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The Deepwater Horizon oil spill has been an enormous environmental disaster which started on 21 April 2010, off the BP-operated Cape Bon offshore in the Gulf of Mexico. This oil rig run by BP is considered one of the biggest man-made oil spills in modern history. On the morning of the spill, the oily slick took on a black, slimy appearance and quickly grew to an alarming level. Within hours it had spread itself across approximately twenty miles of area. It is estimated that at least three thousand dead fish died, as well as millions of crabs, shrimp, birds and marine animals.
The spill has also resulted in unprecedented damage to the ecosystem. Thousands of dead sea creatures have washed up on shores and lagoons along the coast. Thousands of dead sea animals have also washed up on shores and lagoons along the coast. Many marine animals such as shrimps, hermit crabs, jellyfish and snails have also died. As a result of the spill many species of aquatic organisms have been devastated.
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It is believed that the oil rig explosion was caused by faulty calculations during maintenance. The mishap has also resulted in extensive ecological damage. The devastation and deaths may also result in severe monetary loss for the oil company and any individual who may have been impacted by the spill. As the spill has affected the ecosystems, the cost to restore and rehabilitate the affected areas will be very expensive.
The ecological impacts of the spill extend far beyond the immediate area affected by the oil rig. It has also caused widespread changes to the eco-system and the way it operates. It is believed that the environmental impact will be far greater if no drastic action is taken to contain and clean up the mess.
Environmentalists are worried about the impact of the oil and chemicals on wildlife. They argue that the acidic water and the mud released during the spill have killed a large number of fish, both marine and freshwater. They have also pointed out that dead fish swimming in the ocean surface show symptoms of severe chemical contamination. Some species have already become extinct. Experts believe that more such mass strandings of animals will occur unless adequate action is taken to protect sea life and restore the ecological balance in the Gulf of Mexico and its surrounding areas.
The oil rig has also blocked off the local fishing community. The fishermen from the area have not been able to return to their fishing grounds because of the spill. The marine biologists have also raised questions about the impact of the oil on the species of birds that live in and on the marine ecosystem. Oil spills also affect marine ecosystem research and studies. Studies on the ecology of the Gulf of Mexico have also been disrupted due to the spill.
So far, authorities have not detected any major ecological effects resulting from the Deepwater Horizon oil spill. However, scientists have warned that the pollution can be a threat to the marine ecosystem as well as people living nearby. “The spill has not only impacted the aquatic system, but it has also affected the land system as well,” said Dr. Jay Famiglie, associate professor at the Florida International University. “There are some marine creatures such as fish and turtles that are sensitive to oil.” He added, “It may take time before the effects of the oil are really felt.”
The National Environmental Protection Agency has ordered the containment of the leaked oil to make sure that the oil does not enter the soil or get into lakes or other bodies of water. The containment area is approximately five miles around. Containment is still in progress at this time.
At the same workshop where I presented our paper about animacy detection, I presented joint work with Mike Kestemont, Christof Schöch and Antal van den Bosch on a computational model of romantic relationships in French classical drama.
We frame this task as a ranking problem in which, for a given character, we try to assign the highest rank to the character with whom (s)he is most likely to be romantically involved. As data we use a publicly available corpus of French 17th and 18th century plays (http://www.theatre-classique.fr/) which is well suited for this type of analysis because of the rich markup it provides (e.g. indications of characters speaking). You should definitely check out this collection. Its detailed annotations are pretty amazing!
We focus on distributional, so-called second-order features, which capture how speakers are contextually embedded in the texts. At a mean reciprocal rate (MRR) of 0.9 and [email protected] of 0.81, our results are encouraging, suggesting that this approach might be successfully extended to other forms of social interactions in literature, such as antagonism or social power relations.
