14 research outputs found

    Beyond the Meta: Leveraging Game Design Parameters for Patch-Agnostic Esport Analytics

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    Esport games comprise a sizeable fraction of the global games market, and is the fastest growing segment in games. This has given rise to the domain of esports analytics, which uses telemetry data from games to inform players, coaches, broadcasters and other stakeholders. Compared to traditional sports, esport titles change rapidly, in terms of mechanics as well as rules. Due to these frequent changes to the parameters of the game, esport analytics models can have a short life-spam, a problem which is largely ignored within the literature. This paper extracts information from game design (i.e. patch notes) and utilises clustering techniques to propose a new form of character representation. As a case study, a neural network model is trained to predict the number of kills in a Dota 2 match utilising this novel character representation technique. The performance of this model is then evaluated against two distinct baselines, including conventional techniques. Not only did the model significantly outperform the baselines in terms of accuracy (85% AUC), but the model also maintains the accuracy in two newer iterations of the game that introduced one new character and a brand new character type. These changes introduced to the design of the game would typically break conventional techniques that are commonly used within the literature. Therefore, the proposed methodology for representing characters can increase the life-spam of machine learning models as well as contribute to a higher performance when compared to traditional techniques typically employed within the literature

    Geographic information extraction from texts

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    A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction

    Preface

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    Π’Π΅ΠΊΡ‚ΠΎΡ€Π½ΠΎΠ΅ прСдставлСниС слов с сСмантичСскими ΠΎΡ‚Π½ΠΎΡˆΠ΅Π½ΠΈΡΠΌΠΈ: ΡΠΊΡΠΏΠ΅Ρ€ΠΈΠΌΠ΅Π½Ρ‚Π°Π»ΡŒΠ½Ρ‹Π΅ наблюдСния

