India is a place littered with incredible ancient yet unexplained ruins. Intricate ancient carvings can be found dotting the cliff faces, seemingly laser-cut caves hewn from enormous rocks, and perhaps the most impressive of its collection, the rock temples hewn straight from bedrock, which can be found all over the country. We recently focused our attention on one of these sites in particular, perhaps the most impressive of these ancient temples. Known as Kailash, it is a structure drenched in sculpted animals and religious idols. Many others also exist, somehow carved straight out of stone hillsides. The accuracy in which these structures were carved, the refined finish achieved, has allowed these structures to evade explanation to this day. There is, in fact, another site within India, another temple that, just like Kailash, was somehow hewn from a solid hillside. However, what is particularly interesting regarding this temple is that it was mysteriously abandoned leaving the apparent different stages of its construction for all to see. Known as Vettavan Coil, it is located within Kallagumalai, a panchayat town in the South Indian state of Tamil Nadu, intriguingly, upon the structure and the carved walls which it is now framed by, is the same telltale chisel marks found at so many other sites around the world, an anomaly we have already covered in depth. However, what is particularly interesting regarding Vettavan Coil is the fact that these crude marks are also accompanied by the seemingly impossible perfect finished sculptures which mystify all who peer at them to this day. It is a visual chronological timeline cast in stone, possibly left by an as-yet-unknown people using unexplained yet amazing artistic skills. The temple seemingly displays the methods used to carve it. The artist responsible crudely chiseled the design, presumably somehow from the mind's eye, then somehow professionally worked into the refined, astonishing art which adorned so many of these ancient Indian structures. Who Built Vettavan Coil? How did they achieve such perfection, with such hard stones, at such an early time in history? Is it like academia would have you believe, a mere 1400 years old? Or is it a far more ancient structure, built using as-yet unknown stone-working techniques used by an unknown group of artists? As research mounts surrounding such sites, the answers will inevitably be discovered. He recently covered the astonishing and largely unexplained ancient temple, known as Kailash, which quietly sits within India. A temple cut out from a solid rock with such precision, such vision and accuracy, it is a feat we would struggle to recreate even to this day, clearly demonstrating an ancient high technology that has undoubtedly been lost over the millennia. Could this temple actually be evidence left by a far older group of people? A remnant left by a far more advanced civilisation than that which academia will allow us to publicly discuss within many modern fields of study. Within the Bharabha and Nagarjuni hills of the Jihannabad district of India sits another series of rock cut features, six crudely cut caves carved into large stones which litter the surrounding hillsites. They could be seen as crude and possibly more modern attempts to recreate what can be found on the top of the hill. Known as the Lomas Rishi Cave cut into an enormous rock, it is the only one out of the many within the area which demonstrates a level of refinement which literally boggles the mind. The only cave in the area that has a delicately cut entrance, but also an interior which has seemingly been protected from the elements, perfectly preserved in its original state. Having a state of rock cutting which has left the rock polished to a mirror-smooth finish, evidence reinforcing the postulation that this cave and additionally Kailash temple are remnants left by a far older and once far more advanced culture than officially accepted. The hut-style facade at the entrance to the cave is officially accepted as the earliest example of the oghi-shaped chacha arch or chandra-shala that was to be an important feature of Indian rock-cut architecture and sculptural decoration for centuries during its post-cataclysmic development. The example here is largely accepted as the specific influence for later examples of which there are many at later Buddhist sites such as Zajanta Caves and Kala Caves in Maharashtra. How or indeed who cut the Lomas Rishi Cave? How did they achieve such an amazing finish to the stonework? These same people responsible for the construction of the Kailash Temple, also another structure exquisitely cut out of a giant solid stone. Although modern academically accepted views state that they were created during the reign of the Mayan emperor Ashoki, a Buddhist ruler from the 3rd century BC who ruled over almost the entire country of India, caves known as Sat Gava were carved into the hills for the use of the monks. Lomas Rishi Cave being said to have been one of them. Yet due to its exquisite quality, it's hard to see just how they can claim this. While perusing the many perplexing sites we are yet to cover on our channel, we stumbled across something which could quite possibly be a massive clue, evidence left as to the method of construction of many ancient sites found all over Earth. Our channel has, for a long time, put forward the hypothesis that a highly advanced worldwide civilization once flourished here on our planet. We believe that many of the ancient sites which display unexplained architecture were left by this lost people placed far within our distant past. And once one begins to investigate these ruins with this possibility in mind, you start to notice some compelling things regarding these amazing sites. For example, the metal clamps we have previously covered, often created using impressive mixes of alloys and somehow poured molten, could now be seen as earlier architectural examples less than the mortarless, mysteriously notched stonework, also found in similar areas all over the world, with the more precise and thus more impressive stonework, seen as a later, more sophisticated method of construction. What's more, although virtually all ancient sites have been dated to the most convenient suspects within known taut history, there also exists the numerous caves and temples hewn from the solid bedrocks carved with such accuracy and vision, they elude recreation even by our modern-day technology, and while looking at an amazing rock-cut cave within the site of Mamala Puram, India, a site we are now convinced was left by this same civilization, a curious piece of evidence seemingly presented itself. Upon the roughly finished roof of this ancient cave is evidence left by the same technology used to not only cut the astonishingly huge Long Yu caves, but also the abandoned Lang Shan quarry, both in China. This discovery, we believe, is only just the beginning of a realization that these telltale signatures are present at many other unexplained sites around the world. We have long stipulated that many of the ancient ruins claimed by our more modern-day ancestors are most likely not their actual creations. If the structure does date to this more recent age, they are usually found to be sitting upon the telltale remnants of a highly precise ancient foundation originally left by this elusive group. Who were these amazing people? When do they flourish here on Earth? What happened to them? Why did they never record how they created such wonders? Although it is easy for skeptics to argue that the caves and architecture were merely created through excruciating hard labor, any practical demonstration of this has eluded us for many centuries. Furthermore, many of the extensive cave excavations found all over the world, presumably dating back to this bygone age, are all absent any waste, as if the machine task with creating these underground labyrinths turned stone to dust. And although the technology and or possible machinery tasked with the job has evaded modern archeology to this point, it is clearly another piece of evidence which takes us one step closer to unraveling the true history of our planet. India is undoubtedly the jewel in the crown of ancient sculpture. And although Rome is home to the Renaissance, an attraction which lures enormous numbers of people there every year, India is unquestionably home to sculpture, which would put even the most efficient of the Italian masters to