UNDERSTAND SHORTTEXTS BY HARVESTING & ANALYZING SEMANTIKNOWLEDGEPrasadu Peddi
The document proposes a system for understanding short texts by exploiting semantic knowledge from external knowledge bases and web corpora. It discusses challenges in analyzing short texts using traditional NLP methods due to their ambiguous and non-standard language. The system addresses this by harvesting semantic knowledge and applying knowledge-intensive approaches to tasks like segmentation, POS tagging and concept labeling to better understand semantics. An evaluation shows these knowledge-based methods are effective and efficient for short text understanding.
Answer extraction and passage retrieval forWaheeb Ahmed
—Question Answering systems (QASs) do the task of
retrieving text portions from a collection of documents that
contain the answer to the user’s questions. These QASs use a
variety of linguistic tools that be able to deal with small
fragments of text. Therefore, to retrieve the documents which
contains the answer from a large document collections, QASs
employ Information Retrieval (IR) techniques to minimize the
number of documents collections to a treatable amount of
relevant text. In this paper, we propose a model for passage
retrieval model that do this task with a better performance for
the purpose of Arabic QASs. We first segment each the top five
ranked documents returned by the IR module into passages.
Then, we compute the similarity score between the user’s
question terms and each passage. The top five passages (with
high similarity score) are retrieved are retrieved. Finally,
Answer Extraction techniques are applied to extract the final
answer. Our method achieved an average for precision of
87.25%, Recall of 86.2% and F1-measure of 87%.
The document describes a prototype system that provides information about common abbreviations to users through text messaging on low-cost mobile phones without internet access. The system receives an acronym query in Roman script from the user, scrapes a brief definition from the English Wikipedia page, translates it to the user's native language, and sends the response via text message transliterated in Roman script. It aims to help semi-literate users who may lack English knowledge or technological skills access information about abbreviations they encounter.
A Novel Method for Encoding Data Firmness in VLSI CircuitsEditor IJCATR
The number of tests, corresponding test data volume and test time increase with each new fabrication process technology.
Higher circuit densities in system-on-chip (SOC) designs have led to drastic increase in test data volume. Larger test data size demands
not only higher memory requirements, but also an increase in testing power and time. Test data compression method can be used to
solve this problem by reducing the test data volume without affecting the overall system performance. The original test data is
compressed and stored in the memory. Thus, the memory size is significantly reduced. The proposed approach combines the selective
encoding method and dictionary based encoding method that reduces test data volume and test application time for testing. The
experiment is done on combinational benchmark circuit that designed using Tanner tool and the encoding algorithm is implemented
using Model -Sim
Improved Count Suffix Trees for Natural Language DataWaqas Tariq
With more and more text data stored in databases, the problem of handling natural language query predicates becomes highly important. Closely related to query optimization for these predicates is the (sub)string estimation problem, i.e., estimating the selectivity of query terms before query execution based on small summary statistics. The Count Suffix Trees (CST) is the data structure commonly used to address this problem. While selectivity estimates based on CST tend to be good, they are computationally expensive to build and require a large amount of memory for storage. To fit CST into the data dictionary of database systems, they have to be pruned severely. Pruning techniques proposed so far are based on term (suffix) frequency or on the tree depth of nodes. In this paper, we propose new filtering and pruning techniques that reduce the building cost and the size of CST over natural-language texts. The core idea is to exploit the features of the natural language data over which the CST is built. In particular, we aim at regarding only those suffixes that are useful in a linguistic sense. We use (wellknown) IR techniques to identify them. The most important innovations are as follows: (a) We propose and use a new optimistic syllabification technique to filter out suffixes. (b) We introduce a new affix and prefix stripping procedure that is more aggressive than conventional stemming techniques, which are commonly used to reduce the size of indices. (c) We observe that misspellings and other language anomalies like foreign words incur an over-proportional growth of the CST. We apply state-of-the-art trigram techniques as well as a new syllable-based non-word detection mechanism to filter out such substrings. – Our evaluation with large English text corpora shows that our new mechanisms in combination decrease the size of a CST by up to 80%, already during construction, and at the same time increase the accuracy of selectivity estimates computed from the final CST by up to 70%.
