Lexical substitution is the task of identifying a substitute for a word in the context of a clause. For instance, given the following text: "After the match, replace any remaining fluid deficit to prevent chronic dehydration throughout the tournament", a substitute of game might be given. Lexical substitution is strictly related to word sense disambiguation (WSD), in that both aim to determine the meaning of a word. However, while WSD consists of automatically assigning the appropriate sense from a fixed sense inventory, lexical substitution does not impose any constraint on which substitute to choose as the best representative for the word in context. By not prescribing the inventory, lexical substitution overcomes the issue of the granularity of sense distinctions and provides a level playing field for automatic systems that automatically acquire word senses (a task referred to as Word Sense Induction). == Evaluation == In order to evaluate automatic systems on lexical substitution, a task was organized at the Semeval-2007 evaluation competition held in Prague in 2007. A Semeval-2010 task on cross-lingual lexical substitution has also taken place. == Skip-gram model == The skip-gram model takes words with similar meanings into a vector space (collection of objects that can be added together and multiplied by numbers) that are found close to each other in N-dimensions (list of items). A variety of neural networks (computer system modeled after a human brain) are formed together as a result of the vectors and networks that are related together. This all occurs in the dimensions of the vocabulary that has been generated in a network. The model has been used in lexical substitution automation and prediction algorithms. One such algorithm developed by Oren Melamud, Omer Levy, and Ido Dagan uses the skip-gram model to find a vector for each word and its synonyms. Then, it calculates the cosine distance between vectors to determine which words will be the best substitutes. === Example === In a sentence like "The dog walked at a quick pace" each word has a specific vector in relation to the other. The vector for "The" would be [1,0,0,0,0,0,0] because the 1 is the word vocabulary and the 0s are the words surrounding that vocabulary, which create a vector.
AZFinText
Arizona Financial Text System (AZFinText) is a textual-based quantitative financial prediction system written by Robert P. Schumaker of University of Texas at Tyler and Hsinchun Chen of the University of Arizona. == System == This system differs from other systems in that it uses financial text as one of its key means of predicting stock price movement. This reduces the information lag-time problem evident in many similar systems where new information must be transcribed (e.g., such as losing a costly court battle or having a product recall), before the quant can react appropriately. AZFinText overcomes these limitations by utilizing the terms used in financial news articles to predict future stock prices twenty minutes after the news article has been released. It is believed that certain article terms can move stocks more than others. Terms such as factory exploded or workers strike will have a depressing effect on stock prices whereas terms such as earnings rose will tend to increase stock prices. The AZFinText system analyzes financial news to identify the patterns in how investors react to such specific information. It uses methods like sentiment analysis and term weighting to examine the text of news articles. This system is designed to find price differences that occur when the market responds to news stories. This approach provides an alternative and easier method for predicting stock market movements. == Overview of research == The foundation of AZFinText can be found in the ACM TOIS article. Within this paper, the authors tested several different prediction models and linguistic textual representations. From this work, it was found that using the article terms and the price of the stock at the time the article was released was the most effective model and using proper nouns was the most effective textual representation technique. Combining the two, AZFinText netted a 2.84% trading return over the five-week study period. AZFinText was then extended to study what combination of peer organizations help to best train the system. Using the premise that IBM has more in common with Microsoft than GM, AZFinText studied the effect of varying peer-based training sets. To do this, AZFinText trained on the various levels of GICS and evaluated the results. It was found that sector-based training was most effective, netting an 8.50% trading return, outperforming Jim Cramer, Jim Jubak and DayTraders.com during the study period. AZFinText was also compared against the top 10 quantitative systems and outperformed 6 of them. A third study investigated the role of portfolio building in a textual financial prediction system. From this study, Momentum and Contrarian stock portfolios were created and tested. Using the premise that past winning stocks will continue to win and past losing stocks will continue to lose, AZFinText netted a 20.79% return during the study period. It was also noted that traders were generally overreacting to news events, creating the opportunity of abnormal returns. A fourth study looked into using author sentiment as an added predictive variable. Using the premise that an author can unwittingly influence market trades simply by the terms they use, AZFinText was tested using tone and polarity features. It was found that Contrarian activity was occurring within the market, where articles of a positive tone would decrease in price and articles of a negative tone would increase in price. A further study investigated what article verbs have the most influence on stock price movement. From this work, it was found that planted, announcing, front, smaller and crude had the highest positive impact on stock price. == Notable publicity == AZFinText has been the topic of discussion by numerous media outlets. Some of the more notable ones include The Wall Street Journal, MIT's Technology Review, Dow Jones Newswire, WBIR in Knoxville, TN, Slashdot and other media outlets.
