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Emory University Events

Computational Linguistics ; 45 4 : — Argument mining is the automatic identification and extraction of the structure of inference and reasoning expressed as arguments presented in natural language.

Understanding argumentative structure makes it possible to determine not only what positions people are adopting, but also why they hold the opinions they do, providing valuable insights in domains as diverse as financial market prediction and public relations. This survey explores the techniques that establish the foundations for argument mining, provides a review of recent advances in argument mining techniques, and discusses the challenges faced in automatically extracting a deeper understanding of reasoning expressed in language in general.

With online fora increasingly serving as the primary media for argument and debate, the automatic processing of such data is rapidly growing in importance.

Unfortunately, though data science techniques have been extraordinarily successful in many natural language processing tasks, existing approaches have struggled to identify more complex structural relationships between concepts. Justifying opinions by presenting reasons for claims is the domain of argumentation theory, which studies arguments in both text and spoken language; in specific domains and in general; with both normative and empirical methodologies; and from philosophical, linguistic, cognitive and computational perspectives.

Though an enormous field with a long and distinguished pedigree see van Eemeren et al. Argument analysis aims to address this issue by turning unstructured text into structured argument data, giving an understanding not just of the individual points being made, but of the relationships between them and how they work together to support or undermine the overall message. Although there is evidence that argument analysis aids comprehension of large volumes of data, the manual extraction of argument structure is a skilled and time-consuming process.

Although attempts have been made to increase the speed of manual argument analysis, it is clearly impossible to keep up with the rate of data being generated across even a small subset of areas and, as such, attention is increasingly turning to argument mining , 2 the automatic identification and extraction of argument components and structure.

The field of argument mining has been expanding rapidly in recent years with ACL workshops on the topic being held annually, from the first in , 3 up to the most recent in , 4 which received a record number of 41 submissions.

Previous reviews, including Palau and Moens and Peldszus and Stede a , predated this explosion in the volume of work in the area, whereas more contemporary reviews are aimed at different audiences: Budzynska and Villata at the computational argumentation community and Lippi and Torroni at a general computational science audience.

Most recently, Stede and Schneider have, in their tour de force, assembled an extensive review of performance on tasks in, and related to, argument mining.

Our goal here is to update and extend, introducing reorganization where more recent results suggest different ways of conceptualizing the field. Our intended audience are those already familiar with computational linguistics, so we spend proportionally more time on those parts of the story that may be less familiar to such an audience, and rather less on things that represent mainstays of modern research in computational linguistics.

With this goal in mind we also move on from Stede and Schneider in three ways. First, we bring the discussion up to date with the newest results based on approaches such as Integer Linear Programming, transfer learning, and new attention management methods, and cover a much larger range of data sources: For a discipline that is so increasingly data-hungry, we review annotated data sources covering over 2.

Second, we provide greater depth in discussion of foundational topics—covering both the rich heritage of philosophical research in the analysis and understanding of argumentation, as well as those areas and techniques in computational linguistics that lay the groundwork for much current argument mining work. Thirdly and finally, the simple pipeline view of argument mining, which characterizes a lot of both older research work and reviews, is increasingly being superceded by more sophisticated and interconnected techniques; here we adopt a more network view of subtasks in argument mining and focus on the interconnections and dependencies between them.

We look first, in Section 2 , at existing work in areas that form the foundation for many of the current approaches to argument mining, including sentiment analysis, citation mining, and argumentative zoning. In Section 3 we look at the task of manual argument analysis, considering the steps involved and tools available, as well as the limitations of manually analyzing large volumes of text.

Section 4 discusses the argumentation data available to those working in the argument mining field, as well as the limitations and challenges that this data presents. In Section 5 , we provide an overview of the tasks involved in argument mining before giving a comprehensive overview of each in Sections 6 , 7 , and 8.

In this section, we look at a range of different areas that constitute precursors to the task of argument mining. Although these areas are somewhat different in their goals and approach, they all offer techniques that at least form a useful starting point for determining argument structure.

We do not aim to present a comprehensive review of these techniques in this section, but, instead, to highlight their key features and how they relate to the task of argument mining.

In Section 2. Section 2. Citation mining, covered in Section 2. Finally, in Section 2. As the volume of online user-generated content has increased, so too has the availability of a wide range of text offering opinions about different subjects, including product reviews, blog posts, and discussion groups.

The information contained within this content is valuable not only to individuals, but also to companies looking to research customer opinion. This demand has resulted in a great deal of development in techniques to automatically identify opinions and emotions. The link between sentiment, opinion, and argumentative structure is described in Hogenboom et al. Based on their role in the argumentation structure, text segments are assigned different weights relating to their contribution to the overall sentiment.