Challenge of Image Retrieval, Brighton, 2000 1 ANVIL: a System for the Retrie...Petros Tsonis
ANVIL is a system designed for the retrieval of images annotated with short captions. It uses NLP techniques to extract dependency structures from captions and queries, and then applies a robust matching algorithm to recursively explore and compare them. There are currently two main interfaces to ANVIL: a list-based display and a 2D spatial layout that allows users to interact with and navigate between similar images. ANVIL was designed to operate as part of a publicly accessible, WWW-based image retrieval server. Consequently, product-level engineering standards were required. This paper examines both the research aspects of the system and also looks at some of the design and evaluation issues.
This document summarizes and compares four research papers related to Domain Name System (DNS) traffic and performance. The original 1991 research paper analyzed DNS traffic captured from root name servers and found that DNS consumed 20 times more bandwidth than necessary due to errors. It classified seven types of DNS implementation errors. The later papers proposed techniques like enhanced round robin DNS load balancing, a CDN-based DNS architecture, and a federated name resolution framework to address DNS issues in different network architectures and improve performance.
UNDERSTAND SHORTTEXTS BY HARVESTING & ANALYZING SEMANTIKNOWLEDGEPrasadu Peddi
The document proposes a system for understanding short texts by exploiting semantic knowledge from external knowledge bases and web corpora. It discusses challenges in analyzing short texts using traditional NLP methods due to their ambiguous and non-standard language. The system addresses this by harvesting semantic knowledge and applying knowledge-intensive approaches to tasks like segmentation, POS tagging and concept labeling to better understand semantics. An evaluation shows these knowledge-based methods are effective and efficient for short text understanding.
Answer extraction and passage retrieval forWaheeb Ahmed
—Question Answering systems (QASs) do the task of
retrieving text portions from a collection of documents that
contain the answer to the user’s questions. These QASs use a
variety of linguistic tools that be able to deal with small
fragments of text. Therefore, to retrieve the documents which
contains the answer from a large document collections, QASs
employ Information Retrieval (IR) techniques to minimize the
number of documents collections to a treatable amount of
relevant text. In this paper, we propose a model for passage
retrieval model that do this task with a better performance for
the purpose of Arabic QASs. We first segment each the top five
ranked documents returned by the IR module into passages.
Then, we compute the similarity score between the user’s
question terms and each passage. The top five passages (with
high similarity score) are retrieved are retrieved. Finally,
Answer Extraction techniques are applied to extract the final
answer. Our method achieved an average for precision of
87.25%, Recall of 86.2% and F1-measure of 87%.
The document describes a prototype system that provides information about common abbreviations to users through text messaging on low-cost mobile phones without internet access. The system receives an acronym query in Roman script from the user, scrapes a brief definition from the English Wikipedia page, translates it to the user's native language, and sends the response via text message transliterated in Roman script. It aims to help semi-literate users who may lack English knowledge or technological skills access information about abbreviations they encounter.
A Novel Method for Encoding Data Firmness in VLSI CircuitsEditor IJCATR
The number of tests, corresponding test data volume and test time increase with each new fabrication process technology.