shame. However, conveniently, academia and thus most of the modern world overlook these astonishing feats of ancient art in favor of less controversial artistic wonders. One of our tried and tested methods of establishing whether an ancient artifact or indeed an ancient ruin attributed to a less capable, more modern imposter is actually evidence of forgotten lost knowledge is finding the puzzling accomplishments often hidden within the architecture or construction. One of the many examples of these is polygonal masonry. And although the modern man does indeed practice this lost art, a good example of this being found within the Cotswolds in the UK, known as Cotswold Drystone Walling, once built and still used to mark out very ancient land boundaries and amazingly, longer than the Great Wall of China. These very old walls created without the use of mortar are compelling examples of a fragmented technique either borrowed or possibly intriguingly left over memories of a now forgotten technology. And although these more modern attempts range in age, stretching far into thousands of years, the lesser capability of the builders is clear for all to see. Our point being that when these ancient walls stretching far before the Romans are compared to Mesoamerica, Peruvian, and indeed ancient Indian ruins, the exquisite polygonal architecture, the precise carving and stone building present are clear, strong, controversial evidences of a forgotten civilization. How did these ancient builders acquire such a sophisticated knowledge and awareness of stone shapes and the subsequent placement of each stone perfectly placed against one another, forming impenetrable barriers which have stood the tests of the ages? We feel that regardless of what academia claims as the truth, pertaining to the origin and creators of these ancient wonders, the skills required to create them are thankfully beginning to become apparent to the majority rather than the few. This ancient, forgotten people clearly attained a level of stoneworking and construction knowledge we are yet to acquire. Clearly, a far more advanced and capable people than we are today, let alone the modern historical imposters academia claims as the culprits. We feel, regardless of others' claims, the evidence to suggest an intercontinental, highly advanced technologically superior civilization once flourished here on our planet is highly compelling. We've often put forward the premise that within our very distant past, a cataclysm occurred, or more precisely, a great flood. Many surviving ancient sites around the world bear the scars of this event. The Great Pyramids of Giza, for example, still held chambers flooded with ancient seawater deep within their bowels until very recently. Much of this evidence slowly removed over the past few decades. And our next ancient structure is no different. Known as Adam's Bridge, it is an astonishing feature which stretches 30 kilometers, connecting southern India with Sri Lanka, believed to date from a pre-Diluvian age, some 1.7 million years ago.  However, with a dating of over one and a half million years, it is clearly a site that academia will continue to reject as actually being man-made, regardless of the mounting data in favor of such a reality. It seems that regardless of the overwhelming evidence that the mountain of unexplained accomplishments by our, quote, primitive ancestors, modern scholars of many subjects, continue an existence in complete denial of these truths. Dr. Bajranarayanan, former director of the Geological Survey within India, performed an in-depth analysis of the suspected bridge's makeup. His research concluded that the Adam's Bridge was indeed an artificial man-made structure, one which stretches far back into the unknown history of our planet. Below the surface, they found organized layers of sandstone blocks, coral boulders, and other cement-like substances. Several divers investigated the length of the bridge and concluded that its entire layout was indeed of an organized and thus artificial nature. The survey also revealed extremely ancient evidence of intense quarrying was also left within the surrounding area, these materials matching those placed carefully within the causeway itself. Interestingly, ancient Hindu legend from the area agrees that this enormous feature is indeed a now-submerged, gigantic earthwork, stating that it was built for the god Rama in order to help him cross the land to the large island to rescue his beloved from a demon. Is Adam's Bridge really a huge pre-diluvian sunken artifact? A 30-kilometer man-made bridge which once connected the two countries? It's an amazing proposition, and the more we learn about the amazing history which was lost here on our planet, the more it seems like a real possibility. Thanks for watching, guys, and until next time, take care. From a few space missions, of course, all occurred here upon our planet. The entirety of humanity, and as so far discovered or publicly disclosed, home to the entire fabric of advanced multicellular life within the entire infinity of our universe. All resting upon this tiny water-covered sphere, spinning its way through what would appear to be an endless space, a vast darkness, an unending void, yet one that is far from empty. For example, just within the observable universe, every nanometer of the sky, drenched in fusion producing balls of fire, stars, and other bodies, many of which are nearly identical to our sun. Yet our living planet has a double-edged sword, violent volcanic eruption, whose core is still molten, a geological consequence of a relatively young planet. All of life's complex ecosystems, many of which we depend upon each day, live on a knife edge, the sheer size of space, the vast emptiness could be seen as a safety cushion. For in such an empty place, our tiny planetary body fills hardly a fraction of even our own galaxy. However, it's filled with moving bodies, comets set hurtling through space by past ancient events, could one day hit our Earth, and have indeed done so in the past. And as mentioned in our previous video, we believe man may have experienced at least one of these events before, yet by luck or preparation, we survived. This assertion, due to our in-depth research into the evidence of the many lost civilizations. In an instant, all this natural beauty, all this seemingly fanatical trial and evolutionary error gone. All these marvelous creations, innovations, varieties, and even entire genuses gone in an instant, wiped from all but fossilized memory. Resetting our flourishing planet to a near ball of dust and ash, something we claim the academically denied ancient civilizations were also aware of. Here on our channel, we posit that the event the dinosaurs experienced a catastrophic natural event, one which nearly wiped them all from the face of the Earth, leaving only a handful of small-scale creatures that man have experienced such events, and that the survivors of such not only went on to flourish, but went to great effort to not only prove they were once here, but were incredibly capable. Thus, as such, we believe ancients from all over the world who are in communication with each other please peruse our work on metal clamps, polygonal masonry, on the Bosda caves, and our many other videos, tool signatures, and so on, for support of this claim of a once-world-going superpower. They deliberately chose incredibly hard stone, often sourced from many miles away, incredibly hard to work. These granite, basalt, sandstone, and other stone megaliths, additionally often of gigantic size, would survive the test of time, resisting a tremendous amount before fading into the chasm of history. We claim this to have been done to say we were here. Long Yu caves and Yangshan quarry China, Petra Jordan, Aswan quarry Egypt, Bosda cave, and many other sites display the same curious stone cut as our anomaly in question in tonight's video. Located within India, a country drenched in antiquity, much mysterious, baffling, and just as awe-inspiring as any other sites found anywhere else on earth, one such feature the Butterwell. It's located within a group of monuments at Mahabali Puram. Not only does it appear as a giant drill hole into solid rock, but the tool marks, far too symmetrical to have been created with primitive tools as suggested, is but yet another anomaly which we claim displays several proofs of a hidden history and indeed technological creation. We find the Butterwell highly compelling.