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    The ability to identify semantic relations between words has made a word2vec model widely used in NLP tasks. The idea of word2vec is based on a simple rule that a higher similarity can be reached if two words have a similar context. Each word can be represented as a vector, so the closest coordinates of vectors can be interpreted as similar words. It allows to establish semantic relations (synonymy, relations of hypernymy and hyponymy and other semantic relations) by applying an automatic extraction. The extraction of semantic relations by hand is considered as a time-consuming and biased task, requiring a large amount of time and some help of experts. Unfortunately, the word2vec model provides an associative list of words which does not consist of relative words only. In this paper, we show some additional criteria that may be applicable to solve this problem. Observations and experiments with well-known characteristics, such as word frequency, a position in an associative list, might be useful for improving results for the task of extraction of semantic relations for the Russian language by using word embedding. In the experiments, the word2vec model trained on the Flibusta and pairs from Wiktionary are used as examples with semantic relationships. Semantically related words are applicable to thesauri, ontologies and intelligent systems for natural language processing.Π’ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡ‚ΡŒ ΠΈΠ΄Π΅Π½Ρ‚ΠΈΡ„ΠΈΠΊΠ°Ρ†ΠΈΠΈ сСмантичСской близости ΠΌΠ΅ΠΆΠ΄Ρƒ словами сдСлала модСль word2vec ΡˆΠΈΡ€ΠΎΠΊΠΎ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΡƒΠ΅ΠΌΠΎΠΉ Π² NLP-Π·Π°Π΄Π°Ρ‡Π°Ρ…. ИдСя word2vec основана Π½Π° контСкстной близости слов. КаТдоС слово ΠΌΠΎΠΆΠ΅Ρ‚ Π±Ρ‹Ρ‚ΡŒ прСдставлСно Π² Π²ΠΈΠ΄Π΅ Π²Π΅ΠΊΡ‚ΠΎΡ€Π°, Π±Π»ΠΈΠ·ΠΊΠΈΠ΅ ΠΊΠΎΠΎΡ€Π΄ΠΈΠ½Π°Ρ‚Ρ‹ Π²Π΅ΠΊΡ‚ΠΎΡ€ΠΎΠ² ΠΌΠΎΠ³ΡƒΡ‚ Π±Ρ‹Ρ‚ΡŒ ΠΈΠ½Ρ‚Π΅Ρ€ΠΏΡ€Π΅Ρ‚ΠΈΡ€ΠΎΠ²Π°Π½Ρ‹ ΠΊΠ°ΠΊ Π±Π»ΠΈΠ·ΠΊΠΈΠ΅ ΠΏΠΎ смыслу слова. Π’Π°ΠΊΠΈΠΌ ΠΎΠ±Ρ€Π°Π·ΠΎΠΌ, ΠΈΠ·Π²Π»Π΅Ρ‡Π΅Π½ΠΈΠ΅ сСмантичСских ΠΎΡ‚Π½ΠΎΡˆΠ΅Π½ΠΈΠΉ (ΠΎΡ‚Π½ΠΎΡˆΠ΅Π½ΠΈΠ΅ синонимии, Ρ€ΠΎΠ΄ΠΎ-Π²ΠΈΠ΄ΠΎΠ²Ρ‹Π΅ ΠΎΡ‚Π½ΠΎΡˆΠ΅Π½ΠΈΡ ΠΈ Π΄Ρ€ΡƒΠ³ΠΈΠ΅) ΠΌΠΎΠΆΠ΅Ρ‚ Π±Ρ‹Ρ‚ΡŒ Π°Π²Ρ‚ΠΎΠΌΠ°Ρ‚ΠΈΠ·ΠΈΡ€ΠΎΠ²Π°Π½ΠΎ. УстановлСниС сСмантичСских ΠΎΡ‚Π½ΠΎΡˆΠ΅Π½ΠΈΠΉ Π²Ρ€ΡƒΡ‡Π½ΡƒΡŽ считаСтся Ρ‚Ρ€ΡƒΠ΄ΠΎΠ΅ΠΌΠΊΠΎΠΉ ΠΈ Π½Π΅ΠΎΠ±ΡŠΠ΅ΠΊΡ‚ΠΈΠ²Π½ΠΎΠΉ Π·Π°Π΄Π°Ρ‡Π΅ΠΉ, Ρ‚Ρ€Π΅Π±ΡƒΡŽΡ‰Π΅ΠΉ большого количСства Π²Ρ€Π΅ΠΌΠ΅Π½ΠΈ ΠΈ привлСчСния экспСртов. Но срСди ассоциативных слов, сформированных с использованиСм ΠΌΠΎΠ΄Π΅Π»ΠΈ word2vec, Π²ΡΡ‚Ρ€Π΅Ρ‡Π°ΡŽΡ‚ΡΡ слова, Π½Π΅ ΠΏΡ€Π΅Π΄ΡΡ‚Π°Π²Π»ΡΡŽΡ‰ΠΈΠ΅ Π½ΠΈΠΊΠ°ΠΊΠΈΡ… ΠΎΡ‚Π½ΠΎΡˆΠ΅Π½ΠΈΠΉ с Π³Π»Π°Π²Π½Ρ‹ΠΌ словом, для ΠΊΠΎΡ‚ΠΎΡ€ΠΎΠ³ΠΎ Π±Ρ‹Π» прСдставлСн ассоциативный ряд. Π’ Ρ€Π°Π±ΠΎΡ‚Π΅ Ρ€Π°ΡΡΠΌΠ°Ρ‚Ρ€ΠΈΠ²Π°ΡŽΡ‚ΡΡ Π΄ΠΎΠΏΠΎΠ»Π½ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Π΅ ΠΊΡ€ΠΈΡ‚Π΅Ρ€ΠΈΠΈ, ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Π΅ ΠΌΠΎΠ³ΡƒΡ‚ Π±Ρ‹Ρ‚ΡŒ ΠΏΡ€ΠΈΠΌΠ΅Π½ΠΈΠΌΡ‹ для Ρ€Π΅ΡˆΠ΅Π½ΠΈΡ Π΄Π°Π½Π½ΠΎΠΉ ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌΡ‹. НаблюдСния ΠΈ ΠΏΡ€ΠΎΠ²Π΅Π΄Π΅Π½Π½Ρ‹Π΅ экспСримСнты с общСизвСстными характСристиками, Ρ‚Π°ΠΊΠΈΠΌΠΈ ΠΊΠ°ΠΊ частота слов, позиция Π² ассоциативном ряду, ΠΌΠΎΠ³ΡƒΡ‚ Π±Ρ‹Ρ‚ΡŒ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Π½Ρ‹ для ΡƒΠ»ΡƒΡ‡ΡˆΠ΅Π½ΠΈΡ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΎΠ² ΠΏΡ€ΠΈ Ρ€Π°Π±ΠΎΡ‚Π΅ с Π²Π΅ΠΊΡ‚ΠΎΡ€Π½Ρ‹ΠΌ прСдставлСниСм слов Π² части опрСдСлСния сСмантичСских ΠΎΡ‚Π½ΠΎΡˆΠ΅Π½ΠΈΠΉ для русского языка. Π’ экспСримСнтах ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΡƒΠ΅Ρ‚ΡΡ обучСнная Π½Π° корпусах Ѐлибусты модСль word2vec ΠΈ Ρ€Π°Π·ΠΌΠ΅Ρ‡Π΅Π½Π½Ρ‹Π΅ Π΄Π°Π½Π½Ρ‹Π΅ Викисловаря Π² качСствС ΠΎΠ±Ρ€Π°Π·Ρ†ΠΎΠ²Ρ‹Ρ… ΠΏΡ€ΠΈΠΌΠ΅Ρ€ΠΎΠ², Π² ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Ρ… ΠΎΡ‚Ρ€Π°ΠΆΠ΅Π½Ρ‹ сСмантичСскиС ΠΎΡ‚Π½ΠΎΡˆΠ΅Π½ΠΈΡ. БСмантичСски связанныС слова (ΠΈΠ»ΠΈ Ρ‚Π΅Ρ€ΠΌΠΈΠ½Ρ‹) нашли своС ΠΏΡ€ΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ Π² тСзаурусах, онтологиях, ΠΈΠ½Ρ‚Π΅Π»Π»Π΅ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½Ρ‹Ρ… систСмах для ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ СстСствСнного языка

    Annual record no. 50

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    INHIGEO produces an annual publication that includes information on the commission's activities, national reports, book reviews, interviews and occasional historical articles.N

    Watset : automatic induction of synsets from a graph of synonyms

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    This paper presents a new graph-based approach that induces synsets using synonymy dictionaries and word embeddings. First, we build a weighted graph of synonyms extracted from commonly available resources, such as Wiktionary. Second, we apply word sense induction to deal with ambiguous words. Finally, we cluster the disambiguated version of the ambiguous input graph into synsets. Our meta-clustering approach lets us use an efficient hard clustering algorithm to perform a fuzzy clustering of the graph. Despite its simplicity, our approach shows excellent results, outperforming five competitive state-of-the-art methods in terms of F-score on three gold standard datasets for English and Russian derived from large-scale manually constructed lexical resources
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