Higher circuit densities in system-on-chip (SOC) designs have led to drastic increase in test data volume. Larger test data size demands
not only higher memory requirements, but also an increase in testing power and time. Test data compression method can be used to
solve this problem by reducing the test data volume without affecting the overall system performance. The original test data is
compressed and stored in the memory. Thus, the memory size is significantly reduced. The proposed approach combines the selective
encoding method and dictionary based encoding method that reduces test data volume and test application time for testing. The
experiment is done on combinational benchmark circuit that designed using Tanner tool and the encoding algorithm is implemented
using Model -Sim
Improved Count Suffix Trees for Natural Language DataWaqas Tariq
With more and more text data stored in databases, the problem of handling natural language query predicates becomes highly important. Closely related to query optimization for these predicates is the (sub)string estimation problem, i.e., estimating the selectivity of query terms before query execution based on small summary statistics. The Count Suffix Trees (CST) is the data structure commonly used to address this problem. While selectivity estimates based on CST tend to be good, they are computationally expensive to build and require a large amount of memory for storage. To fit CST into the data dictionary of database systems, they have to be pruned severely. Pruning techniques proposed so far are based on term (suffix) frequency or on the tree depth of nodes. In this paper, we propose new filtering and pruning techniques that reduce the building cost and the size of CST over natural-language texts. The core idea is to exploit the features of the natural language data over which the CST is built. In particular, we aim at regarding only those suffixes that are useful in a linguistic sense. We use (wellknown) IR techniques to identify them. The most important innovations are as follows: (a) We propose and use a new optimistic syllabification technique to filter out suffixes. (b) We introduce a new affix and prefix stripping procedure that is more aggressive than conventional stemming techniques, which are commonly used to reduce the size of indices. (c) We observe that misspellings and other language anomalies like foreign words incur an over-proportional growth of the CST. We apply state-of-the-art trigram techniques as well as a new syllable-based non-word detection mechanism to filter out such substrings. – Our evaluation with large English text corpora shows that our new mechanisms in combination decrease the size of a CST by up to 80%, already during construction, and at the same time increase the accuracy of selectivity estimates computed from the final CST by up to 70%.
Challenge of Image Retrieval, Brighton, 2000 1 ANVIL: a System for the Retrie...Petros Tsonis
ANVIL is a system designed for the retrieval of images annotated with short captions. It uses NLP techniques to extract dependency structures from captions and queries, and then applies a robust matching algorithm to recursively explore and compare them. There are currently two main interfaces to ANVIL: a list-based display and a 2D spatial layout that allows users to interact with and navigate between similar images. ANVIL was designed to operate as part of a publicly accessible, WWW-based image retrieval server. Consequently, product-level engineering standards were required. This paper examines both the research aspects of the system and also looks at some of the design and evaluation issues.
This document summarizes and compares four research papers related to Domain Name System (DNS) traffic and performance. The original 1991 research paper analyzed DNS traffic captured from root name servers and found that DNS consumed 20 times more bandwidth than necessary due to errors. It classified seven types of DNS implementation errors. The later papers proposed techniques like enhanced round robin DNS load balancing, a CDN-based DNS architecture, and a federated name resolution framework to address DNS issues in different network architectures and improve performance.
Implementing a Caching Scheme for Media Streaming in a Proxy ServerAbdelrahman Hosny
In the past few years, websites have moved from being
static web pages into rich media applications that use audio,
images and videos heavily in their interaction with users. This
change has made a dramatic change in network traffics
nowadays. Organizations spend a lot of effort, time and money
to improve response time and design intermediary systems that
enhance overall user experience. Media traffic represents
about 69.9-88.8% of all traffic. Therefore, enhancing networks
to accommodate this large traffic is a major trend. Content
Distribution Networks (CDNs) are now largely deployed for a
faster delivery of media. Redundancy and caching are also
implemented to decrease response time.
In this project, we are implementing a caching scheme for
media streaming in a proxy server. Unlike CDNs, which
require huge infrastructure, our caching proxy server will be
as simple as a piece of software that is portable and can be
installed in small as well as large scales. It may be deployed in
a university network, company’s private network or on ISPs
servers. This caching scheme, specially tailored for media
streaming, will reduce traffic and enhance network efficiency
in general.