Trie
In computer science, a trie (, ), also known as a digital tree or prefix tree, is a specialized search tree data structure used to store and retrieve strings from a dictionary or set. Unlike a binary search tree, nodes in a trie do not store their associated key. Instead, each node's position within the trie determines its associated key, with the connections between nodes defined by individual characters rather than the entire key. Tries are particularly effective for tasks such as autocomplete, spell checking, and IP routing, offering advantages over hash tables due to their prefix-based organization and lack of hash collisions. Every child node shares a common prefix with its parent node, and the root node represents the empty string. While basic trie implementations can be memory-intensive, various optimization techniques such as compression and bitwise representations have been developed to improve their efficiency. A notable optimization is the radix tree, which provides more efficient prefix-based storage. While tries store character strings, they can be adapted to work with any ordered sequence of elements, such as permutations of digits or shapes. A notable variant is the bitwise trie, which uses individual bits from fixed-length binary data (such as integers or memory addresses) as keys. == History, etymology, and pronunciation == The idea of a trie for representing a set of strings was first abstractly described by Axel Thue in 1912. Tries were first described in a computer context by René de la Briandais in 1959. The idea was independently described in 1960 by Edward Fredkin, who coined the term trie, pronouncing it (as "tree"), after the middle syllable of retrieval. However, other authors pronounce it (as "try"), in an attempt to distinguish it verbally from "tree". == Overview == Tries are a form of string-indexed look-up data structure, which is used to store a dictionary list of words that can be searched on in a manner that allows for efficient generation of completion lists. A prefix trie is an ordered tree data structure used in the representation of a set of strings over a finite alphabet set, which allows efficient storage of words with common prefixes. Tries can be efficacious on string-searching algorithms such as predictive text, approximate string matching, and spell checking in comparison to binary search trees. A trie can be seen as a tree-shaped deterministic finite automaton. == Operations == Tries support various operations: insertion, deletion, and lookup of a string key. Tries are composed of nodes that contain links, which either point to other suffix child nodes or null. As for every tree, each node except the root is pointed to by only one other node, called its parent. Each node contains as many links as the number of characters in the applicable alphabet (although tries tend to have a substantial number of null links). In some cases, the alphabet used is simply that of the character encoding—resulting in, for example, a size of 128 in the case of ASCII. The null links within the children of a node emphasize the following characteristics: Characters and string keys are implicitly stored in the trie, and include a character sentinel value indicating string termination. Each node contains one possible link to a prefix of strong keys of the set. A basic structure type of nodes in the trie is as follows: Node {\displaystyle {\text{Node}}} may contain an optional Value {\displaystyle {\text{Value}}} , which is associated with the key that corresponds to the node. === Searching === Searching for a value in a trie is guided by the characters in the search string key, as each node in the trie contains a corresponding link to each possible character in the given string. Thus, following the string within the trie yields the associated value for the given string key. A null link during the search indicates the inexistence of the key. The following pseudocode implements the search procedure for a given string key in a rooted trie x. In the above pseudocode, x and key correspond to the pointer of the trie's root node and the string key, respectively. The search operation takes O ( m ) {\displaystyle O(m)} time, where m {\displaystyle m} is the size of the string parameter key. In a balanced binary search tree, on the other hand, it takes O ( m log n ) {\displaystyle O(m\log n)} time, in the worst case, since key needs to be compared with O ( log n ) {\displaystyle O(\log n)} other keys and each comparison takes O ( m ) {\displaystyle O(m)} time, in the worst case. The trie occupies less space, in comparison with a binary search tree, in the case of a large number of short strings, since nodes share common initial string subsequences and store the keys implicitly. === Insertion === Insertion into a trie is guided by using the character sets as indexes to the children array until the last character of the string key is reached. Each node in the trie corresponds to one call of the radix sorting routine, as the trie structure reflects the execution pattern of the top-down radix sort. If null links are encountered before reaching the last character of the string key, new nodes are created. The input value is assigned to the value of the last node traversed, which is the node that corresponds to the key. === Deletion === Deletion of a key–value pair from a trie involves finding the node corresponding to the key, setting its value to null, and recursively removing nodes that have no children. The procedure begins by examining key; an empty string indicates arrival at the node corresponding to the (original) key, in