Well, hello for everybody. Before I start, I want you to know that this is not a technical talk. I'm a computational linguist, so I prepare the data. I pre-process the data for modeling later. So this is a practical approach on how to prepare and how to pre-process data in order to be able to use it when modeling. So just for you to know, I'm a computational linguist, as she said. You need first, for this recipe, you need to gather the corpus, then you pre-process it, then you do the text modeling, and then I'm going to tell you about the pros and cons of supervised learning. So I'm going to talk about pre-processing in social media, preprocessing text. So we need to know first what social media is. And basically, there are four things that you need to take into account. First, it needs to be web-based app. Also, it needs to be user-generated content. Users must be able to create profiles and to be able to connect with other users. And with this, we have the development of social networks, which are basically social media. So what is social media? With the definition we had before, we can think of Twitter, Facebook, Instagram, Pinterest maybe, LinkedIn, but also Amazon. Because in Amazon, you have a lot of content reviews. Booking, the same, TripAdvisor, and also Wikipedia, if we think as a content site where you can share ideas. So the type of content we find in social media is text, as in Twitter, for instance, or any other web-based application. Also, Instagram with images, videos in YouTube, for instance, or Vimeo. Then the step for text analysis, as I said before, are those there. And we start with gathering the corpus. To gather the corpus, you can take corpora from online-free sources or from web scrapping. Those are the two main basic places where you can find data. So online-free corpora, you have this corpora there. And for instance, you have the first one. So you can type import NLTK. I know because it's a video, but I'm not writing any code. You write NLTK.download, and then you get these user-graphic interface where you can download all the data.  I'm not coding. But if you have a look at this, you can download a lot of packages which are really useful when starting, especially doing NLP-based applications, because they're really useful. Because they have labels, they have a lot of resources that are really useful for starters. Also, the big young university, they have a really good corpora in English, also in Spanish and Portuguese. But any other languages are difficult to find. The British National Corpus, you can access the corpora or you can download it. And there's this guy called Martin Weisser. And he also has a really good corpora of online English. And he's got really good resources there, too. And then you can do the web scrapping of social media and information resources. Social media, you know that. It's Facebook, Twitter, whatever. And then the information resources. In my case, for instance, I did a script that was able to retrieve information from the Spanish academia web page. And it was useful because I needed to check whether a list of words do really exist or not in the Spanish academia. And I could do it automatically. So it was really helpful. So the kind of texts we find in social media are the ones that are tricky ones. For some reasons, I'll tell you later. But it determines the way we're going to analyze the data. So we have posts. We have tweets. We have those tags and comments in the post. I mean, you see there's a lot of comments. And in the tweets, you can also have the hashtags. All of these information are really valuable information for text analysis, because they will, all those tags and comments and whatever are going to be really helpful when organizing and classifying text and all these tasks we're going to perform. So now we have the corpus, and we need to go to preprocessing. When preprocessing, you can do a lot of tasks. But I'm going to explain those three because they are the main important ones and also the most useful ones. Tokenization is separating a text into smaller units. So you can separate into sentences, or words, or whatever unit you need. And you might think this is very easy, apparently. But you might find some examples like those, like City of Bombay. You have to decide whether you want to keep this as a whole unit, or you want to separate each word as a separate unit. So this is a lot of work you need to think about before you do the processing. Also, you will have problems such as ex-Malaysian Prime Minister. You need to decide whether you want to keep those units as one unit or separate ones. So this is it. Also, want and theirs, you need to decide whether you want to keep the verb and the negative form or you want to separate them, because especially the negatives are tricky when sentiment analysis, text analysis. So all of these you need to think about beforehand so that you can have the proper data you need. Also, it depends on the language. It's not only based on the text itself, but the language. You see Japanese and Chinese used to write everything together so that you cannot use spaces as word breakers. And especially Japanese has also those four alphabets. So it's not that easy to decide which one of the tokens are you going to separate. As the word removal is pretty good also for removing words that are meaningless. In the case of pronouns or determiners, possessives, I know you can read those. But it's basically a list of words you want to remove from your text, because it will be noise in your algorithms, in your models. So it's very useful to have a list of words you don't need. And finally, lemmatization and stemming are useful because you can group lemmas better than if you have all these inflectional endings that are also noise. So for instance, lemmatization is to remove inflectional endings, but coming back to the original lemma. So in this case, you will basically need a dictionary or a set of rules that help you to go back from the inflectional ending. I mean, the word with inflectional ending, for instance, in smiling, to smile. So you need all the set of rules or the dictionary to go back to the lemma, which is smile. Stemming is easier because you just chop off word endings. So you don't need to do, you don't need the dictionary. You can only chop off the endings, have a small list of endings, and then you chop off, and it's over. So what are the problems we find in text in social media? Basically, the most important one is time sensitivity. As you know, Twitter, Facebook, and Amazon, or Amazon reviews, I mean, and all of these sites have content which is dynamic. It means that it's constantly changing. People are constantly creating new content, and it's really difficult to build a model because you cannot decide on a set of parameters and expect it to work because parameters are constantly changing. So this is the main problem we have in social media when analyzing the text. Also the short length. If you try to analyze Twitter, you'll see that you have, for instance, I don't know, Superman and Clark Kent, and you know that they are the same person. But you might have a Twitter on one name and a Twitter about the other name. So when you come to cluster those words into different groups, you'll see that they might not be related in the same cluster because there's no contextual information that is telling you they are the same person. So it's difficult. This is a problem for text analysis. And this brings you to the semantic gap, which is exactly what I just explained. Also the problem of unstructured data. There's two variants. First, the variants of content quality. You see there's people that write really good in a really polite manner, but then there's people who write the way words come out of their mind and they don't try to make sense of the sentences. So this is a problem. And also acronyms, abbreviations like you or two in this case, or the end. And the misspellings, like where is the word, but how do you distinguish where from we are? So all of these problems you'll find in text analysis