One of the questions that is most frequently asked is: How big is your corpus? The answer is: Beats me, its constantly changing and there are several different versions of the corpus available at any one time. But people usually aren’t satisfied with that answer, so here are the details of where the SPinTX corpus currently stands to the best of my knowledge (as researched this morning):
Total n interviews: 123
Total n transcripts: 74
Total n words: 315,673
Total n transcripts approved and tagged: 32
Total n words for approved and tagged transcripts: 134,737
Total n clips available to public taken from approved videos: 328
Total n words for clips: 102,573 (Note: many of the clips overlap, this is not filtered out in this count.)
Please let me know if there are any other stats that would be of use/interest and I will append them to this post.
One of the goals in the Corpus to Classroom project is to design a pedagogical interface for the repository of video clips that are being generated out of the more than 100 interviews that were collected in the past as part of the Spanish in Texas project. From our interviews with actual teachers and materials developers, we confirmed that teachers are potentially interested in applying the following types of filtering criteria to their searches:
- Grammar topics: e.g., search for those clips that contain a significant number of occurrences of por and para
- Functional topics: e.g., search for those clips that contain exponents of the function apologizing
- Vocabulary: e.g., clips that contain words (in a pre-defined list maybe) that relate to the topic la familia (papá, mamá, padre(s), madre, hermano/a, abuelo/a…)
- Thematic: e.g., clips talking about food, traditions, reasons for moving to the US (in our case)…
This is not a complete list, but it is a starting one that contains the most common types of criteria (emotion and phonetics are two criteria that were mentioned too).
With this in mind we are considering the use of a standard search engine (such as Apache Solr/Lucene) to allow teachers to search for the clips and use facets (filtering options) to dig down or define finer-grained queries. However, we also consider the use of typical corpus query tools (such as CWB or SketchEngine — or NoSketchEngine). With this we can cover the Information Retrieval part of our task (more appropriate for document retrieval on the basis of word- or term-based queries) and the Information Extraction part of our task (more appropriate for the queries driven by linguistic patterns).
We will further describe our advances in future posts.