Conclusions, for example, are hypothesized to be good summaries of the main message in a text and therefore key indicators of sentiment. The interesting point here, from an argument mining perspective, is that this theory could equally be reversed and sentiment be used as an indicator of the argumentative process found in a text.

Taking the example of conclusions, those segments that align with the overall sentiment of the document are more likely to be a conclusion than those that do not. Many applications of sentiment analysis are carried out at the document level to determine an overall positive or negative sentiment. In terms of relative performance, the support vector machines SVMs achieved the best results, with average 3-fold cross-validation accuracies over 0.

Sentiment analysis tools are used to determine the overall sentiment for an initial one word query, which is then extended and the change in overall sentiment recalculated. By following this procedure, it is possible to see where extending the query results in a change of overall sentiment and, as such, to determine those terms that introduce conflict with the previous query. Opinion mining, however, is not limited to just determining positive and negative views. In Kim and Hovy b sentences from online news media texts are examined to determine the topic and proponent of opinions being expressed.

The approach uses semantic role labeling to attach an opinion holder and topic to an opinion-bearing word in each sentence using FrameNet 12 a lexical database of English, based on manual annotation of how words are used in actual texts. To supplement the FrameNet data, a clustering technique is used to predict the most probable frame for words that FrameNet does not include.

This method is split into three subtasks:. Collection of opinion words and opinion-related frames—1, adjectives and 2, verbs classified into positive, negative, and neutral. Clustering By Committee Pantel is used to find the closest frame.

This method uses the hypothesis that words that occur in the same context tend to be similar. Semantic role labeling for those frames. A maximum entropy model is used to classify frame element types Stimulus, Degree, Experiencer, etc. Mapping of semantic roles to the opinion holder and topic. A manually built mapping table maps Frame Elements to a holder or topic.

Results show an increase from the baseline of 0. Although understanding the sentiment of a document as a whole could be a useful step in extracting the argument structure, the work carried out on sentiment analysis at a finer-grained level perhaps offers greater benefit still.

In Wilson, Wiebe, and Hoffmann , an approach to phrase-level sentiment analysis is presented, using a two-step process: first, applying a machine learning algorithm to classify a phrase as either neutral or polar for which an accuracy of 0.

In Sobhani, Inkpen, and Matwin , we see an example of extending simple pro and con sentiment analysis, to determine the stance which online comments take toward an article. These stances are then linked more clearly to the argumentative structure by using a topic model to determine what is being discussed in each comment, and classify it to a hierarchical structure of argument topics.

This combination of stance and topic hints at possible argumentative relations—for example, comments about the same topic that have opposing stance classifications are likely to be connected by conflict relations, whereas those with similar stance classifications are more likely to connect through support relations.

In Kim and Hovy a , the link between argument mining and opinion mining is clearer still. Instead of looking solely at whether online reviews are positive or negative, a system is developed for extracting the reasons why the review is positive or negative.

Using reviews from epinions. One extension to the field of opinion mining that has particular relevance to argument mining is controversy detection, where the aim is to identify controversial topics and text where conflicting points of view are being presented.

Controversy detection to date has largely targeted specific domains: Kittur et al. Of these, articles were additionally marked as being controversial in their most recent revision. A selection of these articles is then used as training data for an SVM classifier. Features are calculated from the specific page such as the length of the page, how many revisions were carried out, links from other articles, and the number of unique editors. It is reasonable to assume that the topics covered on those pages with a high CRC are controversial and, therefore, topics for which more complex argument is likely to occur.

The scope of controversy detection is broadened slightly in Choi, Jung, and Myaeng and Awadallah, Ramanath, and Weikum , who both look at identifying controversy in news articles. First, noun and verb phrases are identified as candidate issues using a mixture of sentiment models and topical information.

The degree of controversy for these issues is calculated by measuring the volume of both positive and negative sentiment and the difference between them. For subtopic extraction, noun phrases are identified as candidates and, for these phrases, three statistical features contextual similarity between the issue and a subtopic candidate, relatedness of a subtopic to sentiment, and the degree of physical vicinity between the issue and the candidate phrases as well as two positional features are calculated.

The results for subtopic identification are poor, with an F-score of 0. Awadallah, Ramanath, and Weikum present the OpinioNetIt system, which aims to automatically derive a map of the opinions-people network from news and other Web documents.

The network is constructed in four stages. First, generic terms are used to identify sample controversial topics; next, opinion holders are identified for each topic, and their opinions extracted; the acquired topics and opinion holders are then used to construct a lexicon of phrases indicating support or opposition.

Finally, this process is performed iteratively using the richer lexicon to identify more opinion holders, opinions, and topics. Using this approach a precision of 0. Despite the specific domain limitations of this controversy detection work, Dori-Hacohen and Allan extend their scope to detecting controversy on the Web as a whole, enabling users to be informed of controversial issues and alerted when alternative viewpoints are available.