which case its value is set to null. If the node, then, has null value and no children, it is removed from the trie by returning null; otherwise, the node is kept by returning the node itself. == Replacing other data structures == === Replacement for hash tables === A trie can be used to replace a hash table, over which it has the following advantages: Searching for a node with an associated key of size m {\displaystyle m} has the complexity of O ( m ) {\displaystyle O(m)} , whereas an imperfect hash function may have numerous colliding keys, and the worst-case lookup speed of such a table would be O ( N ) {\displaystyle O(N)} , where N {\displaystyle N} denotes the total number of nodes within the table. Tries do not need a hash function for the operation, unlike a hash table; there are also no collisions of different keys in a trie. Within a trie, keys can be efficiently sorted lexicographically. However, tries are less efficient than a hash table when the data is directly accessed on a secondary storage device such as a hard disk drive that has higher random access time than the main memory. == Implementation strategies == Tries can be represented in several ways, corresponding to different trade-offs between memory use and speed of the operations. Using a vector of pointers for representing a trie consumes enormous space; however, memory space can be reduced at the expense of running time if a singly linked list is used for each node vector, as most entries of the vector contains nil {\displaystyle {\text{nil}}} . Techniques such as alphabet reduction may reduce the large space requirements by reinterpreting the original string as a longer string over a smaller alphabet. For example, a string of n bytes can alternatively be regarded as a string of 2n four-bit units. This can reduce memory usage by a factor of eight; but lookups need to visit twice as many nodes in the worst case. Another technique includes storing a vector of 256 ASCII pointers as a bitmap of 256 bits representing ASCII alphabet, which reduces the size of individual nodes dramatically. === Bitwise tries === Bitwise tries are used to address the enormous space requirement for the trie nodes in a naive simple pointer vector implementations. Each character in the string key set is represented via individual bits, which are used to traverse the trie over a string key. The implementations for these types of trie use vectorized CPU instructions to find the first set bit in a fixed-length key input (e.g. GCC's __builtin_clz() intrinsic function). Accordingly, the set bit is used to index the first item, or child node, in the 32- or 64-entry based bitwise tree. Search then proceeds by testing each subsequent bit in the key. This procedure is also cache-local and highly parallelizable due to register independency, and thus performant on out-of-order execution CPUs. === Compressed tries === Radix tree, also known as a compressed trie, is a space-optimized variant of a trie in which any node with only one child gets merged with its parent; elimination of branches of the nodes with a single child results in better metrics in both space and time. This works best when the trie remains static and set of keys stored are very sparse within their representation space. One more approach for static tries is to "pack" the trie by storing disjoint
Brian D. Ripley
Brian David Ripley FRSE (born 29 April 1952) is a British statistician. From 1990, he was professor of applied statistics at the University of Oxford and also a professorial fellow at St Peter's College. He retired August 2014 due to ill health. == Biography == Ripley has made contributions to the fields of spatial statistics and pattern recognition. His work on artificial neural networks in the 1990s helped to bring aspects of machine learning and data mining to the attention of statistical audiences. He emphasised the value of robust statistics in his books Pattern Recognition and Neural Networks and Modern Applied Statistics with S. Ripley helped develop the S-PLUS programming language and its open source derivative R. He co-authored two books based on S, S Programming and Modern Applied Statistics with S. Since mid-1997 he is a member of the "R Core Team" and from 2000 to 2021 he was one of the most active committers to the R core. The package MASS is one of only fifteen "recommended packages" for R (with June 2024 more than 20,900). He was educated at the University of Cambridge, where he was awarded both the Smith's Prize (at the time awarded to the best graduate essay writer who had been undergraduate at Cambridge in that cohort) and the Rollo Davidson Prize. The university also awarded him the Adams Prize in 1987 for an essay entitled Statistical Inference for Spatial Processes, later published as a book. He served on the faculty of Imperial College, London from 1976 until 1983, at which point he moved to the University of Strathclyde. == Authored books == Ripley, B. D. (1981) Spatial Statistics. Wiley, 252pp. ISBN 0-471-08367-4. Ripley, B. D. (1983) Stochastic Simulation. Wiley, ISBN 0-471-81884-4. Ripley, B. D. (1988). Statistical Inference for Spatial Processes. Cambridge University Press. ISBN 0-521-35234-7. Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge University Press. 403 pages. ISBN 0-521-46086-7. Venables, W. N. and Ripley, B. D. (2000) S Programming. Springer, 264pp. ISBN 978-0-387-98966-2. Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S (Fourth Edition; previous editions published as Modern Applied Statistics with S-PLUS in 1994, 1997 & 1999). Springer, 462pp. ISBN 978-0-387-95457-8.