Index Terms – Proxy servers, Caching, Media streaming
The document describes a voice activated text editing software project that uses speech recognition, speech synthesis, and machine learning based text summarization. The software allows users to create notes, import documents, and perform text functions using voice commands. It was created to reduce the time spent manually typing documents and to provide independent digital note-taking for visually impaired individuals. The system design and algorithms for extractive and abstractive text summarization are presented along with experimental results and comparisons to other systems.
ANALYTICS OF PATENT CASE RULINGS: EMPIRICAL EVALUATION OF MODELS FOR LEGAL RE...Kripa (कृपा) Rajshekhar
Recent progress in incorporating word order and semantics to the decades-old, tried-and-tested bag-of-words representation of text meaning has yielded promising results in computational text classification and analysis. This development, and the availability of a large number of legal rulings from the PTAB (Patent Trial and Appeal Board motivated us to revisit possibilities for practical, computational models of legal relevance -- starting with this narrow and approachable niche of jurisprudence. We present results from our analysis and experiments towards this goal using a corpus of approximately 8000 rulings from the PTAB. This work makes three important contributions towards the development of models for legal relevance semantics: (a) Using state-of-art Natural Language Processing (NLP) methods, we characterize the diversity and types of semantic relationships that are implicit in select judgements of legal relevance at the PTAB (b) We achieve new state-of-art results on practical information retrieval tasks using our customized semantic representations on this corpus (c) We outline promising avenues for future work in the area - including preliminary evidence from human-in-loop interaction, and new forms of text representation developed using input from over a hundred interviews with practitioners in the field. Using the PTAB data set for testing relevance in patent document retrieval, instead of traditional citations search, also shows a bigger gap between the needs of practitioners and the capabilities of current information retrieval and NLP technologies.
The document discusses how media content can influence society. It notes that while media were originally used for education and information, entertainment has become a major role. Television in particular has had a huge influence since being introduced. The document discusses how media portrayals can shape perceptions and spread misinformation, citing examples of how television influences behavior in young people and how some news coverage has contributed to growing Islamophobia.
A NOVEL APPROACH OF CLASSIFICATION TECHNIQUES FOR CLIRcscpconf
Recent and continuing advances in online information systems are creating many opportunities
and also new problems in information retrieval. Gathering the information in different natural
language is the most difficult task, which often requires huge resources. Cross-language
information retrieval (CLIR) is the retrieval of information for a query written in the native
language. This paper deals with various classification techniques that can be used for solving
the problems encountered in CLIR.
This document summarizes a technical seminar about Grouper, a system that provides dynamic document clustering of web search results. Grouper clusters search results using the STC algorithm to group similar documents together and make the results easier to browse. An evaluation found that Grouper produced more coherent clusters than k-means clustering and that users viewed more documents, spent less time per document, and had shorter distances between clicks when using Grouper compared to a standard ranked search results list. The conclusion discusses some limitations of the initial Grouper system and proposes improvements in a next version.
This document discusses test estimation techniques. It explains that good estimates are accurate, realistic, and actionable. It recommends asking experts, using metrics and industry averages, and negotiating with managers. It also discusses estimating techniques like planning poker, three-point estimates, and understanding dependencies. The document emphasizes using historical data to predict testing time and the number of bugs found.
Understanding Natural Languange with Corpora-based Generation of Dependency G...Edmond Lepedus
This document discusses training a dependency parser using an unparsed corpus rather than a manually parsed training set. It develops an iterative training method that generates training examples using heuristics from past parsing decisions. The method is shown to produce parse trees qualitatively similar to conventionally trained parsers. Three avenues for future research using this corpus-based generation method are proposed.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Real-time DirectTranslation System for Sinhala and Tamil Languages.Sheeyam Shellvacumar
Presented my research on "Real-time DirectTranslation System for Sinhala and Tamil Languages" at the FedCSIS 2015 Research Conference hosted by University of Lodz, Poland from 13 - 17th of September 2015.
1) Scientific research increasingly relies on large-scale data transfers between collaborating institutions over high-speed networks.