a lot. And also you have abundant information. There's tons of data, and you need to cut somewhere in order to be able to process. So applications in real world. You can use all those strategies for event detection, for instance, to know what is a new which is very famous or which is popular, or to predict what kind of information is going to be fashioned in the next week or whatever. Also you can take advantage of collaborative question and answering in sites like Stack Overflow, for instance. If you scrap the web, you will be able to find a really important information and a specific one rather than if you Google your search. Also you can use Wikipedia to fill in the semantic gap I was talking about before. If you use Wikipedia, which is a trustful resource, you might be able to create somehow relations between those words that are apparently non-related. But with Wikipedia, you find relations of the two. It's also useful for sentiment analysis. I will put an example later. And also to identify influencers and see if they are a lot of mentions of that person or also for quality prediction. Like in Amazon, you might want to know if a review is faithful or not. And it's very useful, these kind of tasks you can perform with NLP to decide whether to trust that user or not. So now we're going to go through text modeling. I'm going to go through it very quick because I'm not an expert. But with this preprocessing we did, we should be able to come up with a proper way I mean proper data set to be able to perform this. So if you go to Amazon and you find those reviews, you might want to separate, well, they already do. But if you have your own page, you might want to separate between positive and negative comments. So to find positive and negative comments, you need a vocabulary of positive words, negative words, and neutral words. So if we go back to these, you first need to define the task. Maybe you want to group the words in clusters and you need to decide which kind of clusters you want. So once we have this task defined, we need to decide the strategy we're going to use for sentiment recognition. There's mainly two ways, the supervised and unsupervised. But unsupervised learning, in this case, it's really, really difficult. So the supervised learning, you will need a labeled corpora, which is time consuming, pretty fine categories. And you need to go all through the data before you decide on the categories. And you can use sentiment lexicons. For unsupervised learning, you will use an unlabeled corpora, which is easy. You can use k-means for a category discovery, which is also useful. But you'll have the problem, I said before, with the time. Because it's constantly changing. So you never know if your model will be properly good. As I said before, we need a list of vocabulary for positive, negative, and neutral. And also, you need a list of comments you want to analyze. The result, this is a very basic algorithm for, this is naive base. But I mean, you can use anyone you want. The input will be mostly always the same. So the result you'll get is generally a positive score and a negative score. And then you can classify the text depending on the result you get. This is a very basic task. In my opinion, the best way is using sentiment lexicons. Because you have a word list of positive and a word list of negative words. And it's a binary fashion. So it's an easy text classification task. The problem is, well, sorry, these are two really good ones. Also, you have WordNet for NLP. And you can download WordNet in Python. And it's very useful. I'll add the link after if you want. But those are really simple ones. And they come with this binary fashion already classified, so it's pretty useful. Another approach, which I thought it was really interesting, although it's not that new, but it's a really interesting approach, are polarity lexicons. What those guys did is they had a small list of positive adjectives, and they thought that every adjective connected with that adjective in the list, by and, which is coordination, will be necessarily synonym. So they decided to scrap the web and find, well, more manually, but they found a lot of pairs of words connected by and. And so all the words were added automatically to their already built list of positive words. They did the same with negative words with the word but, or however, so they could automatically have a bigger list of negative words. So this is a really good semi-supervised approach for this task, because you get to have the best of both. Like the unsupervised is less time consuming, and it's easier. This is an example from Dandrovsky's Stand for NLP course. This is a really good book also. They have a book that they are editing right now. The third edition, I guess, is going to be ready in a few months. And this is a really good course also for a lot of NLP tasks. And this is a really good approach so far I've seen so far. So the results are basically depending on the task you want to perform. As I said before, having tasks in NLP depends a lot on the text you have. I mean, if you are analyzing Twitter, you might need some strategies really different from the strategies you're going to use if you are analyzing, I don't know, a text in a novel. So this is a very important thing you have to bear in mind, because if your task is different, you're going to need a completely different strategy, only also a very different algorithm. So K-Means is a good one for clustering. If you have a bag of words and you need to group them in clusters, but if you want to, I don't know, for instance, decide what in a spell corrector, if you want to know if a grammar is well written or not. I mean, in a sentence, if you want to check the grammar, you might need a completely different approach. And you might want to use n-grams or another strategy. So using the lexicon for sentiment recognition, for me, is the best approach, as I said before. And we have prompts, like the topic discovery is a challenge. And if you already have a small set of words, it's really helpful, although you can increase the amount of words automatically somehow. And also, the performance is better, almost always. I mean, all the cases I've seen, always the performance is better. On the contrary, dynamic language is difficult. So it's really time-consuming, building the lexicans and labeling the corpora. So it depends on how, if you need to have a task finished in a small amount of time, or you have more time, you can use one strategy or another one. So this is the bibliography. I'm going to upload the slides in case you want to check. Mining text data is a really, really good book. They have a lot of algorithms explained for each kind of task, so it's very useful. And just to finish, I'm from Mallorca. I'm a co-organizer of the PyData. And I'm a computational linguist. I couldn't came up in the whole web with the proper definition of what a computational linguist is. So I found this slide, which I think is awesome. And I decided to copy it here. You also have the information there. It's from a presentation. And well, thank you very much for watching. Thank you very much. We have a few minutes for questions. How do you determine your stop words? Is there a common set of words that tend to work? Or is it based on your application? Well, basically, anything without lexical information would do. Like, for instance, if you have the word house, you know that it might be necessary to have the information of house. Because you can find synonyms. I don't know. You can find words that are related, such as the roof or whatever. But if you have, for instance, the determiner, it has no semantic meaning, which means that you cannot find relative words, I mean, related words, sorry. So basically, this is what you take into account when listing stop words. Also, it depends on your task. You might want to, I don't know, decide that house, in your case, makes no sense in your backup words. So you won't use it. So there are lists of stop words which are already built. And you can, in LTK, for instance, the package has some, and I know many packages have lists. But basically, this is the main principle of when deciding whether a word is a stop word or not. No, you tokenize first.  