Karsdorp, Folgert & Kestemont, Mike & Schöch, Christof & Van den Bosch, Antal (2015). ‘The Love Equation: Computational Modeling of Romantic Relationships in French Classical Drama’. In Mark A. Finlayson, Ben Miller, Antonio Lieto, and Remi Ronfard (ed.). Proceedings of the Workshop on Computational Models of Narrative (CMN’15), May 26-28, 2015, Atlanta, USA, pp. 98–107. (Full text)
Two weeks ago, I attended the 6th edition of the workshop for Computational Models of Narrative in Atlanta. Together with my colleague Marten van der Meulen, I presented a paper on detection animate entities in stories. Animacy is often conceived as a categorical binary distinction, i.e. a chair is inanimate and a monkey, for example, is animate. This view has been challenged, however, by researchers from different fields. To give an example from linguistic typology, it is well-known that not all languages award animacy to the same entities in different grammatical categories. In Persian, for example, a tree is grammatically marked as animate whereas a flower is inanimate. In the paper we argue that animacy should be treated as epistemological stances rather than fixed states in the world: not ineffable qualia but behavioral capacity defines our stance towards objects.
The paper presents a linguistically uninformed computational model for animacy classification. The model makes use of word $n$-grams in combination with lower dimensional word embedding representations that are learned from a web-scale corpus. We compare the model to a number of linguistically informed models that use features such as dependency tags and show competitive results. We apply our animacy classifier to a large collection of Dutch folktales to obtain a list of all characters in the stories. We then draw a semantic map of all automatically extracted characters which provides a unique entrance point to the collection.
To our surprise, the paper won the best paper award of the workshop!
Karsdorp, Folgert & Van der Meulen, Marten & Meder, Theo & Van den Bosch (2015). ‘Animacy detection in Stories’. In Mark A. Finlayson, Ben Miller, Antonio Lieto, and Remi Ronfard (ed.). Proceedings of the Workshop on Computational Models of Narrative (CMN’15), May 26-28, 2015, Atlanta, USA, pp. 82–97. (Full text)
More than fifty years after the first edition of Thompson’s seminal Motif-Index of Folk Literature, I and my colleagues Marten van der Meulen, Theo Meder and Antal van den Bosch present an online search engine tailored to fully disclose the index digitally. This search engine, called MOMFER, greatly enhances the searchability of the Motif-Index and provides exciting new ways to explore the collection. This is enabled by the use of modern techniques from both natural language processing and information retrieval. The key feature of the search tool is the way in which it allows users to search the Motif-Index for semantic concepts, such as ‘mythical animals’, ‘mortality’, or ‘emotions’. The paper, published in the latest edition of Folklore explains the motivations for creating the search tool, explicates the production process, and shows in a number of case studies how the search tool can be used to explore the index in innovative ways.
One of the case studies presented in the paper, attempts to highlight the benefits of the semantic query expansion implemented in MOMFER. Monsters are an important part of folklore which is reflected by the high number of motifs in the Motif-Index. Problematically, however, is that these motifs are not listed in a single category but rather are spread out under different subheadings. Using MOMFER’s query expansion, researchers can search for all instances of monsters using the single query: wn:monster. Most motifs dealing with monsters in the Motif-Index are about dragons and serpents. This is followed by a long tail distribution of monster motifs discussing the nature and practice of e.g. griffins, werewolves, chimeras, unicorns, and so on and so forth.
For each motif mentioning a monster, Karsdorp and colleagues extract all geographical locations from the Motif-Index where that motif has been attested. The locations are aggregated by country. The following picture visualizes the geographical distribution. The colour gradient represents the frequency with which monsters are found in a particular country. We see a strong preference for sources containing monsters from Ireland, Iceland, India, and, most notably, China.
Folgert Karsdorp, Marten van der Meulen, Theo Meder & Antal van den Bosch (2015) MOMFER: A Search Engine of Thompson’s Motif-Index of Folk Literature, Folklore, 126:1, 37-52.
web interface search engine: http://www.momfer.ml
The Arabian Nights is a tremendously rich document containing centuries of cultural thought, ideas and history. The Nights contains stories from an enormous geographical region: from Morocco to Ethiopia to modern Iraq to India. In this context it is no surprise to find an overwhelming number of different topics and subjects in the stories, yet they form a remarkably coherent collection.