2) ESNet provides high-bandwidth connectivity between DOE sites but needs to efficiently allocate guaranteed bandwidth for transfers.
3) The presentation proposes enhancements to ESNet's OSCARS reservation system to suggest optimal reservations that meet researchers' bandwidth and timing requirements for data transfers.
The document discusses research on quantifying user satisfaction (QoE) in VoIP applications like Skype calls. It presents three key contributions:
1) Developing the first QoE measurement methodology based on analyzing large-scale Skype call data to correlate call duration with network quality factors like jitter and bit rate.
2) Proposing OneClick, a simple framework for crowdsourced QoE experiments based on user clicks to indicate dissatisfaction.
3) Introducing the first crowdsourcable QoE evaluation methodology to verify user judgments.
This document provides an overview of machine translation and the Moses machine translation toolkit. It defines machine translation and statistical machine translation. It describes the major components of Moses, including GIZA++ for word alignment, SRILM for language modeling, and the Moses decoder. It explains how Moses uses phrase-based translation and tuning to produce translations. It also discusses how to set up and use a Moses server for translating webpages.
This document describes a new English audio and textual dataset of recorded debating speeches produced by professional debaters. The dataset includes 60 speeches on various controversial topics, each provided in 5 formats corresponding to different stages of production: 1) the original audio recording, 2) an automatic transcription from speech recognition, 3) a "cleaned" version of the automatic transcription, 4) a manual transcription serving as a reference, and 5) background information on the topic. The intention is to make the data available for various computational argumentation and debating research purposes such as training speech recognition systems or applying argument mining techniques.
Semi-Supervised Keyword Spotting in Arabic Speech Using Self-Training EnsemblesMohamed El-Geish
Arabic speech recognition suffers from the scarcity of properly labeled data. In this project, we introduce a pipeline that performs semi-supervised segmentation of audio then— after hand-labeling a small dataset—feeds labeled segments to a supervised learning framework to select, through many rounds of hyperparameter optimization, an ensemble of models to infer labels for a larger dataset; using which we improved the keyword spotter’s F1 score from 75.85% (using a baseline model) to 90.91% on a ground-truth test set. We picked the keyword na`am (yes) to spot; we defined the system’s input as an audio file of an utterance and the output as a binary label: keyword or filler.
ANALYTICS OF PATENT CASE RULINGS: EMPIRICAL EVALUATION OF MODELS FOR LEGAL RE...Kripa (कृपा) Rajshekhar
Recent progress in incorporating word order and semantics to the decades-old, tried-and-tested bag-of-words representation of text meaning has yielded promising results in computational text classification and analysis. This development, and the availability of a large number of legal rulings from the PTAB (Patent Trial and Appeal Board) motivated us to revisit possibilities for practical, computational models of legal relevance - starting with this narrow and approachable niche of jurisprudence. We present results from our analysis and experiments towards this goal using a corpus of approximately 8000 rulings from the PTAB. This work makes three important contributions towards the development of models for legal relevance semantics: (a) Using state-of-art Natural Language Processing (NLP) methods, we characterize the diversity and types of semantic relationships that are implicit in practical judgements of legal relevance at the PTAB (b) We achieve new state-of-art results on practical information retrieval using our customized semantic representations on this corpus (c) We outline promising avenues for future work in the area - including preliminary evidence from human-in-loop interaction, and new forms of text representation developed using input from over a hundred interviews with practitioners in the field. Finally, we argue that PTAB relevance is a practical and realistic baseline for performance measurement - with the desirable property of evaluating NLP improvements against “real world” legal judgement.
A Two-Tiered On-Line Server-Side Bandwidth Reservation Framework for the Real...white paper
This document summarizes a two-tiered bandwidth reservation framework for delivering multiple video streams from servers in real-time. The framework uses a combination of per-stream reservations and a shared aggregate reservation across all streams. Each stream is allocated a guaranteed reservation equal to the p percentile of its bandwidth distribution. An additional shared reservation provides statistical multiplexing of peak bandwidth demands. This enables delivery of streams with less total bandwidth than deterministic approaches while bounding frame drop probabilities based on system parameters. The document proposes an online admission control algorithm that uses three pre-computed parameters per stream and has linear complexity in the number of servers.
