Update README.md

README.md

@@ -55,3 +55,117 @@ tags:
 - movie dialog
 - cornell
 ---
+
+Cornell Movie-Dialogs Corpus
+
+Distributed together with:
+
+"Chameleons in imagined conversations: A new approach to understanding coordination of linguistic style in dialogs"
+Cristian Danescu-Niculescu-Mizil and Lillian Lee
+Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, ACL 2011.
+
+(this paper is included in this zip file)
+
+NOTE: If you have results to report on these corpora, please send email to cristian@cs.cornell.edu or llee@cs.cornell.edu so we can add you to our list of people using this data.  Thanks!
+
+
+Contents of this README:
+
+	A) Brief description
+	B) Files description
+	C) Details on the collection procedure
+	D) Contact
+
+
+A) Brief description:
+
+This corpus contains a metadata-rich collection of fictional conversations extracted from raw movie scripts:
+
+- 220,579 conversational exchanges between 10,292 pairs of movie characters
+- involves 9,035 characters from 617 movies
+- in total 304,713 utterances
+- movie metadata included:
+	- genres
+	- release year
+	- IMDB rating
+	- number of IMDB votes
+	- IMDB rating
+- character metadata included:
+	- gender (for 3,774 characters)
+	- position on movie credits (3,321 characters)
+
+
+B) Files description:
+
+In all files the field separator is " +++$+++ "
+
+- movie_titles_metadata.txt
+	- contains information about each movie title
+	- fields: 
+		- movieID, 
+		- movie title,
+		- movie year, 
+	   	- IMDB rating,
+		- no. IMDB votes,
+ 		- genres in the format ['genre1','genre2',�,'genreN']
+
+- movie_characters_metadata.txt
+	- contains information about each movie character
+	- fields:
+		- characterID
+		- character name
+		- movieID
+		- movie title
+		- gender ("?" for unlabeled cases)
+		- position in credits ("?" for unlabeled cases) 
+
+- movie_lines.txt
+	- contains the actual text of each utterance
+	- fields:
+		- lineID
+		- characterID (who uttered this phrase)
+		- movieID
+		- character name
+		- text of the utterance
+
+- movie_conversations.txt
+	- the structure of the conversations
+	- fields
+		- characterID of the first character involved in the conversation
+		- characterID of the second character involved in the conversation
+		- movieID of the movie in which the conversation occurred
+		- list of the utterances that make the conversation, in chronological 
+			order: ['lineID1','lineID2',�,'lineIDN']
+			has to be matched with movie_lines.txt to reconstruct the actual content
+
+- raw_script_urls.txt
+	- the urls from which the raw sources were retrieved
+
+C) Details on the collection procedure:
+
+We started from raw publicly available movie scripts (sources acknowledged in 
+raw_script_urls.txt).  In order to collect the metadata necessary for this study 
+and to distinguish between two script versions of the same movie, we automatically
+ matched each script with an entry in movie database provided by IMDB (The Internet
+ Movie Database; data interfaces available at http://www.imdb.com/interfaces). Some
+ amount of manual correction was also involved. When  more than one movie with the same
+ title was found in IMBD, the match was made with the most popular title 
+(the one that received most IMDB votes)  
+
+After discarding all movies that could not be matched or that had less than 5 IMDB 
+votes, we were left with 617 unique titles with metadata including genre, release 
+year, IMDB rating and no. of IMDB votes and cast distribution.  We then identified 
+the pairs of characters that interact and separated their conversations automatically 
+using simple data processing heuristics. After discarding all pairs that exchanged 
+less than 5 conversational exchanges there were 10,292 left, exchanging 220,579 
+conversational exchanges (304,713 utterances).  After automatically matching the names 
+of the 9,035 involved characters to the list of cast distribution, we used the 
+gender of each interpreting actor to infer the fictional gender of a subset of 
+3,321 movie characters (we raised the number of gendered 3,774 characters through
+ manual annotation). Similarly, we collected the end credit position of a subset 
+of 3,321 characters as a proxy for their status.
+
+
+D) Contact:
+
+Please email any questions to: cristian@cs.cornell.edu (Cristian Danescu-Niculescu-Mizil)