Brendan Frey
Brendan John Frey FRSC (born 29 August 1968) is a Canadian computer scientist, entrepreneur, and engineer. He is Founder and CEO of Deep Genomics, Cofounder of the Vector Institute for Artificial Intelligence and Professor of Engineering and Medicine at the University of Toronto. Frey is a pioneer in the development of machine learning and artificial intelligence methods, their use in accurately determining the consequences of genetic mutations, and in designing medications that can slow, stop or reverse the progression of disease. As far back as 1995, Frey co-invented one of the first deep learning methods, called the wake-sleep algorithm, the affinity propagation algorithm for clustering and data summarization, and the factor graph notation for probability models. In the late 1990s, Frey was a leading researcher in the areas of computer vision, speech recognition, and digital communications. == Education == Frey studied computer engineering and physics at the University of Calgary (BSc 1990) and the University of Manitoba (MSc 1993), and then studied neural networks and graphical models as a doctoral candidate at the University of Toronto under the supervision of Geoffrey Hinton (PhD 1997). He was an invited participant of the Machine Learning program at the Isaac Newton Institute for Mathematical Sciences in Cambridge, UK (1997) and was a Beckman Fellow at the University of Illinois at Urbana Champaign (1999). == Career == Following his undergraduate studies, Frey worked as a junior research scientist at Bell-Northern Research from 1990 to 1991. After completing his postdoctoral studies at the University of Illinois at Urbana-Champaign, Frey was an assistant professor in the Department of Computer Science at the University of Waterloo, from 1999 to 2001. In 2001, Frey joined the Department of Electrical and Computer Engineering at the University of Toronto and was cross-appointed to the Department of Computer Science, the Banting and Best Department of Medical Research and the Terrence Donnelly Centre for Cellular and Biomolecular Research. From 2008 to 2009, he was a visiting researcher at Microsoft Research (Cambridge, UK) and a visiting professor in the Cavendish Laboratories and Darwin College at Cambridge University. Between 2001 and 2014, Frey consulted for several groups at Microsoft Research and acted as a member of its Technical Advisory Board. In 2002, a personal crisis led Frey to face the fact that there was a tragic gap between our ability to measure a patient's mutations and our ability to understand and treat the consequences. Recognizing that biology is too complex for humans to understand, that in the decades to come there would be an exponential growth in biology data, and that machine learning is the best technology we have for discovering relationships in large datasets, Frey set out to build machine learning systems that could accurately predict genome and cell biology. Frey’s group pioneered much of the early work in the field and over the next 15 years published more papers in leading-edge journals than any other academic or industrial research lab. In 2015, Frey founded Deep Genomics, with the goal of building a company that can produce effective and safe genetic medicines more rapidly and with a higher rate of success than was previously possible. The company has received 240 million dollars in funding to date from leading Bay Area investors, including the backers of SpaceX and Tesla.
Connectionist expert system
Connectionist expert systems are artificial neural network (ANN) based expert systems where the ANN generates inferencing rules e.g., fuzzy-multi layer perceptron where linguistic and natural form of inputs are used. Apart from that, rough set theory may be used for encoding knowledge in the weights better and also genetic algorithms may be used to optimize the search solutions better. Symbolic reasoning methods may also be incorporated (see hybrid intelligent system). (Also see expert system, neural network, clinical decision support system.)
Brainware
Brainware was an American software company that marketed Automatic identification and data capture and data extraction products. The company was acquired by Hyland Software in 2017. Brainware originally spun out of Dulles, Virginia-based SER Solutions Inc. in February 2006 when SER was acquired by The Gores Group LLC. From February 2006 to March 2012, Brainware's majority owner was San Francisco-based private equity firm Vista Equity Partners. == History == On March 5, 2012, Lexmark International announced it had acquired the company for a cash price of approximately $148 million. The company was added to Lexmark's Perceptive Software division. On July 10, 2017, Hyland Software finalized its acquisition of the Perceptive Business Unit of Lexmark International, Inc. All enterprise software business assets in the Perceptive business unit, including Perceptive Content (formerly ImageNow), Perceptive Intelligent Capture (formerly Brainware), Acuo VNA, PACSGEAR, Claron, Nolij, Saperion, Pallas Athena, ISYS and Twistage, now operate under Hyland's portfolio of products. Brainware was headquartered in Ashburn, Virginia, USA, with sales, support, professional services and R&D offices in London, UK; Kirchzarten, Germany; and Neuchâtel, Switzerland. The company had partnerships with most major enterprise software providers, including Oracle, SAP and Microsoft, and said its software integrated with most available enterprise content management platforms. Brainware also partnered with a number of hardware providers, including Hewlett-Packard, Fujitsu and OPEX. Brainware's core solution, Distiller, "disrupted the data capture industry by using contextual document data to deliver higher automated processing than earlier technology" said Henry Ijams, Managing Director and Founder, PayStream Advisors. Brainware was awarded a Technology Excellence Award by PayStream Advisors and their Advisory Board to honor those providers who are delivering industry leading solutions. Brainware said its software "could relieve a company of 60 percent to 80 percent of the work of manually keying in information from unstructured documents," and serviced companies such as NEC, Mayo Clinic, Bechtel, Royal Dutch Shell, and Rabobank. In a 2011 comparison report, Real Story Group classifies Brainware as a "Capture Solutions" vendor, competing directly with Kofax and ReadSoft. Brainware and its customers were profiled in publications including Profit Online, Business Finance, imageSource, Managing Automation, Industryweek, Treasury & Risk and others. The company's enterprise search technology has been profiled by InfoWorld.