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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在线教育网站Jigsaw Academy获得300万美元A轮融资
Jigsaw Academy 成立于 2011 年，总部位于印度。公司为一家在线教育网站，致力于为用户提供关于大数据处理，数据挖掘以及数据分析等课程。据公司负责人称， Jigsaw Academy 已经为全球 30多个国家，超过40000 名学生提供课程服务，课程主要包括零售，电商，人力资源。
本轮领投的公司为同样为用户提供课程测试和教育的Manipal Global Education Service (MaGE)公司，此外Mohandas Pai，MaGE CEO以及数位投资人也加入此次的融资。本轮融资的金额将会用于更多的课程开发，同时也将会利用 领投公司MaGE 在全球市场的优势拓展市场。
来源：vccircle.com
美国招聘网站Phenom People获得600万美元A轮融资
Phenom People成立于，总部位于美国霍舍姆。
该公司为一个招聘网站平台，他们通过传统的指纹和IP来收集用户的信息并且智能的匹配用户和工作的相关度。
本次领投的公司是Sierra Ventures。
Phenom People, A Customizable Job Application Service, Raises $6M
It can seem at times that job application sites for companies are a black hole and impossible to navigate. That’s why Phenom People, a service that customizes company job search pages for individuals, wants to try to fix that experience.
The company, which said it raised $6 million in venture financing led by Sierra Ventures, basically keeps tabs on who is visiting a company’s application page — like what jobs they view and where they come in from — and builds custom job search pages for them. For example, if someone has viewed a couple of jobs for a marketing role, they might be shown other roles that they might not have thought of when they visit a second time.
“We collect [a lot of data] to understand candidate intent — why is this here, what is it looking for,” CEO Mahe Bayireddi said. “So every time they come back we give a unique experience based on previous behavior, intent and persona. It’s all so we can give them a unique employment value proposition based on what they’re looking for.”
Recruiters also use the service to find potential recruits that might be good fits, which isn’t necessarily obvious if recruiters are just paying attention to the applications coming in. If a potential recruit is coming back three or four times and browsing the site, it may show that they are more interested in the job than someone just simply applying for jobs point-blank, Bayireddi said.
Phenom People uses traditional fingerprinting and IP tools in order to understand who that person is and collect data on them. It then builds an interest graph of sorts for that person, customizing the page for a company’s application site on the spot.
Part of the justification for a service like this to exist, Bayireddi said, is that while there are other services for searching jobs like LinkedIn, many inevitably end up back on the company’s home job search page. They might not actually apply for a job there, but might be coming back to the site regularly from other kinds of job search services, which offers additional data.
“The person can come from Google, look for a job, leave, and after 15 minutes come in from LinkedIn, and then leave, and then come in from Indeed,” he said. “People think it’s just from Indeed, but no, it’s multiple steps. They might even visit GlassDoor. Every track is basically managed by us to understand where they are coming from.”
So how is this different from a service like Greenhouse, or other job application services? Those services are simply backend systems that serve as a supply chain of sorts for recruiters, Bayireddi said. That, of course, doesn’t mean that they will always be that — if the market for something like this is as large as Bayireddi believes, it’s certainly in the realm of possibility that a company like other recruiting startups would begin doing something similar. And of course, there are ways to apply directly through LinkedIn, which might render a service like this moot.
This is Bayireddi’s first round of financing, but the company has been around for around four years, teaching him to basically run a lean an operation as a company can get. And that’s part of the reason the startup is going to remain in Philadelphia, as well, he said.
“That’s part of the unique nature of how we did it,” he said. “We primarily lean toward less costs, that’s a good attribution being in Philadelphia. We have no intention to move to Silicon Valley. Some of the funds asked us if you can move to Silicon Valley, our thought process is, ‘hey, we want to sit here because we want to build a community around here,’ and that’s important.”
来源：techcrunch.com
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