Documentation

What's up, Switzerland?

User Tools

Site Tools


02_browsing:02_layers

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Next revision
Previous revision
02_browsing:02_layers [2020/01/06 16:59]
simone
02_browsing:02_layers [2020/05/11 08:56] (current)
Line 1: Line 1:
 ====== 2.2 Layers of information ====== ====== 2.2 Layers of information ======
-WhatsApp messages are built up in a hierarchy: a chat contains messages that contain tokens that contain characters. A corpus of WhatsApp chats should allow for all these layers to be queried. Additionally,​ there is meta-data about the chats (e.g. number of messages) and about the messages (e.g. the timestamp when it was written) and about the informant (e.g. his/her age) and about the tokens (e.g. part of speech). This makes our corpus a rather challenging and complex ​endeavor+WhatsApp messages are built up in a hierarchy: a chat contains messages that contain tokens that contain characters. A corpus of WhatsApp chats should allow for all these layers to be queried. Additionally,​ there is meta-data about the chats (e.g. number of messages) and about the messages (e.g. the timestamp when they were written) and about the informant (e.g. his/her age) and about the tokens (e.g. part of speech). This makes our corpus a rather challenging and complex ​resource
  
 These layers can nicely be seen when browsing results from a query: These layers can nicely be seen when browsing results from a query:
 {{ :​02_browsing:​layers.png?​direct&​600 |}} {{ :​02_browsing:​layers.png?​direct&​600 |}}
 +Figure 1: Representation of layers when browsing results
  
 ===== Chats ===== ===== Chats =====
  
-In this example, you find the chat back as an ID (chat138) at the top in pink. If you want to see the whole chat, you see two options at the very bottom: chat in context (faster) or the whole chat (can be slow). When you click on the little ​<iin the top bar, you can also see meta data about the chat, such as the number of speakers, languages, total messages etc.+In this example, you find the chat back as an ID (chat138) at the top in pink in figure 1. If you want to see the whole chat, you see two options at the very bottom: ​''​chat (in context)'' ​(faster) or ''​chat (complete)'' ​(can be slow). When you click on the little ​''​i'' ​in the top bar, you can also see meta data about the chat, such as the number of speakers, languages, total messages etc.
  
 ===== Messages ===== ===== Messages =====
  
-In this pink chat, you see three selected messages in blue:+In the chat in figure 1, you see three selected messages in blue:
   * Message 165379: Anke adesso se vuoi   * Message 165379: Anke adesso se vuoi
   * Message 165380: Aeh ho solo 10 percento di batteria xo   * Message 165380: Aeh ho solo 10 percento di batteria xo
   * Message 165381: Ah ecco   * Message 165381: Ah ecco
  
-As you can see, these messages have meta data assigned to themas well, e.g. the message ID and the speaker (these pieces of information are always available) as well as information provided by the informant such as age, mothertongue etc.+As you can see, these messages have meta data assigned to them as well, e.g. the message ID and the speaker (these pieces of information are always available) as well as information provided by the informant such as age, mothertongue etc.
  
 ===== Tokens ===== ===== Tokens =====
-The individual tokens are annoted ​in green in the above example ​and they are aligned to the messageto which they belong.+The individual tokens are marked ​in green in figure 1 and they are aligned to the message to which they belong.
  
-Tokens, too, (can) have annotations that are assigned to them. In the example shown above, ​you have the following meta data that was created by our team or by our computational linguists:+Tokens, too, (can) have annotations that are assigned to them. In figure 1 you have the following meta data:
   * Gloss: a normalization,​ i.e. a "​translation"​ into standard spelling. A good example here is //xo//, which was normalized as <​però>​.   * Gloss: a normalization,​ i.e. a "​translation"​ into standard spelling. A good example here is //xo//, which was normalized as <​però>​.
-  * tt_pos: A part-of-speech annotation generated with the parser ​[[https://​www.cis.uni-muenchen.de/​~schmid/​tools/​TreeTagger/​|TreeTagger]].+  * tt_pos: A part-of-speech annotation generated with [[https://​www.cis.uni-muenchen.de/​~schmid/​tools/​TreeTagger/​|TreeTagger]].
   * tt_lem: The lemma for each token as it was created by TreeTagger.   * tt_lem: The lemma for each token as it was created by TreeTagger.
  
Line 32: Line 33:
  
 Examples: ​ Examples: ​
-  * If you want to see the whole message 165380, your query would be //msg_id="​165380"​// +  * If you want to see the whole message 165380, your query is ''​msg_id="​165380"​''​ 
-  * If you want to find verbs in the present tense, your query is //tt_pos="​VER:​pres"​//+  * If you want to find verbs in the present tense, your query is ''​tt_pos="​VER:​pres"​''​
  
 To see the query-labels for the chat as well as all the labels available in a specific sub-corpus, check the information for the [[02_browsing:​01_sub_corpora|sub-corpus]]. To see the query-labels for the chat as well as all the labels available in a specific sub-corpus, check the information for the [[02_browsing:​01_sub_corpora|sub-corpus]].
02_browsing/02_layers.txt · Last modified: 2020/05/11 08:56 (external edit)