An Efficient Distributed Control Law for Load Balancing in Content Delivery N...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
MADS4007_Fall2022-Intro to Web Technologies.docx.pptxawadalsabbah
The document provides an origin story of HTTP from Dr. Tim Berners-Lee at CERN in 1989. Dr. Berners-Lee developed HTTP as a stateless protocol to allow computers to communicate and access information stored on servers. He explained that each HTTP request is treated independently, with the server not storing information about previous requests. While the students understood the concept, the stateless nature of HTTP would later require workarounds for web commerce.
FINDING OUT NOISY PATTERNS FOR RELATION EXTRACTION OF BANGLA SENTENCESkevig
Relation extraction is one of the most important parts of natural language processing. It is the process of extracting relationships from a text. Extracted relationships actually occur between two or more entities of a certain type and these relations may have different patterns. The goal of the paper is to find out the noisy patterns for relation extraction of Bangla sentences. For the research work, seed tuples were needed containing two entities and the relation between them. We can get seed tuples from Freebase. Freebase is a large collaborative knowledge base and database of general, structured information for public use. But for Bangla language, there is no available Freebase. So we made Bangla Freebase which was the real challenge and it can be used for any other NLP based works. Then we tried to find out the noisy patterns for relation extraction by measuring conflict score.
Tutorial given at RANLP 2015 in Hissar, Bulgaria
Recent years have seen lots of changes in the field of computational linguistics, most of them due to the widespread use of the Internet and the benefits and problems it brings. The first part of this tutorial will discuss these changes and will focus on crowdsourcing and how it influenced the creation of annotated data.
Annotation of data employed to train and test NLP methods used to be the task of language experts who had a good understanding of the linguistic phenomena to be tackled. Given that a large number of people now have access to the Internet, crowdsourcing has become an alternative way of obtaining annotated data. The core idea of crowdsourcing is that it is possible to design tasks that can be completed by non-experts and that the outputs of these tasks can be combined to obtain high-quality linguistic annotation, which would normally be produced by experts. Examples of how crowdsourcing was employed in computational linguistics will be given.
Big data is another trend in computational linguistics as researchers rely on more and more data for improving the results of a method. The second part of the tutorial will introduce the MapReduce programming model and show how it was used in processing language. Combined with processing larger quantities of data, the field of computational linguistics has applied deep learning to various tasks successfully, improving their accuracy. An introduction to deep learning will be provided, followed by examples of how it was applied to tasks such as learning semantic representations, sentiment analysis and machine translation evaluation.
From TREC to Watson: is open domain question answering a solved problem?Constantin Orasan
The document summarizes a presentation on question answering systems. It begins by providing context on information overload and defining question answering. It then discusses the evolution of QA systems from early databases to today's open-domain systems. The presentation focuses on IBM's Watson system, providing an overview of its unprecedented ability to answer open-domain questions as well as the massive resources required for its development. It concludes by arguing that open-domain QA remains unsolved and that closed-domain, interactive QA may be more practical for real-world applications.
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In the past few years, websites have moved from being
static web pages into rich media applications that use audio,
images and videos heavily in their interaction with users. This
change has made a dramatic change in network traffics
nowadays. Organizations spend a lot of effort, time and money
to improve response time and design intermediary systems that
enhance overall user experience. Media traffic represents
about 69.9-88.8% of all traffic. Therefore, enhancing networks
to accommodate this large traffic is a major trend. Content
Distribution Networks (CDNs) are now largely deployed for a
faster delivery of media. Redundancy and caching are also
implemented to decrease response time.
