| 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) |