81aec284616a95a07d5ee84f]
Cost: 11017
Pretty fun build as well, but you won't get this many items in a 30 to 40 minute game. If you can farm that much, give yourself a pat on the back. This is usually my build for a long game because of the added survivability.
Tank
[builder=Garen/f14e3022aa193077e4b3d65ef6fddeb4]
Cost: 8780
This base build is extremely cheap and powerful. You can put out a lot of damage and still take quite a punishment. This is my usual build every game now, but sometimes I pick DPS when my team needs one. You can add more dps after this if your team needs it; Youmuu's Ghostblade always works.
[builder=Garen/fd22ce03903a589c03d7b837c1ff6659]
Cost: 8560
This is a rare build when we don't have a tank. You can substitute Force of Nature and Guardian Angel if you are playing against more magic users..
About the Guide
-Separated the builds into different topics
-Added build examples
-Adding Videos Later
I could use lot's of feedback. What works for you? I want to hear it! Feel free to add me in game!
Shout Outs
svs_pt for some item build help.
Boscogn for all his item suggestions and feedback. Thanks a bunch Bosc!
Build Guide?
Submitted by Godsend 23
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- 45430Home Entertainment Edouard Mendy’s unusual mistake in Benzema’s third goal in Chelsea-Real Madrid
Chelsea and Real Madrid starred in the first leg of the Champions League quarterfinals. In the stadium Stamford Bridgethe Spanish team won 3-1 with three goals from Karim Benzema; Kai Havertz converted for the English.
The third goal of The Blues had the goalkeeper Edouard Mendy as main protagonist, after a serious mistake. The goalkeeper received a defensive rejection of the visit almost in the middle of the field and in front of Benzema’s stalking he gave a faulty pass.
the french striker continued in search of the ball and his pressure against Antonio Rüdiger had an effect because he kept the rebound and converted with the bow at his mercy.
Karim Benzema took advantage of the errors of goalkeeper Edouard Mendy and defender Antonio Rudiger and scored the third of his personal account in Real Madrid’s victory against Chelsea (Reuters / Peter Cziborra)
Benzema opened the scoring with a header at 21 and repeated with another header at 24before closing his hat trick to the minute of the complement with the aforementioned Mendy blooper. Kai Havertz, also with a header, closed the gap at 24 of the first half to maintain the hope of the reigning European champion. In spite of this, Real Madrid’s 3-1 allows it to face with an important advantage the turn the next week in the Santiago Bernabeu.
Although Chelsea started out dominating the ball against a Real Madrid team that pressed trying to recover the ball early to go on the attack, they were met by the orderly defense of Real Madrid. The English team also had problems in their pressure, too slow, which favored Merengue, who soon found the goal.
A new arrival from Vinicius on the left ended with a cross into the area, where Benzema appeared to finish off a header to the nets of Edouard Mendy (21). Just three minutes later, the French extended the merengue account with another header in the race from a pass from Luka Modric (24).
Behind on the scoreboard, the English team did not lower their arms and rushed forward in search of goal, but it was difficult for them to find the gaps in the compact white defense. Thomas Tuchel’s team managed to close the gap when Havertz headed a cross into the box almost from close range (40).
As soon as he came back from the break, Benzema took advantage of a serious error by goalkeeper Mendy who had left his area and, under pressure from the Frenchman, gave Antonio Rudiger a poor performance. Benzema cut the pass to go towards the goal and score on an empty goal to make it 3-1 (46), which made the game very difficult for the locals.
The English team began to create more problems with the entry of striker Romelo Lukaku for Christian Pulisic (64). In the final minutes, Real Madrid began to feel tired, backing down and facilitating local arrivals, although they managed to hold on to the result.
Fierce criticism from Ruggeri to Battaglia: “He is not prepared to lead Boca, he has to call Bianchi and Gallardo”
Previous articleWhat are the most commented games on Twitter? This is the Top 10 of the first quarter of 2022SocialNLP 2018 EmotionX Challenge Overview: Recognizing Emotions in Dialogues

This paper describes an overview of the Dialogue Emotion Recognition Challenge, EmotionX, at the Sixth SocialNLP Workshop, which recognizes the emotion of each utterance in dialogues. This challenge offers the EmotionLines dataset as the experimental materials. The EmotionLines dataset contains conversations from Friends TV show transcripts (Friends) and real chatting logs (EmotionPush), where every dialogue utterance is labeled with emotions. Organizers provide baseline results. 18 teams registered in this challenge and 5 of them submitted their results successfully. The best team achieves the unweighted accuracy 62.48 and 62.5 on EmotionPush and Friends, respectively. In this paper we present the task definition, test collection, the evaluation results of the groups that participated in this challenge, and their approach.


Introduction
Human emotion underlays in our daily interactions with other people, and study from Ekman(1987) shows that emotion is a universal phenomena across different cultures. An emotion detection system can improve mutual understanding between individuals by providing undetected emotion signal. For a common sense of human perception that emotion is inherently multi-modality including vision and speech, multi-modal emotion recognition plays an important role in emotion detection area (Sebe et al.;Kessous et al., 2010;Haq and Jackson, 2011). At the same time, studies in uni-modal emotion recognition also contribute in variety of modalities like vision (Ekman and Friesen, 2003), speech (Nwe et al., 2003) and text (Alm et al., 2005).

Chandler
Matthew Perry talking about signs in Las Vegas. (Neutral) Chandler I guess it must've been some movie I saw.

Rachel
Oh okay, I'll fix that to. What's her e-mail address? (Neutral) Ross Rachel! (Anger)

Rachel
All right, I promise. I'll fix this. I swear. I'll-I'll-I'll-I'll talk to her. (Non-neutral)

Ross
Okay! (Anger) Rachel Okay. (Neutral) However, with the progress of social media and dialogue systems, especially the online customer services, textual emotion recognition has attracted more attention. In the social media, the hashtag and emoji are widely used and could provide substantial emotion clues (Qadir and Riloff, 2014;Kralj Novak et al., 2015). For the dialogue systems, instant emotion detection could help costumer service notice dissatisfaction of clients. Still, textual emotion recognition needs further exploration in dialogue systems for many reasons. For instance, a text segment can express various emotions given different context. Take the dialogue from Hsu et al.(2018) in Table 1 as an example, Okay! could be joy or anger in different scenarios. One more reason is that informal language and short sentence are everywhere in daily conversation. For instance, lol actually means laugh out loud. Therefore, emotion flow modeling and informal language understanding are essential for improving dialogue emotion recognition system.
For EmotionX shared task in SocialNLP 2018, we select an emotional dialogue dataset, Emo-

EmotionLines Dataset
EmotionLines is collected from two sources: Friends TV show transcripts (Friends) and Facebook messenger logs (EmotionPush). Dialogues are randomly selected from the raw data in four buckets of dialogue length [4-9], [10-14], [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29], and [20-24], with 250 dialogues for each bucket. However, EmotionPush is a private chat log and releasing it may encounter privacy issues. To cope with this problem, Stanford Named Entity Recognizer ) was adopted to replace the named entities in the corpus. In (Hsu et al., 2018), Amazon Mechanical Turk is utilized to label the emotion of every utterance. Following Ekman's(1987) six basic emotions and with neutral added, seven emotions are available for annotators in the labeling interface. To eliminate diverse emotion-labeled utterances, the utterance annotated with more than two emotions is considered as the non-neutral utter