word-level corrections are aligned and the base form (lemma) is generated. For verbs, the the indices are restored. Figure 4 and 5 show how we base form is the infinitive; for most other words, this enriched the logged process data with different kinds base form is the stem, i.e. the word form without of linguistic information: lemmata, part-of-speech inflectional affixes. The lemmatizers make use of the predicted PoS codes to disambiguate ambiguous word forms, e.g. Dutch "landen" can be an infinitive (base We further added some word-level annotations on the form "landen") or plural form of a noun (base form final writing product and the deletions, viz. syllable "land"). The lemmatizers were trained on the English boundaries and word frequencies (see last two and Dutch parts of the Celex lexical database columns in Figure 4 and 5). respectively (Baayen, Piepenbrock, & van Rijn, 1993). The syllabification tools were trained on Celex ( During text chunking syntactically related consecutive Syllabification was approached as a classification words are combined into non-overlapping, non- task: a large instance base of syllabified data is recursive chunks on the basis of a fairly superficial presented to a classification algorithm, which analysis. The chunks are represented by means of automatically learns from it the patterns needed to IOB-tags. syllabify unseen data. Accuracy scores for syllabification reside in the range of 92-95%. Figure 4. Final writing product and word-level revisions enrich Table 1. Example of process data and linguistic information Frequency lists for Dutch and English were compiled on the basis of Wikipedia pages, which were extracted from the XML dump of the Dutch and English Wikipedia of December 2011. We used the Wikipedia Extractor developed by Medialab4 to extract the text from the wiki files. The Wikipedia text files were further tokenized and enriched with part-of-speech B (begin) tags and lemmata. The Wikipedia frequency lists can thus group different word forms belonging to one lemma. The current version of the Dutch frequency list has been compiled on the basis of nearly 100 million In this example the mean pausing time before tokens coming from 395,673 wikipedia pages, which adjectives is twice as long as before nouns. The is almost half of the Dutch wikipedia dump of pausing time after such a segment shows the opposite December 2011. proportion. Also pauses in the beginning of chunks are more than twice as long as in the middle of a Frequencies are presented as absolute frequencies. 5 Future research
In this paper we presented how writing process data can be enriched with linguistic information. The annotated output facilitates the linguistic analysis of the logged data and provides a valuable basis for more linguistically-oriented writing process research.We hope that this perspective will further enrich writing process research. Additional annotations and analyses In a first phase we only focused on English and Dutch, but the method can be easily applied to other languages as well provided that the linguistic tools are available for a Windows platform. For the moment, the linguistic annotations are limited to part-of-speech tags, lemmata, chunk information, syllabification and word frequency information, but can be extended, e.g. by n-gram frequencies to capture collocations. By aggregating the logged process data from the character level (keystroke) to the word level, general Figure 5. Deleted fragments enriched with linguistic statistics (e.g. total number of deleted or inserted words, pause length before nouns preceded by an adjective or not) can be generated easily from the Step 4 - combining process data with linguistic output of Inputlog as well.
In a final step we combine the process data with the Technical flow of Inputlog & linguistic tools linguistic information. Based on the time information At this point Inputlog is a standalone program which provided by Inputlog, researchers can calculate needs to be installed on the same local machine that is various measures, eg. length of a pause within, before used to produce the texts. This makes sense as long as and after lemmata, part-of-speech tags, and at chunk the heaviest part of the work is the logging of a writing process. However, extending the scope from a character based analysis device to a system that As an example Table 1 shows the mean pausing time supplements fine grained production and process before and after the adjectives and nouns in the tweet. information to various NLP tools is a compelling Of course, this is a very small-scale example, but it reason to rethink the overall architecture of the shows the possibilities of exploring writing process It is not feasible to install the necessary linguistic software with its accompanying databases on every 4 device. By decoupling the capturing part from the compounds: Effects of lexical frequency and analytics a research group will have a better view on semantic transparency. Written Language the use of its hard- and software resources while also allowing to solve potential copyright issues. Inputlog Strömqvist, S., Holmqvist, K., Johansson, V., is now pragmatically Windows-based, but with the Karlsson, H., & Wengelin, A. (2006). What new architecture any tool on any OS will be capable keystroke logging can reveal about writing. to exchange data and results. It will be possible to add In K. P. H. Sullivan & E. Lindgren (Eds.), a NLP module that receives Inputlog data through a Computer Keystroke Logging and Writing: communication layer. A workflow procedure then Methods and Applications (pp. 45-71). presents the data in order to the different NLP packages and collects the final output. Because all Sullivan, K. P. H., & Lindgren, E. (2006). Computer data traffic is done with xml files, cooperation Key-Stroke Logging and Writing. Oxford: between software with different creeds becomes conceivable. Finally, the module has an Van Eynde, F., Zavrel, J., & Daelemans, W. (2000). administration utility handling the necessary user Part of Speech Tagging and Lemmatisation for the Spoken Dutch Corpus. Paper presented at the Proceedings of the second Acknowledgements
International Conference on Language Resources and Evaluation (LREC), Athens, This study is partially funded by a research grant of the Flanders Research Foundation (FWO 2009-2012). Van Waes, L., & Leijten, M. (2010). The dynamics of typing errors in text production. Paper 6 References
presented at the SIG Writing 2010, 12th International Conference of the Earli Special Baayen, R. H., R. Piepenbrock, & H. van Rijn. (1993). The CELEX lexical database on CD-ROM. Wengelin, A., Torrance, M., Holmqvist, K., Simpson, Philadelphia, PA: Linguistic Data Consortium. Baayen, R. H., Piepenbrock, R., & van Rijn, H. Johansson, R. (2009). Combined eyetracking (1993). The CELEX lexical database on CD- and keystroke-logging methods for studying Behavior Research Methods, 41(2), 337-351. Berninger, V. (2012). Past, Present, and Future Contributions of Cognitive Writing Research to Cognitive Psychology: Taylor and Francis. Grabowski, J. (2008). The internal structure of university students’ keyboard skills. Journal of Writing Research, 1(1), 27-52. Jakobsen, A. L. (2006). Translog: Research methods in translation. In K. P. H. Sullivan & E. Lindgren (Eds.), Computer Keystroke Logging and Writing: Methods and Applications (pp. 95-105). Oxford: Elsevier. Kollberg, P., & Severinson Eklundh, K. (2002). Studying writers' revising patterns with S-notation analysis. In T. Olive & C. M. Levy (Eds.), Contemporary Tools and Techniques for Studying Writing (pp. 89-104). Dordrecht: Kluwer Academic Publishers. Leijten, M., & Van Waes, L. (2006). Inputlog: New Perspectives on the Logging of On-Line Writing. In K. P. H. Sullivan & E. Lindgren (Eds.), Computer Keystroke Logging and Writing: Methods and Applications (pp. 73-94). Oxford: Elsevier. Nottbusch, G. (2010). Grammatical planning, execution, and control in written sentence production. Reading and Writing, 23(7), 777-801. Sahel, S., Nottbusch, G., Grimm, A., & Weingarten,


Microsoft word - herbalmethodspaper_jbi-resubmit_2-28[1]

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