14 research outputs found
Beyond the Meta: Leveraging Game Design Parameters for Patch-Agnostic Esport Analytics
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
A large volume of unstructured texts, containing valuable geographic information, is available online. This information β provided implicitly or explicitly β 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
ΠΠ΅ΠΊΡΠΎΡΠ½ΠΎΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ ΡΠ»ΠΎΠ² Ρ ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΡΠΌΠΈ: ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΡΠ΅ Π½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΡ
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.ΠΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π±Π»ΠΈΠ·ΠΎΡΡΠΈ ΠΌΠ΅ΠΆΠ΄Ρ ΡΠ»ΠΎΠ²Π°ΠΌΠΈ ΡΠ΄Π΅Π»Π°Π»Π° ΠΌΠΎΠ΄Π΅Π»Ρ word2vec ΡΠΈΡΠΎΠΊΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΠΌΠΎΠΉ Π² NLP-Π·Π°Π΄Π°ΡΠ°Ρ
. ΠΠ΄Π΅Ρ word2vec ΠΎΡΠ½ΠΎΠ²Π°Π½Π° Π½Π° ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡΠ½ΠΎΠΉ Π±Π»ΠΈΠ·ΠΎΡΡΠΈ ΡΠ»ΠΎΠ². ΠΠ°ΠΆΠ΄ΠΎΠ΅ ΡΠ»ΠΎΠ²ΠΎ ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΎ Π² Π²ΠΈΠ΄Π΅ Π²Π΅ΠΊΡΠΎΡΠ°, Π±Π»ΠΈΠ·ΠΊΠΈΠ΅ ΠΊΠΎΠΎΡΠ΄ΠΈΠ½Π°ΡΡ Π²Π΅ΠΊΡΠΎΡΠΎΠ² ΠΌΠΎΠ³ΡΡ Π±ΡΡΡ ΠΈΠ½ΡΠ΅ΡΠΏΡΠ΅ΡΠΈΡΠΎΠ²Π°Π½Ρ ΠΊΠ°ΠΊ Π±Π»ΠΈΠ·ΠΊΠΈΠ΅ ΠΏΠΎ ΡΠΌΡΡΠ»Ρ ΡΠ»ΠΎΠ²Π°. Π’Π°ΠΊΠΈΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠΌ, ΠΈΠ·Π²Π»Π΅ΡΠ΅Π½ΠΈΠ΅ ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠΉ (ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠ΅ ΡΠΈΠ½ΠΎΠ½ΠΈΠΌΠΈΠΈ, ΡΠΎΠ΄ΠΎ-Π²ΠΈΠ΄ΠΎΠ²ΡΠ΅ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΡ ΠΈ Π΄ΡΡΠ³ΠΈΠ΅) ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½ΠΎ. Π£ΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΈΠ΅ ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠΉ Π²ΡΡΡΠ½ΡΡ ΡΡΠΈΡΠ°Π΅ΡΡΡ ΡΡΡΠ΄ΠΎΠ΅ΠΌΠΊΠΎΠΉ ΠΈ Π½Π΅ΠΎΠ±ΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΠΉ Π·Π°Π΄Π°ΡΠ΅ΠΉ, ΡΡΠ΅Π±ΡΡΡΠ΅ΠΉ Π±ΠΎΠ»ΡΡΠΎΠ³ΠΎ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π° Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ ΠΈ ΠΏΡΠΈΠ²Π»Π΅ΡΠ΅Π½ΠΈΡ ΡΠΊΡΠΏΠ΅ΡΡΠΎΠ². ΠΠΎ ΡΡΠ΅Π΄ΠΈ Π°ΡΡΠΎΡΠΈΠ°ΡΠΈΠ²Π½ΡΡ
ΡΠ»ΠΎΠ², ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΌΠΎΠ΄Π΅Π»ΠΈ word2vec, Π²ΡΡΡΠ΅ΡΠ°ΡΡΡΡ ΡΠ»ΠΎΠ²Π°, Π½Π΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΡΡΠΈΠ΅ Π½ΠΈΠΊΠ°ΠΊΠΈΡ
ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠΉ Ρ Π³Π»Π°Π²Π½ΡΠΌ ΡΠ»ΠΎΠ²ΠΎΠΌ, Π΄Π»Ρ ΠΊΠΎΡΠΎΡΠΎΠ³ΠΎ Π±ΡΠ» ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ Π°ΡΡΠΎΡΠΈΠ°ΡΠΈΠ²Π½ΡΠΉ ΡΡΠ΄. Π ΡΠ°Π±ΠΎΡΠ΅ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°ΡΡΡΡ Π΄ΠΎΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΠΊΡΠΈΡΠ΅ΡΠΈΠΈ, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΌΠΎΠ³ΡΡ Π±ΡΡΡ ΠΏΡΠΈΠΌΠ΅Π½ΠΈΠΌΡ Π΄Π»Ρ ΡΠ΅ΡΠ΅Π½ΠΈΡ Π΄Π°Π½Π½ΠΎΠΉ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ. ΠΠ°Π±Π»ΡΠ΄Π΅Π½ΠΈΡ ΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Π½ΡΠ΅ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΡ Ρ ΠΎΠ±ΡΠ΅ΠΈΠ·Π²Π΅ΡΡΠ½ΡΠΌΠΈ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠ°ΠΌΠΈ, ΡΠ°ΠΊΠΈΠΌΠΈ ΠΊΠ°ΠΊ ΡΠ°ΡΡΠΎΡΠ° ΡΠ»ΠΎΠ², ΠΏΠΎΠ·ΠΈΡΠΈΡ Π² Π°ΡΡΠΎΡΠΈΠ°ΡΠΈΠ²Π½ΠΎΠΌ ΡΡΠ΄Ρ, ΠΌΠΎΠ³ΡΡ Π±ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Ρ Π΄Π»Ρ ΡΠ»ΡΡΡΠ΅Π½ΠΈΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² ΠΏΡΠΈ ΡΠ°Π±ΠΎΡΠ΅ Ρ Π²Π΅ΠΊΡΠΎΡΠ½ΡΠΌ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ΠΌ ΡΠ»ΠΎΠ² Π² ΡΠ°ΡΡΠΈ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠΉ Π΄Π»Ρ ΡΡΡΡΠΊΠΎΠ³ΠΎ ΡΠ·ΡΠΊΠ°. Π ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Ρ
ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΡΡΡ ΠΎΠ±ΡΡΠ΅Π½Π½Π°Ρ Π½Π° ΠΊΠΎΡΠΏΡΡΠ°Ρ
Π€Π»ΠΈΠ±ΡΡΡΡ ΠΌΠΎΠ΄Π΅Π»Ρ word2vec ΠΈ ΡΠ°Π·ΠΌΠ΅ΡΠ΅Π½Π½ΡΠ΅ Π΄Π°Π½Π½ΡΠ΅ ΠΠΈΠΊΠΈΡΠ»ΠΎΠ²Π°ΡΡ Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΎΠ±ΡΠ°Π·ΡΠΎΠ²ΡΡ
ΠΏΡΠΈΠΌΠ΅ΡΠΎΠ², Π² ΠΊΠΎΡΠΎΡΡΡ
ΠΎΡΡΠ°ΠΆΠ΅Π½Ρ ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΡ. Π‘Π΅ΠΌΠ°Π½ΡΠΈΡΠ΅ΡΠΊΠΈ ΡΠ²ΡΠ·Π°Π½Π½ΡΠ΅ ΡΠ»ΠΎΠ²Π° (ΠΈΠ»ΠΈ ΡΠ΅ΡΠΌΠΈΠ½Ρ) Π½Π°ΡΠ»ΠΈ ΡΠ²ΠΎΠ΅ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ Π² ΡΠ΅Π·Π°ΡΡΡΡΠ°Ρ
, ΠΎΠ½ΡΠΎΠ»ΠΎΠ³ΠΈΡΡ
, ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΡΡ
ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
Π΄Π»Ρ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ Π΅ΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΡΠ·ΡΠΊΠ°
Annual record no. 50
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
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