Thanks for the talk. I was wondering how you apply K-means to a backup words. Like, there must be a step in between, right? Well, as I said before, I don't do a lot of modeling myself, but I usually prepare the data. So I know the problem in that case with K-means, the clusters that resulted from the first task made no sense at all. Because as I said in the example, you might have two words that are related because you know it. But when it comes to clustering, somehow the algorithm doesn't find similarities. So my job was to decide how to improve the data so that the algorithm was better. I don't know exactly how they did implement the algorithm itself. OK. So I'm not so much talking about K-means or anything. It's more like how you represent the data to an algorithm like K-means. Because usually you have a set of vectors, and then you compare the vectors, if this makes sense to you. And it's difficult. Like, you need to encode the word into something that is meaningful for a machine, right? Yeah, I understand. But I don't code the algorithm. So I don't know if they did a step in the middle. I mean, I have a set of data, a list of words in that case. And I had the results, and I saw that the clusters made no sense. And I had to come up with a solution on how to improve those clusters. So from a linguistic point of view, I saw that words that should be related were not related at all. So I started reading a couple of articles, and I found out that the problem with K-means, for instance, is that you need somehow information that relates the words. And this was missing. So this is why I was saying that if you use another resource like Wikipedia, you can somehow find relations between the words. But I'm not coding the K-means algorithm in this case. OK, thank you. Another question? Hi, thanks for the talk. So these days on social media, especially if I'm trying to measure sentiment, what I see is that a lot of people respond or review with emojis, or they would use sarcasm, or they would just react with like a gif of something that they're feeling. So when you're preparing data to measure sentiment, for example, do you take these things into account somehow? Yeah, of course. It's really difficult.  So when it comes to sarcasm and you find that some words are really not helping and are noise, you basically remove them. Also, you can try to do another analysis, which is deeper linguistically, I mean. And you try to label everything. And with this, you improve a lot. But it takes a lot of time. It's time consuming. And also, you need a lot of people working in that. You need a lot of people labeling the data and helping with the task. Like you have lists of stop words. There's no such resource out there where you can say, OK, this emoji reflects this emotion. No. It's most of a negative. No. OK. Thank you. Well, not that I know. But so far, I now found a list. So you mentioned one of the difficulty is to decide if you split the combination of words. So in your practical experience, what kind of metrics you use to determine if you need to split or not? You do it manually. Or you use rules. Like what kind of rules? Well, you can use. I mean, of course, you can use algorithms and try to do it automatically. And I mean, you can get good results. But if you want to be really, really specific and make sure that all the words are the way you want them to be, you usually can build a dictionary or you can use. So my question is, how do you determine if you want to split or not? This is depending on what you want to do. I mean, the task. If you want to, I don't know, if you need the names of people, you might want to focus on that task specifically. And then you perform that task better.  But I mean, I don't like doing it this way. There's many ways you can do it better. I mean, I'm a linguist and my job is to solve the problems that the algorithms cannot solve. So my job is not so good as, I mean, it's not that cool as writing code and everything works perfectly. I need to find a box and resolve them, which is the problem with the algorithms, which are obviously much fun. But I work with lists or with rules like a regular expressions or whatever I can. Thank you. Basically. We are run out of time for questions already, so let's thank Olaia once again for the talk. Thank you.


I'm Jared Beering, and my name is Spencer Steiner. We are tremendously excited about this X Prize. Our math centers around a novel innovation in chemical and material engineering that we're calling nanoman.


Elizabeth and I would start by saying thank you both to Elizabeth and to John Cherry for organising this conference and inviting us to speak. As you said, it's a joint talk to myself and Rob Webly. I'll be doing the talking at this point, but it is here if there are questions later. And we hope really our talk will be an introduction to the work of the Porta Plan Tickety scheme and that hopefully will provide kind of a general background for some of the talks from Helen Geek later today, but also to talk by Malcolm Jones next week, for example. So I'll just get our talk up. So I should say also Rob Webly and I no longer work for the Tickety scheme, both of us did for many years, probably over 20 years between us. But we're both currently on projects, but we're still quite involved in doing things like training with the scheme and involved in working that material. So the Porta Plan Tickety scheme, as Elizabeth mentioned, is a national scheme across England and Wales to record objects found by members of the public. It's voluntary for you to record things apart from the case of treasure cases. And the Porta Plan Tickety scheme records things from everyone, but obviously a lot of the metal finds come specifically through mesh detecting. And I would say right at the beginning that the kind of information we are able to pull out, the pictures we can share with you, the data is all built on the work of all those hardworking flows of another country and volunteers who work with them and also of those who offer for their finds for recording. And I think, you know, that that thanks has to be there right at the beginning to kind of provide about data, which we can then use. So as you said, finds recorded through the Porta Plan Tickety scheme are stratified finds which lack a kind of vertical context within a site, but retain a horizontal context within the landscape. The pre-circulated summary we sent out gave some details of numbers. So if you want to know more about the scheme, do have a look there. But to summarise, the Bias has recorded over 7,700 seal matrices, as well as quite large quantities of cloth and product seals, bullet and seal rings, which will also be of interest to syglobrophers. One feature of the Porta Plan Tickety scheme is that every find, as well as having a detailed record, for example, here, one for April 1, is that they have a precise find spot accessible to registered users. So this concentrised national publicly available data is the PSA's real mainstream and allows both a kind of detailed approach to individual matrices through the good photographs, or small groups of matrices, or a kind of big data approach. And that's the approach we're really taking today. As the map on the left shows, which is a density of all medieval finds recorded through the Porta Plan Tickety scheme, there is some variation across the country. But as you can see from the map on the right, we've got map, the medieval and post medieval seal matrices. There are examples from across the country. There's no area without them. But this kind of variation, there is some variation in the density. And think about areas where work could happen. There is quite a lot of opportunity there for local and regional research and regional comparisons of both the adoption of kind of seals and general seal practices, but also in particular features for seals. You've done some work