In this project, we are implementing a caching scheme for
media streaming in a proxy server. Unlike CDNs, which
require huge infrastructure, our caching proxy server will be
as simple as a piece of software that is portable and can be
installed in small as well as large scales. It may be deployed in
a university network, company’s private network or on ISPs
servers. This caching scheme, specially tailored for media
streaming, will reduce traffic and enhance network efficiency
in general.
Index Terms – Proxy servers, Caching, Media streaming
The document describes a voice activated text editing software project that uses speech recognition, speech synthesis, and machine learning based text summarization. The software allows users to create notes, import documents, and perform text functions using voice commands. It was created to reduce the time spent manually typing documents and to provide independent digital note-taking for visually impaired individuals. The system design and algorithms for extractive and abstractive text summarization are presented along with experimental results and comparisons to other systems.
ANALYTICS OF PATENT CASE RULINGS: EMPIRICAL EVALUATION OF MODELS FOR LEGAL RE...Kripa (कृपा) Rajshekhar
Recent progress in incorporating word order and semantics to the decades-old, tried-and-tested bag-of-words representation of text meaning has yielded promising results in computational text classification and analysis. This development, and the availability of a large number of legal rulings from the PTAB (Patent Trial and Appeal Board motivated us to revisit possibilities for practical, computational models of legal relevance -- starting with this narrow and approachable niche of jurisprudence. We present results from our analysis and experiments towards this goal using a corpus of approximately 8000 rulings from the PTAB. This work makes three important contributions towards the development of models for legal relevance semantics: (a) Using state-of-art Natural Language Processing (NLP) methods, we characterize the diversity and types of semantic relationships that are implicit in select judgements of legal relevance at the PTAB (b) We achieve new state-of-art results on practical information retrieval tasks using our customized semantic representations on this corpus (c) We outline promising avenues for future work in the area - including preliminary evidence from human-in-loop interaction, and new forms of text representation developed using input from over a hundred interviews with practitioners in the field. Using the PTAB data set for testing relevance in patent document retrieval, instead of traditional citations search, also shows a bigger gap between the needs of practitioners and the capabilities of current information retrieval and NLP technologies.
The document discusses how media content can influence society. It notes that while media were originally used for education and information, entertainment has become a major role. Television in particular has had a huge influence since being introduced. The document discusses how media portrayals can shape perceptions and spread misinformation, citing examples of how television influences behavior in young people and how some news coverage has contributed to growing Islamophobia.
A NOVEL APPROACH OF CLASSIFICATION TECHNIQUES FOR CLIRcscpconf
Recent and continuing advances in online information systems are creating many opportunities
and also new problems in information retrieval. Gathering the information in different natural
language is the most difficult task, which often requires huge resources. Cross-language
information retrieval (CLIR) is the retrieval of information for a query written in the native
language. This paper deals with various classification techniques that can be used for solving
the problems encountered in CLIR.
This document summarizes a technical seminar about Grouper, a system that provides dynamic document clustering of web search results. Grouper clusters search results using the STC algorithm to group similar documents together and make the results easier to browse. An evaluation found that Grouper produced more coherent clusters than k-means clustering and that users viewed more documents, spent less time per document, and had shorter distances between clicks when using Grouper compared to a standard ranked search results list. The conclusion discusses some limitations of the initial Grouper system and proposes improvements in a next version.
This document discusses test estimation techniques. It explains that good estimates are accurate, realistic, and actionable. It recommends asking experts, using metrics and industry averages, and negotiating with managers. It also discusses estimating techniques like planning poker, three-point estimates, and understanding dependencies. The document emphasizes using historical data to predict testing time and the number of bugs found.