In her wonderful Stranger Magic (go read it, if you haven’t), Marina Warner (2012) notes that a number of very famous stories in the Nights (‘Aladdin and the Wonderful Lamp’ and ‘Ali Baba and the Forty Thieves’) are not originally part of the Night but are ‘orphan’ tales probably to be contributed to the French translator Galland. Evidence for this claim is found in the first Arabic version of Aladdin which can be back-traced quite directly to Galland’s writings in French. Another sign of his “bricolage” as Warner calls it, is that the story of Aladdin
“pieces and patches many elements from different tales in the book, especially from ‘the true Aladdin’ (‘Aladdin of the Beautiful Moles’) and ‘Hasan of Basra’. […] Yet the plot of ‘Aladdin’, which upholds the rise of a worthless orphan boy to princely fortune, fame and power, oddly replicates the fate of the book itself, as does the story of Morgiana the plucky slave girl in ‘Ali Baba’, for she too marries up; it is as if Galland were unconsciously confessing his own craft and luck.” (Warner 2012, pp. 58).
In this post I want to explore the Nights from a distance. More specifically, using a technique called Topic Modeling, I want to investigate this idea that the “bricolage” of Galland can be observed from the many pieces and patches, or as I will call them, topical connections, between the orphan stories and the rest of the collection.
Over the last 10 years, Topic Modeling gained a lot of attention in Machine Learning and Information Retrieval. This technique allows researchers to browse a collection, not on the basis of single words, but on the basis of topics, such as ‘love’, ‘despair’, ‘war’ or ‘magic’. Scholars from the Humanities increasingly show interest in using these techniques although they also show a healthy skepticism towards the meaningfulness of applying these methods. The topic models generally provide information that most scholars are already aware of, because the topics are often of a very general nature. Although I wholeheartedly agree with these objections, I do find that a distant view on a sufficiently large collection can provide insights about the data and sometimes even proof for certain hypotheses, that are otherwise hard to obtain.
Constructing the Topic Model
There are quite some Topic Modeling toolkits available. One of the best toolkits that is also quite easy to use is the one that is included in Mallet (MAchine Learning for LanguagE Toolkit). I used the modern English translation of the nights by Malcolm Lyons (2009) from the Penguin Classics Series. Contrary to popular belief, the 1001 nights do not contain 1001 stories. There are about 260 different stories told over 1001 nights. Interestingly, night 261 seems to be missing from the Lyons edition. I constructed a corpus from Lyons’ (2009) edition that consists of 1000 documents (one for each night) plus the orphan stories of ‘Aladdin’ and ‘Ali Baba’.
I then run the Mallet Topic Model using 300 topics. The number of topics is always somewhat of a black art, and you need to experiment with a number of settings. The general idea is that if you use only a few topics, the model will provide a very general view on the data. If you choose many topics, you will obtain many fine-grained topical differences. The risk of having too many topics is that you lose generalization. For most corpora, 200 to 400 topics seems to be a good number.
Here are some of the topics learned by the Topic Model:
- Topic 222: god, night, pray, men, prayer, grant, pious, blessing, granted, prayers;
- Topic 209: ship, sea, captain, island, board, shore, sailed, water, city, wind;
- Topic 155: fish, fisherman, sea, net, baker, water, cast, bread, give, day;
- Topic 114: men, thousand, muslims, fight, army, riders, battle, killed, swords, infidels.