on the kind of adoption of the marrying cult things like the existence of lilies and so on and looking at that kind of regional variation would be something that could be done through the Porta Tickety scheme reasonably easily and conveniently from home, which is always useful during a pandemic. So like I said, there's a lot of opportunity there to look at variations between the matrices across the country and that regional variation. And one reason we really want to give this talk is to hopefully kind of inspire more people to use the data, to use this and to use it in their research and encourage their students to use it as well and enhance our knowledge in that way. So before going into kind of more statistical analysis, the Porta Tickety scheme, seal matrix data in comparison to other resources, we want to talk about some specific objects recorded by the PS and how they give an insight of the life cycle of seals. So starting at the end of that life cycle with the production, with the creation. And the evidence of this includes moulds such as the one here on the left from Lincolnshire, a multi-section copper and iron mould for a pedestal type seal matrix. But we can also look at the matrices themselves where there are clear design links between matrices. On the right you can see two examples from Kent. They've got an unusual back design. Interestingly, little in detail, they're not necessarily from the same mould, but they're obviously a very unusual time, which is presumably the mark of that particular producer of the seal matrix, Blank. And the, so I'm just going to make it so you can just see one picture of me, hopefully carrying less bit. So and that kind of looking at back designs, using that to link different makers together, is something where there's really quite a lot of potential I think for further work. I know Helen also talks a little bit, her geek talks a little bit about backmarks in her session, but I'd say this scenario that could be looked at. We have tried to find links for many of the objects we're showing on our pre-circulated summary. So I think they were originally as direct links. I'm not sure those links are always working, but if there's something you see, an object you see here that you want the link for and need help finding, then do get in touch with us. So you've got your basic kind of matrix and the next stage of production is the kind of engraving of the matrix with design and legend. And one of the strengths of the PS data is that we now have a really good range of partial engraved but uncompleted matrices. And obviously this is the sort of thing that we don't really see perhaps often in archive, seeing impressions, or in museum collections. And knowing now that there's this kind of large number of unengraved and incomplete matrices suggests perhaps there was often quite a reasonable gap between the casting of the matrix and engraving them, particularly the lead alloyed personal matrices of the 13th century. And that the partial engraved seals and the discarded again suggests that these have been done not where the people are originally casting matrices, where they have access to furnaces for recycling material, but in a later stage and separately. It's been suggested by a few authors that based on seal impressions that come from seals made by the same hand, that simple cheaper seals may produce particularly for the signing of one document, I think an example particularly from Hideout in Winchester here. And given that kind of thought could we go further built on the data we've got here and suggest that legends and designs on some of these simpler easily cut lead alloy seals might even be the work of more generalized class rather than specialist seal makers. Perhaps lead alloy blanks were kept to hand and cut as needed. This obviously is kind of a speculation, but builds on the suggestion perhaps of John McKeown in the study of seal makers in London, that there might be separate production routes for lead alloy matrices to the copper alloy ones. Obviously can't forget we've got a soft metal and writing description backwards are not easy and do require some skill, but perhaps the skills more of a clerk or some attached to a scriptorium rather than loads of metal worker. And kind of another little strand of evidence perhaps in support of the suggestion, so I'll try and bring it up so you can see me as well, is the kind of other examples of seals made on reused other objects. So this is this is the favourite seal I'm going to show you of our entire session here or my favourite one is a reused papal bullet here. You can see on the left you can see the remains of the raised design of the bullet, which has then been cut down the backs or if I'm not actually with the the post name has been smoothed and it's been cut to make a seal matrix, probably named Thomas, although obviously it's now complete. And there are other examples of reused bullet to a known, so these people who are not casting matrices, but they're making seals matrices. And there's also one example here of a reused harness pendant from Norfolk, which is that big Rob's favourite seal of the ones with the seal matrix the ones we're showing you. And this papal bullet kind of leads us neatly on to the end life of seals because you can see that one reason we have passed it is it's been cut deliberately to destroy it. And as we're going straight from here production, we're skipping over the use life, which obviously the in-pip project and perhaps other research says more of, and we're going to go straight on to the end. Because again this is what somewhere where the PS data set is bringing new evidence and new kind of areas that can be researched. And that's because they're quite a big range again of cuts and deliberately to face seals on the database. Again perhaps more usually the personal seals in the lead alloy, perhaps because the research was destroyed by hand, perhaps because recycling lead priority cost-wise. The top left example here shows damage with a bend which may be placed deep position but as well as a deliberate cut. And I would say this is an area where given this new data set more work could be done. Looking at the method and the style of distraction we can see clear kind of knife cut marks on the one bottom right. But also thinking kind of perhaps more abstractly about how vis-to-facing is needed to make a seal unusable. While simply removing the centre, as has been done to that copper alloy example, enough ordered part of the legend need to be removed. In wider sense that allows us to understand how people at time, what people at time thought was necessary for a seal. What do you need to destroy to make this object no longer a seal matrix? What is the critical bit? Do you need to destroy the image? Do you need to destroy the legend? Do you need to destroy both? How much of a seal would still operate as a seal? If that makes sense. So having got to the end and destroyed the seal matrix, got sent to Lifecycle. What I want to do now is to go on to look at the PS data set as a whole and compare it to kind of other data sets. And I'm afraid that does mean we get to where we move away from perhaps some nice images of individual seals onto seal matrices onto lots of charts and graphs. Let's get this slide going up. So first of all, data. And as unstratified finds, dating a PS material is of course stylistic. And what we're comparing this slide is an heuristic part of a PS material with the now online collections of the British Museum and with the National Archive Collection 13 of seal impressions. And I have to tell you, we chose that collection just because it was readily accessible and the data was very easy to search. I haven't put the two graphs together. The reason is because PS1 is plotted in an heuristic matter, which we're graphing dates of varying precision together. But it means it's kind of not directly comparable perhaps particularly to National Archive data. So what we saw in all of them however is this sudden peak in the 13th century. Yes, there are no numbers in the 12th century