Understanding Natural Languange with Corpora-based Generation of Dependency G...Edmond Lepedus
This document discusses training a dependency parser using an unparsed corpus rather than a manually parsed training set. It develops an iterative training method that generates training examples using heuristics from past parsing decisions. The method is shown to produce parse trees qualitatively similar to conventionally trained parsers. Three avenues for future research using this corpus-based generation method are proposed.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Real-time DirectTranslation System for Sinhala and Tamil Languages.Sheeyam Shellvacumar
Presented my research on "Real-time DirectTranslation System for Sinhala and Tamil Languages" at the FedCSIS 2015 Research Conference hosted by University of Lodz, Poland from 13 - 17th of September 2015.
1) Scientific research increasingly relies on large-scale data transfers between collaborating institutions over high-speed networks.
2) ESNet provides high-bandwidth connectivity between DOE sites but needs to efficiently allocate guaranteed bandwidth for transfers.
3) The presentation proposes enhancements to ESNet's OSCARS reservation system to suggest optimal reservations that meet researchers' bandwidth and timing requirements for data transfers.
The document discusses research on quantifying user satisfaction (QoE) in VoIP applications like Skype calls. It presents three key contributions:
1) Developing the first QoE measurement methodology based on analyzing large-scale Skype call data to correlate call duration with network quality factors like jitter and bit rate.
2) Proposing OneClick, a simple framework for crowdsourced QoE experiments based on user clicks to indicate dissatisfaction.
3) Introducing the first crowdsourcable QoE evaluation methodology to verify user judgments.
This document provides an overview of machine translation and the Moses machine translation toolkit. It defines machine translation and statistical machine translation. It describes the major components of Moses, including GIZA++ for word alignment, SRILM for language modeling, and the Moses decoder. It explains how Moses uses phrase-based translation and tuning to produce translations. It also discusses how to set up and use a Moses server for translating webpages.
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Harnessing Court Data using NLP and Spoken Language Technology
1. Harnessing court data using NLP and spoken language technology
Constantin Orăsan and Hadeel Saadany
{C.Orasan,Hadeel.Saadany}@surrey.ac.uk
Objective 1: Improve Automatic Transcription of Courtroom Hearings
Amazon Web Service Transcription
gives errors with legal terminology/
jargon, named entities, numbers
We minimize the transcription errors by building a Custom
Language Model (CLM) trained on legal text and edited
transcriptions. Our CLM results correct most errors of AWS
generic model and reduce Word Error Rate (WER)
1. Employ NLP tools to extract a vocabulary list with most common bigrams in the edited transcripts.
2. Employ Blackstone library to create a list of legal terms
3. Scraped a large dataset of judgements (approx. 25k documents) from the UK Tribunal Decisions
(https://www.judiciary.uk) to use for training the CLM.
4. Use the extracted list of legal terms and most common entities to build CLM using different
training datasets (supreme court judgements and edited transcripts).
Our pipeline for Improving Transcription Quality
Objective 2: Linking Judgement to Transcripts
The objective and design expectations
• Transcripts (very long 10+ hrs.) can be used as a tool for
students/academics/lawyers to learn about a particular law.
• We semantically link time spans of transcription to
sections/paragraphs in the judgement.
• This can help understand how the judgment is reached.
Progress so far
• GPT 3 semantic search spots the gold-standard timestamp in
a list of top 20 most relevant.
• Manual analysis of results shows that the 20 extracted
timestamps are relevant to the judgement and
demonstrates good linking.
Our Method
We treat the judgement sections as a Query and the Transcriptions as potential
answers. We rank spans using the cosine similarity between the GPT3 sentence
embeddings of the judgement and transcription spans
Next steps
• Create a large gold standard for training and testing
• Experiment with more NLP methods to improve linking
accuracy
• Produce a smaller list of timestamps linked to judgement
• Gain a better understanding of how the users would use
such a system
• Create a pipeline for cleaning, segmenting and linking the
Courtroom hearings and the Transcriptions produced by
our CLM model
Publications
Saadany, H., Breslin, C., Orăsan, C. and Walker, S. (2022) Better Transcription of UK Supreme Court Hearings. arXiv:2211.17094