Evernote高层持续动荡，负责合作伙伴关系的副总裁也将离职
Evernote 现在正处于急剧的过渡与转型期，这从公司高层的持续动荡就能看出。消息称，Evernote 负责合作伙伴的副总裁 Alex Pachikov 将于明年1月 离职，随他离职的还有他手下的几名高管。不久前，公司的品牌营销副总裁 Andrew Sinkov 已经离职了。
5 个月前，Evernote 的 CEO 兼联合创始人 Phil Libin 也卸任了 CEO 一职。公司 CEO 由 Chris O ‘Neill 接任。上个月，还有消息称公司的 COO Linda Kozlowski 也将于今年年 底离职。
今年对于 Evernote 可谓是非常动荡的一年。除了公司高层的持续动荡外，公司还进行了系列裁员和产品调整，包括关闭了 3 个海外办事处、裁员 47 人、关闭 Evernote Food、Skitch、Clearly 应用服务。对于为何关闭一些应用服务，Evernote 表示是为了专注于公司的核心业务。
很长一段时间以来，Evernote 的业务可谓是停滞不前，饱受外界诟病，因此公司高层的离职和新高管的引入并不见得是一件坏事。接下来，公司 CEO O’ Neill 需要的是重新改造公司，高管重组应是其中的一个重要部分。Evernote 究竟会走向何处，Time will tell！
来源：36氪，作者：达达，如若转载，请注明出处：http://36kr.com/p/5041447.html
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2015年12月24日
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招聘文本分析创企Textio，获800万美元A轮融资来源：猎云网（编译：竹子）
Textio，一家分析特定情景中的词汇和语言的创企，今天宣布完成了由Emergence Capital领投的800万美元A轮融资。Cowboy Ventures、Bloomberg Beta和Upside Partnership也参与了此次融资。
Textio的第一款工具瞄准的是人才并购领域如招聘。创始人Snyder发现，某些特定的词汇和设计对应聘者更有吸引力，于是这些预测分析就被吸纳进了Textio的服务里。Textio可以分析一个公司的职务说明、绩效考核与其它备案，并判断这些文案是否可以为公司取得最佳效果。
该软件利用人工智能技术扫描招聘文本信息，然后向公司建议进行调整，以提高该公司吸引能力强的应聘者的机会。比如，有重点句的职位招聘总是比没有的更吸引求职者。Textio的软件还会建议各公司引进更多样化的应聘者。例如女性职场人士通常不会参与办公室内斗，或通常不会从事代码类的工作。
Textio成立于去年秋天，它的两位创始人 Kieran Snyder 和 Jensen Harris 分别是微软和亚马逊的前员工，Kieran Snyder之前致力于科技公司中性别歧视的研究，同时曾在微软、亚马逊任职语言学家，Jensen Harris曾在微软工作过16年。Textio创办后不到5个月，就在今年2月份拿到了150万美元的融资。
Textio主要以三个方面来衡量相关的科技短语：一是申请包含该短语岗位的应聘者人数；二是满足该词语要求技能的应聘者在全部应聘者中的百分比；第三是工作招聘发出后多久能够招到相关的人才。
虽然目前它的用处在于职位招聘，但很明显，这些技术可以被用到其他很多方面，比如邮件、简历以及其他各类信息。如果技术运行良好，理论上它可以为各类文档搭建分数库，这或许也是吸引投资人的地方。
Textio还有其他有价值的过人之处吗？大概就是它的客户了吧。目前使用Textio服务的企业有Twitter、Atlassian、Starbucks、Square和Microsoft等等。自然语言处理技术有广阔的应用领域，这又是对投资人的另一大强烈吸引。
Snyder表示，Textio目前可以识别出超过6万句短语词组，而这一数据还在持续增长。它会研究词汇是以何种方式组合在一起，比如词组中动词的密度和其他语法相关的特性。基于以上种种，最后给出评定分数。
当然，Textio也有不少潜在的竞争对手，诸如IBM Watson理论上也能分析文本并给出类似的结果。不过Snyder表示他们的优势在于专注的内容领域更具体。
Textio, A Startup That Analyzes Text Performance, Raises $8M
Textio CEO Kieran Snyder took a quantitative approach to how language worked in her linguistics studies. And when she and her co-founder Jensen Harris were leaving Microsoft to start a new company, it was only natural that it would be centered around language in some way.
That’s how Textio, a startup that analyzes text for how well words and phrases perform in certain scenarios, was born. The company today said it raised $8 million in a financing round led by Emergence Capital. Cowboy Ventures, Bloomberg Beta, and Upside Partnership also participated in the financing round.
“We had this premise that word processing in text hadn’t been disrupted in a while, from command line to GUI,” CEO Kieran Snyder said. “We had the internet come along, it was about social and sharing, and we think that AI and the set of related technologies is the next big disruptor of text. If you know the performance of a document before it’s ever published then you can fix it before it’s published.”
Textio’s first tool looks at talent acquisition documents — like job postings — to determine how well they will perform among candidates. Certain words and layouts attract more candidates than others, Snyder found, and those predictive analytics are baked into the service. For example, Textio shows that job postings with bullet points tend to perform better than job postings without them.
Right now it’s used for talent acquisition documents, but it’s pretty easy to see that the technology can be applied to documents that include common phrases — such as email, resumes, or other kinds of messages. If the technology works, it can theoretically begin building up scores for those kinds of documents, which is likely what attracted investors to the product and the team.
Another reason it might be so valuable to investors? Its customers. Already Textio is being used by companies like Twitter, Atlassian, Starbucks, Square and Microsoft. Natural Language Processing technology has very broad applications if done right, which makes it an attractive bet for many investors.
Textio recognizes more than 60,000 phrases with its predictive technology, Snyder said, and that data set is changing constantly as it continues to operate. It looks at how words are put together — such as how verb dense a phrase is — and at other syntax-related properties the document may have. All that put together results in a score for the document, based on how likely it is to succeed in whatever the writer set out to do.
Given who’s likely using Textio, it’s important that it feels easy to use — hence the highlighting and dropdown boxes rather than readouts. Snyder said, at its core, Textio can’t feel like a statistics tool, and that’s probably because the kinds of people using it might not always be NLP experts.
Of course, there are potential competitors in the space when it comes to natural language processing. There are tools like IBM Watson that can analyze text and, in theory, pull off a similar result. But Snyder says Textio’s results will be better because they are content-specific — like in the case of talent-acquisition documents.
Source:TC
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