in some cases in the 11th century, but there's a sudden huge peak in the 13th century. And that's both very visible here in the PAS and the National Archives and to some extent the British Museum as well. What happens with the Archive Service is that that peak drops off very quickly in the 14th century, whereas on the British Museum it's actually higher in the 14th century and the PAS retains a very strong peak. And this can in part be a factor of dating precision, obviously more close often with with kind of archive material where it's attached to a particular document. But it may also relate to a change in seal use, which I'll discuss later. So the National Archive data also had a peak kind of secondary peak in the 16th century. That's the blue peak. Here you can see. And a chemistry look suggests that relates to great seals, which are absent from the PAS data and not as common in the British Museum collection. And I think one thing that again brings about a theme more of a tenth round of talk is the type of seals that get preserved through history in order to make it into archives can relate to the kind of documents they're attached to, which may mean great seals and more preferentially surviving in archives than some other types. One thing that's quite interesting to me given, I know, as a talk is going to talk at a call, so I was going to address this next week, is this 15th to 16th century weakness and whether that's a factor of genuine changes in behavior or whether it's a factor of our own dating blindness. So I'm really interested to what she has to say. Now, both the PAS and the British Museum data, so here in orange and PAS up here, have another strong peak in the 18th century. And this is really representative tool in the archive collection we're looking at. And one reason for this is this is a change in use partly to seals as kind of dormant and decorative use. Obviously we're worn in the medieval period as well, but I think particularly of the fog seals of the 18th century, this becomes a very widespread method of adornment. And also the use of seals as seals of closure, so in personal letters, rather than authorization. And that change in use makes them perhaps harder, I'd suggest, to study through archives rather than through the matrices. And it's interesting to think we know that's what's happening perhaps in the 18th century, and that's why the difference is there, is that also what's happening in the 14th century. And that's why the difference is there then as well. So again, think about gaps. Well, research of course exists on the 16th to 18th century seals. They're also perhaps an under-researched resource in terms of design choices, materials, affiliations. And so just to share another personal favourite which I'm looking at but other research, here's an 18th century example of a rotating fob seal. It's an example praising Frederick King of Prussia, and it's a very kind of cheap, kind of small, you can see how tiny it is, personal object that's shown popular support for the Seven Years War. And that kind of interpretation of some of these fob seals, like I said, is another area wide open for further research. So moving on from dating to material, and as the discussion on kind of broad production has perhaps already started making, kind of raising, when the strength appears as a cheaper end market, and this is seen in the materials. So compared to the British Museum collection, and here we're only looking at medieval material, which of course seems quite, the British Museum is quite comparable in date, what we see is that for both copper is the most common material, and then the majority of thrust is made up of silver and lead. The British Museum also has some stone organic material made to seize, the pair has numbers prepared and both have negligible number of gold made to seize. The contrast further between the British Museum and the pair stays risk clear. The lead alloy seals, the kind of cheap end market in raw material, and most often the skill of design represents 40% of the 40% of the skill data in any 11% of the British Museum data, and I'd say there's probably a double bias behind this, that it's easy to say there's museum collection bias, and I'm sure I'm afraid there is, but also I'd say there's probably a bias of loss. If anything silver seals once found are more likely to be recorded with the PIS, as they're recorded illegally enforced through treasure rather than voluntary, so it's unlikely to be a bias of recording. The bias of loss can come from the inherent value of the material, the Nazi silver, but also that wealthy people and institutions have more to fear from the loss and misuse of their seal, both due to the kind of value of their land holdings and credit, but also, as we've seen by contemporary cases, there's much more risk of fraud by people in their employ, or by someone else finding and using their seal, and there are cases known of that. So in the final part of the talk, I want to move on from the seal matrices, physical objects from their material, from the kind of way they're being made, to think about the designs and the legends, and these figures in those following are based on our categorization of a sample of PIS medieval data. It's always, it'd be great to see more, more regional comparisons of it, so I want to take on that pattern. So, as was raised in the pre-seculated summary, and as we throw a bit in her introduction, there has been a shift in seal matrices away from a focus on institutional seals, which has been suggested account for perhaps 20% of those in archives to personal seals, for kind of other 80% of the stuff out there in archives. However, as these graphs make clear, that shift has perhaps not gone fast enough. Well, for the British Museum, and we see other museums, institutional seals make up 40% of their collection. For PIS, this is only 3%, and here I'm lumping together, obviously, governmental seals, religious institutions, and various other institutions, but you can see how small a proportion that is of the PIS data. Again, there are biases against loss of this type of seal, again, as we mentioned, but the seal seals, but I'll give you past this discrepancy as a survival bias amongst documents as well, towards those of larger institutions in the wealthy in general. It's also worth noting the very large proportion of anonymous seals in both the British Museum and the PIS data. We really, I'm very struck to find a readily comparable figure for archives. It may be some people today have those figures immediately to hand and can say whether this is true, but my impression is that the proportion of anonymous seals in archives is much lower, and here we're talking about seals which don't name an individual. And that, again, may mark the anonymous seals' greater use of seals of closure. To not say that anonymous seals aren't by archives, I know they are, but, and they are used as seals of authority, but it's about the relative popularity of different uses of different types of seals and designs. And again, studies are focusing on seal impressions in archives, and it's no wonder that such seals perhaps are under-researched relative to their perhaps true popularity out there in the medieval period. So it seemed useful because we've got that data to kind of categorize these anonymous seals, to think about the type of messages being given by these, and in future to our comparison the best survival in the archives and what that tells us about their kind of different, useful different types of documents. And this also perhaps will find a useful background to discussions of individual seals this time by Malcolm Jerry's next week. What we see is that over a third of these kind of anonymous legends focus on the role of the seal itself. When we're thinking here about seals, now we have to apologize to my Latin, which is non-existent, we think about seals such as I'm Frangere lego tege, so break, read, seal. A further third religious, and these can vary from very grand 14th century copper allied, pointed ally seals with standing