Language Understanding Annotation Corpus
|Item Name:||Language Understanding Annotation Corpus|
|Author(s):||Mona Diab, Bonnie Dorr, Lori Levin, Teruko Mitamura, Rebecca Passonneau, Owen Rambow, Lance Ramshaw|
|LDC Catalog No.:||LDC2009T10|
|Release Date:||March 17, 2009|
|Data Source(s):||telephone speech, newswire, email, broadcast news, broadcast conversation, varied|
|Language(s):||English, Standard Arabic, Arabic|
|Language ID(s):||eng, arb, ara|
LDC User Agreement for Non-Members
|Licensing Instructions:||Subscription & Standard Members, and Non-Members|
|Citation:||Diab, Mona, et al. Language Understanding Annotation Corpus LDC2009T10. Web Download. Philadelphia: Linguistic Data Consortium, 2009.|
The Language Understanding Annotation Corpus, Linguistic Data Consortium (LDC) catalog number LDC2009T10 and isbn 1-58563-513-8, emerged from a series of interdisciplinary meetings on semantics and pragmatics hosted by the Human Language Technology Center of Excellence at Johns Hopkins University. The participants were researchers from Raytheon BBN Technologies, Carnegie Mellon University and Columbia University who were developing representations of text semantics, machine translation and summarization systems. The resulting corpus contains over 9000 words of English text (6949 words) and Arabic text (2183 words) annotated for committed belief, event and entity coreference, dialog acts and temporal relations. The source materials were chosen from various genres to represent "informal input," that is, text that contains colloquial forms. The documents in the corpus include excerpts from newswire stories, telephone conversation transcripts, emails, contracts and written instructions.
The problem was modeled as an extended exercise in extracting information elements from a "document" (that is, from discrete language records in written or spoken forms). The goal was to answer two broad questions:
- What are the elements of knowledge that can be derived from a document?
- Can the representation, and hence, the annotation, be laid out in terms of iterative layers, the accumulation of which would represent the sum of the knowledge?
The annotations attempted to resolve these questions in the following ways:
- Belief/Opinion/Confidence. Committed belief annotation distinguishes between statements which assert belief or opinion, those which contain speculation, and statements which convey facts or otherwise do not convey belief. The goal is to be able to determine automatically from a given text what beliefs can be ascribed to the author and with what strength the author holds those beliefs.
- Dialog Acts. Dialog act annotation seeks to determine the forward and backward links between pairs of dialog acts.
- Coreference (entities and events). Event coreferences indicate which events are related to other events at the document level. Entity relations within these related events provide further information about e.g., the main actors, targets and causes of the events.
- Temporal relations. Temporal annotations mark the temporal relationship between the different events and time anchors mentioned in a document, that is, it highlights what the text is saying about the time line of time-mentions.