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A block is highlighted if the term occurs anywhere within those sentences. Such visualizations are popular in text analysis interfaces when allowing a user to visualize the distribution of a search query.

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In the second phase, we would develop a computational tool for examining the prevalence of any particular s, and for comparing different sterotypes. The ability to investigate the prevalence of a stereotype was central to the second phase of our analysis. When mentioned, they are always examples of kindness, and sympathy towards the abolitionist cause.

We were able to determine that there were indeed certain events that occurred so barsh in the collection as to be rightly called stereotypical.

Tools In the first phase, we built a tool to help gather examples of the various narrative stereotypes. Figure 4: Stereotypes compared.

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Often, they would not be able to include these words in a search because it would also capture a lot of other events in the text that were not related of the stereotype. Each vertical column is a narrative.

Clicking on a word in the graph filters the result set to match only that word. The s of escape, however, are far from being representative of the collection as a whole.

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This project aimed to uncover these patterns with computer-assisted techniques. This is because our true information need cannot always be expressed as a collection of words.

The power of grammatical search does not stop harah issuing precise queries. In a novel step, we created a search engine based on this technology. To address this problem, we created grammatical search.

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In the second, we would have to create a way to get a sense of the distribution and prevalence of particular stereotypes. In Conclusion Over the course of our analysis, it became apparent that many stereotypes were beyond our computational grasp.

It can also be used for discovery. The vocabulary problem is a well-known problem in search interfaces.

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This information could ly only be learned from reading. In these narratives, escape is not necessarily guided by the north star. Even though grammatical search could express certain concepts very precisely, others were more difficult to describe. In the second phase we analyzed the gathered examples and come up with queries representing the stereotypes. Keyword search, familiar to us from web search engines, is an approximate way of searching.

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The s on the graph confirm the high prevalence revealed by the newspaper-column visualization, but the adjectives accompanying these actions paint a more complete picture: one of severe, cruel, and inhuman treatment. Compared with the stereotype about north-star-guided escapes Figure 4it also seems much more prevalent.

It refers to the great variety of words with which different people can express the same concept. Figure 1: Grammatical search for the cruel treatment stereotype. Like the north star, a more conservative restatment seems more appropriate.

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We chose this area of exploration because it satisfied two criteria: first, it was interesting to the literary scholars we are collaborating with, and not just a task created to demonstrate the computational powers of a tool. Searching with normal keyword search, however, does not work well. Second, would be a novel problem in the field of text analysis systems, and therefore interesting to the computer scientist in the collaboration — not just a simple application of ly-existing tools.

Are there narratives that do not match these patterns?

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Grammatical Search We were in need of a good way to search the narrative collection for stereotypes. In our case, it manifested itself as the problem of not knowing which words were used to describe a particular stereotype in the narratives.

Analysis 5 a Widespread usage of words related to punishment. We performed dependency parsing on the entire slave narratives collection and created a way for a scholar to specify the grammatical relationships between words.

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With this tool, scholars would be able to make a quick assessment of how widespread a certain grammatical construction was, and determine whether or not it was prevalent enough to be considered stereotypical. We would use the tool to gather examples, and attempt to translate these intstances of stereotypes into some representation in fro of words and relationships between them.

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Grammatical search allows users to be precise about the relationships between query words. Nevertheless, this convention is far less prevalent than the cruelty, escape, or separation stereotypes. Similarly, Quakers are mentioned in some, but not all narratives Figure 7. Each phase of the plan presented a new computational challenge.

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It is immediately apparent that this phrase occurs mostly towards the beginnings of narratives. The narratives are segmented into vertical blocks corresponding to 30 sentences each.

idioms - Meaning of "harsh mistress" - English Language & Usage Stack Exchange

In the first phase, we would have to develop a way to easily find instances of various stereotypes. Essentially, this amounts to arranging the narratives side by side, and highlighting occurrences of the query in a given color.

The second problem we faced was that some stereotypes were hard to characterize in terms of grammatical relationships. The first is relatively more prevalent, and the two do not occur in the same places. Leaving one side of the search box blank returns all the words that match the query in the form of a bar graph.