seals and kind of kneeling figures, right down to the very simple hill of calvary images, such as this one with ESOS Amome. The final third is again perhaps disproportionately where a lot of attention has been focused on the kind of amortery and hunting examples and the funny joking legends. And it's something because they're really fun ones, but it's important to bear in mind that these are in a small proportion of this and again it'd be great to compare some of these statistics to archives and hopefully these figures provide a kind of useful baseline for that sort of future comparison. It would also be good to see more work in future on dating and how the use of them changes through time amongst the PS data set as well as in archive and seal oppressions. This obviously is looking at the legend and it is the case that hunting scenes, for example, can be combined with legends talking about the role of the seal, for example. So the designs will be finally turned and moving from the legend sign, this is what we can really study in the X-men resource of DigiSync and we sent a link I think in the pre-circulated summaries and just have to say thank you to the creator of that, which by categorising seals in online database allows to compare the PS data to their data and because it's that one thing talking about data consistency we can see where they've applied a particular term to a particular design. We can be sure that we're applying it to the same types of designs because we can see the visual record of their example. Although DigiSync I know is pulling in PS data at the moment because it's not categorised it doesn't turn up when you're kind of looking through their proportions. So what their categorised data is mainly archive and museum based so it's kind of comparisons to the PS data. So looking at this what we see is kind of a real clear discrepancy in that the kind of designs being found in cataloged archive museum collections are both different so within the PS devices and here we're talking about things like flood and lead or radiating devices and the most common followed by animals in contrast on DigiSync at humans and objects predominate and when we break this down in detail just going to examples there for an animal a device a standing figure and of an object nice hatchet there. So if we break this down in detail a lot of this discrepancy is actually caused by kind of a few particular motifs within these classes. So it's a very complicated graph but just say the red and blue dots represent the portions of seals of that sign on DigiSync in red or PS in blue and for most actually they're not that for most classes in each kind of column in effect represents class they're not that clear apart partly actually because most of these designs aren't that common so the difference between one and three percent just isn't that great that makes sense. But what we see is there are some real discrepancies it's much stronger the DigiSync data is much stronger in terms of full-length human figures and representations of periodic shields in the object class and that this kind of difference is not just a product source we categorize so if our DigiSync is shown by archive research so there's only two percent of PS data as our moral designs and this can be compared to the 17th cent London and 15th cent in their new cluster that analysis you can have in your research so again we're seeing a difference between the matrices that are lost and the seals that survive on documents to make it into archives the higher number of animals in pairs seems to reflect the higher number of hunting-rated creatures to become anonymous seals and again are we looking at a difference there in terms of the kind of different uses of these different seals and what then happens that while their seals may be used in legal texts are they more popular because they're more popular being used on personal letters and documentation. Final PS data has a lot more radial devices and they've been more stylized lilies and these particularly common lead are known matrices 44% of all lead animating are sample peptides in a copper alloy while radial designs should not be assumed to be a below status individual I think it's better that again we're seeing a link between a less individual design and intrinsically less valuable material and a pair of status sets of seals which people are more like to lease um I'm just coming to the end sorry so these are people who are choosing to let our seal or being provided with one perhaps people I just wonder whether people who do not need is hard-wearing a seal or to invest in much money in a fancy material or individualized design a group who perhaps don't need to use a seal as often a seal in fewer land rather lead or documents and expect their seal impression to be seen by fewer people and therefore the display potential which I know is going to discuss a lot in seals and we'll discuss very much dates next week which might encourage people to invest in seal it's a messy medium it's kind of less I was just that the documentation of these people's also less like to buy and that's the large institution so overall differences here are perhaps not surprising but they seem to confirm that we are in a pair of state-based revealing a largely different sector than the medieval seal using population seal matrices of different styles and of different individuals having different types of use lines and the different life cycles which can each different design choices but also difference in where and how they're used and ultimately whether they survive for us to study today whether it's discarded objects we're going to get here or impressions in archives so thank you very much thank you very much indeed I mean that was absolutely fascinating very exciting we have a lot of questions I don't know if we're going to get through them all now so I'm going to throw some of them at you maybe Helen could also speak to some of these as well for her paper why 500 paper bullet in the PAS from that so where are they um so there's a suggestion of whether some of them are ones where they document has been destroyed things like the information but also a suggestion that some may have been deliberately used kind of reused in new ways I'm looking at Rob here he has more comment no he's shaking his head as well I I think there has been work on this and I would just point them to the work of Tim Pestle here who's recently published about that and we can find that reference if useful if I can break in there's also um something that's been done on paper bullet in the Netherlands by um I've got a note of Bartels 2017 um and really I think we're a bit desperate aren't we Laura for somebody to come in and do a bit more work on the paper bullet I mean Tim Pestle but we need we could do with a PhD student yes right thanks very much um I'm just taking these in order next one from Ella Paul um they're all so similar gaps in 15th century evidence in Scotland um but do you have other objects being reused as civil matrices I'm any aware of but I'm the harness pendant I think I'm trying to think of other objects that could be I think the problem is is to what example I'm thinking about other types of seal tickly cloth seals and so on the impression would really hide any other data I think again it's a case of a really good search so the payers may bring more of these up and again it'd be a wonderful project for somebody but yeah below the one I can think of at least two or three examples and that's a perhaps thank you from Paul Drury why are there so many discoveries recorded from East Anglia from East Anglia um a history of excellent liaison in that area particularly in Norfolk it's also a geographical reason obviously it's a very heavily plowed agricultural landscape compared to the upland landscapes but yeah that combination of a lot of activity there in terms of the agriculture and also that long history of liaison and also a slightly more investment in terms of the number of people recording you know the number of staff recording in that area right