These topics seem to deal with religion, marine, fishing and war. Some topics are rather general and appear throughout the Nights. The following plot visualizes the ten most common topics in the nights as an area chart. The nights are on the x-axis and the probability of a topic occurring in a particular night is on the y-axis:
- Topic 59: god, heard, asked, told, replied, don’t, hand, morning, made, night;
- Topic 143: back, left, put, find, happened, leave, afraid, heard, good, thought;
- Topic 111: night, morning, hundred, told, continued, heard, broke, king, fortunate, allowed;
- Topic 121: sight, found, left, started, time, clothes, day, fell, walked, looked;
- Topic 114: men, thousand, muslims, fight, army, riders, battle, killed, swords, infidels;
- Topic 48: man, asked, told, back, gave, replied, shop, bring, home, don’t;
- Topic 72: king, palace, ground, state, city, kissed, ordered, emirs, son, throne;
- Topic 30: gharib, ajib, sahim, mirdas, brother, hundred, friend, al-kailajan, abraham, replied;
- Topic 5: great, gave, time, men, brought, filled, honour, taking, joy, provided;
- Topic 154: tears, left, time, god, recited, life, heart, lines, back, wept.
The most general and common topic in the Nights is about communication. No surprise. Other topics, such as topic 114 deal with war. The plot nicely visualizes where in the Nights wars are fought. These general topics don’t tell us much, but do function as a sanity check that our models is capable of finding common topics. Let’s now have a look at some relatively common topics that display a higher degree of granularity:
This mountainous landscape shows some interesting peaks of topic usage in certain nights. For example, around night 357-370, the Topic 204 shows a big burst. This topic has the following top words: prince, king, horse, princess, father, city, persian, palace, sorcerer, roof. In these nights Shahrazad tells the Sultan the story of the Ebony Horse. This story tells about a Persian Sage who brings an flying ebony horse to king Sabut. In return the king promises one of his daughters, but the princess is reluctant to marry this ugly and old man. A lot of adventures follow, but the point here is that, as you can see, many of the key words of the story are present in the topic. Topics such as Topic 204 are story-specific topics. Topic 2 is another example of this which shows a burst around night 550-600. During these nights Shahrazad tells the story of Sindbad. The words in Topic 2 provide somewhat of a summary of this story: sindbad, goods, island, voyage, god, large, friends, baghdad, sailor, merchants.
Aladdin & Ali Baba
According to Warner (2012), the stories of Aladdin and Ali Baba are in a way a reflection of the Nights and share many topics from many stories. Let’s have a look at some of the topics present in these two stories:
The most dominant topic in Ali Baba is Topic 201 which is represented by the following words: ali baba, captain, qasim, oil, wife, gold, jar, city, husain, abdullah, coin. These words beautifully summarize the story. Aladdin’s most probable topic is Topic 266 which contains the following words: aladdin, princess, palace, magician, sultan, mother, aladdin’s, grand, don’t, sultan’s, majesty. Again, the words seem to capture (some of) the essence of the story.
Reflections of the Nights?
How are Aladdin and Ali Baba related in terms of their topics to the rest of the Nights? To find that out we can use the topic distributions of all nights and the alleged orphans and compute the pairwise distances between them. This results in a matrix of distances between all stories. One downside of this is that is not straightforward to inspect such a large table. Therefore, I make use of another technique called t-SNE (developed by Van der Maaten & Hinton, info). This technique allows us to visualize the distances between the stories in a two-dimensional plot. To make a long story short, here it is (Right-click and open the image in a new tab, if it’s to small.):
The plot displays a number of clusters. Far to the right we have a cluster containing the nights in which the story of ‘Hasan of Basra’ is told. The blue cluster on the top contains the nights in which Shahrazad tells the story about ‘‘Ajib and Gharib’. Exactly why these nights are so far away from the other stories is something I would like to look into another time. For now it is intriguing to see that ‘Aladdin’ and ‘Ali Baba’ are placed right next to each other. What is even more striking is that they occupy the center of the plot, suggesting that the distances between them and the stories in the Nights is relatively small and that they have many topical connections with these other stories. Although, my analysis is in no way conclusive, given the central position of the orphan stories, it does make an argument for Warner’s idea that the “bricolage” of Galland can be observed from the many pieces and patches from the rest of the Nights.