Such widespread use of controversy detection offers the ability to address potential hotspot issues as they arise and the possibility of dealing with conflict in a debate at an early stage, before the quality of discussion can be negatively impacted.

Rumshisky et al. Such methods for determining controversial issues can play a significant role in determining the argumentative structure inherent in a piece of text. Those points that are controversial are likely to attract not only more attention, but also a more even mix of supporting and attacking views, than those on which there is broad consensus.

Lawrence et al. A proposition with many of both might be taken to be divisive, whereas few of either might suggest only limited divisiveness. Alternatively, given a pair of propositions that are in conflict, the divisiveness of this conflict is shown to be a measure of the amount of support on both sides.

It is easy to see how this process could be reversed, meaning that if we are able to identify controversial points in a piece of text, we already know something about the argumentative structure. Citation mining involves the labeling of citation instances in scientific writing with their rhetorical roles in the discourse. The techniques used to automatically determine the motivating factors behind each citation map closely to applications in argument mining, where text spans are labeled based on their argumentative role.

For example, if a citation is being used to highlight a gap or deficiency in the referenced work, then the language used will be suggestive of conflict relations between the two; if a citation is being used to back up the current work, then there are likely argumentative support relations between the two.

There are a broad range of manual schemes for classifying citation motivation and citation function the reason why an author chooses to cite a paper , and Teufel, Siddharthan, and Tidhar look at how this classification can be automated. Sentences containing citations are extracted first, before determining the opinion orientation of the subjective words in the context of the citation. From these opinion orientations, the attitude of the author toward the work that they are citing is labeled.

Athar takes a similar approach, whereby analysis is performed on a corpus of scientific texts taken from the ACL Anthology, and consisting of 8, citations from research papers manually annotated for their sentiment. Sentences are labeled as positive, negative, or objective, with 1, used for development and training.

POS tags are also included using two approaches: attaching the tag to the word by a delimiter, and appending all tags at the end of the sentence. A science-specific sentiment lexicon is also added, consisting of 83 polar phrases such as efficient, popular, successful, state-of-the-art, and effective.

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Introduced Bills. Bills, memorials and resolutions are measures enacted or under consideration by the Legislature. Invalid search. Motions Session Statistics.

Bitcoin Miner Pool

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Highlights of the 2020 AICPA Conference on Current SEC and PCAOB Developments (December 13, 2020)

A capital gains tax CGT event occurs when you dispose of your cryptocurrency. A disposal can occur when you:. If you make a capital gain on the disposal of cryptocurrency, some or all of the gain may be taxed. Certain capital gains or losses from disposing of a cryptocurrency that is a personal use asset are disregarded.

SMOLTECH: CONCERT [mathr, xname, Rumble-San, digital selves…

The challenge that digital artists, curators and collectors have faced for decades has been how to sell artwork that is by nature infinitely reproducible. Unlike a painting or sculpture, digital artworks can be reduced to a series of 0s and 1s, or an algorithm that can easily be copied. Non-fungible tokens, or NFTs, offer a technological solution. They work as public ledgers, using blockchain technology to track digital transactions. That also allows the artist to retain a percentage of the revenue each time their work is sold.

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This won't really do anything for availability or reducing scalper pricing. Miners are a sizeable proportion of sales but far from the majority. GPU prices won't actually change. The real reason Nvidia is doing this is that when mining profitability inevitably drops, either because of a crypto price crash or Ethereum moving to Proof of Stake, the miners will flood the used market and ruin Nvidia's profits for a few quarters. Nvidia is trying to get miners to buy mining-only cards, because those can't be sold to gamers down the line. They'd rather mining cards become e-waste in a few years than get resold.

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Welcome to Decentraland

The Bitcoin scalability problem refers to the limited capability of the Bitcoin network to handle large amounts of transaction data on its platform in a short span of time. Bitcoin's blocks contain the transactions on the bitcoin network. These jointly constrain the network's throughput. The transaction processing capacity maximum estimated using an average or median transaction size is between 3. The block size limit, in concert with the proof-of-work difficulty adjustment settings of bitcoin's consensus protocol, constitutes a bottleneck in bitcoin's transaction processing capacity.

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Blockchain technology is most simply defined as a decentralized, distributed ledger that records the provenance of a digital asset. By inherent design, the data on a blockchain is unable to be modified, which makes it a legitimate disruptor for industries like payments, cybersecurity and healthcare. Our guide will walk you through what it is, how it's used and its history. Blockchain, sometimes referred to as Distributed Ledger Technology DLT , makes the history of any digital asset unalterable and transparent through the use of decentralization and cryptographic hashing. A simple analogy for understanding blockchain technology is a Google Doc.

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