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Natural Language Processing Jobs

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Hourly - Intermediate ($$) - Est. Time: 1 to 3 months, Less than 10 hrs/week - Posted
Looking for someone familiar with ShrapNLP and how to customize Models for it. This project is about aligning syntactic representations to an Ontology Question/Answer application using natural language processing and natural language generation.
Skills: Natural language processing C#
Fixed-Price - Expert ($$$) - Est. Budget: $1,000 - Posted
the project is a java software it read text files CSV and output CSV it use one library for text categorisation/classification feature, the library read a sentence and return its probable category, it need a training process before to be able to recognize categories correctly We need a Java developer experienced with machine learning based toolkit for processing of natural language text Please give us some examples of work you have do with this kind of library Project is composed of the following modules Module google-query - query google with a text ( the list of texts query are provided in text file) - parse google first 100 results - load all 100 results pages Module Analyse-Page ( analyse each page loaded by module google-query ) - with jsoup split the page into paragraph text - analyse each paragraph with the Paragraph-Analyser - analyse all the paragraph results with the Page-Paragraphs-Analyser Module Text entity recognition - use http://alias-i.com/lingpipe/demos/tutorial/ne/read-me.html or http://opennlp.apache.org/documentation/1.6.0/manual/opennlp.html#tools.namefind or better - we provide list of street name, people name, cities names, and some items-categories/groups lists to train model - In the text paragraph , All entity recognized should be replaced by their entity name Module Paragraph-Analyser trainer - use http://alias-i.com/lingpipe/demos/tutorial/classify/read-me.html or http://opennlp.apache.org/documentation/1.6.0/manual/opennlp.html#tools.doccat or better - We provide a list of 1000 paragraph texts with the category/classification - the trainer will build a model from the 1000 sample to use by Module Paragraph-Analyser Module Paragraph-Analyser - take a paragraph text as input and give a probable detected category/classification - use library http://opennlp.apache.org/index.html or http://alias-i.com/lingpipe/index.html or better Module Page-Paragraphs-Analyser trainer - We provide 1000 pages URL and the category/classification of each pages-url - we need to load the url and to process it with Module Paragraph-Analyser - the Page-Paragraphs-Analyser receive the Paragraph-Analyser result and is trained to match the provided category/classification - use http://alias-i.com/lingpipe/demos/tutorial/classify/read-me.html or http://opennlp.apache.org/documentation/1.6.0/manual/opennlp.html#tools.doccat or better Module Page-Paragraphs-Analyser - receive category/classification of all paragraphs of the page, then it concatenate all into a single text string - give a probable global category/classification - as input is a text string composed of multiple words we use the same library : http://opennlp.apache.org/index.html or http://alias-i.com/lingpipe/index.html or better note: We call a page : a HTML page loaded from a URL We call a paragraph : a text inside a HTML-Block-Tag like <DIV>, <TD>, <H1> all others tags like <A>,<P>,<B>,<SPAN> are stripped out from the block , only text remain We call a category/classification : a simple text-name (color, city, etc..) We call a Text entity : a text (example green, dark red, yellow) which have a entity name (example colors)
Skills: Natural language processing Artificial Neural Networks
Hourly - Expert ($$$) - Est. Time: Less than 1 week, Less than 10 hrs/week - Posted
I am a journalist and would like to create an interesting infographic about "Mom blogs" in Germany/Austria. I have a list of 1,700 URLs from those mom blogs and would like to analyze them. Topics to be analyzed could be: - What CMS are they using? - What are they talking about on the front page (first 10 pages linked from the homepage) (using natural language processing for German)? - ... + other things you come up with! Your goal would be to create a spider + scraper, data extraction tool, analysis of the data and create a quick summary within in an Excel Sheet. The summary should allow a designer to create an infographic containing interesting facts.
Skills: Natural language processing Data Analytics Web scraping
Fixed-Price - Expert ($$$) - Est. Budget: $800 - Posted
We need an NLP expert who can help us with creating a synonyms dictionary which can be used locally in the system. the input and output formats for the dictionary system could be de discussed later. Bid only if you have relevant experience in NLP.
  • Number of freelancers needed: 3
Skills: Natural language processing
Fixed-Price - Expert ($$$) - Est. Budget: $3,500 - Posted
We are looking for a data scientist, specializing in natural language processing and machine learning to closely work with our Data Science team in building next gen text analytics applications. We have already built quite a few use cases revolving around NLP, we are looking to scale quickly now. The person would be working in the area of NLPand ML for atleast 3 years and proficient in Python and open source modules like OpenNLP, Mallet, CoreNLP, GATE etc. Knowledge of RDF and Graph theory would be desired. Over a period of 6 months, you would be involved in building several machine learning models, algorithms etc. Broadly, the requirements would look something like this; • Building intelligent systems leveraging the vast amount of available user/behavior data to enrich the end user experience • Use of cutting edge data mining, machine learning, deep learning techniques for building advanced recommender systems and matching algorithms. • Research on innovative technologies to solve text mining, natural language processing, knowledge management and information retrieval problems. • Use techniques from artificial intelligence/machine learning to solve supervised and unsupervised learning problems. • Design solutions for complex business problems related to BIG Data by using NLP/Machine Learning/Text Mining techniques. • Analyze human behavior, perform sentiment analysis, solve complex problems related to Text Mining, do research on Image Processing Workload per month would be around 80-120 hours.
Skills: Natural language processing Data Modeling Data Science Machine learning
Fixed-Price - Expert ($$$) - Est. Budget: $100 - Posted
The specifications should contain: 1. Uses 2. Overall program architecture (Neural Networks, RBM, RNTN, DBN, etc.) 3. Programming language(s) to be used (Python is preferred) 4. Guidelines for design 5. Hardware Requirements (Amazon AWS GPU's, etc) 6. Team structure with and roles / duties. Envisioned uses include: 1. Accessing ERP and making decisions based on existing data and objectives. 2. Setting off warnings if certain parameters are reached. 3. Automated sales and customer service correspondence. 4. Automated sales and customer service correspondence. 5. Data mining. 6. Reacting to the results of data mining. 7. Organizing and prioritizing task and time. 8. Reorganizing production schedules based on client requirements. The scope can be expanded or reduced based on the conversation. Every effort should be made to use existing code and resources to minimize development time (https://www.tensorflow.org/, http://openai.sourceforge.net/, http://wiki.opencog.org/) The goal is to have the AI be highly functional. If it can have artificial general intelligence and be friendly, that would be preferred but is not required. A natural language processing system that is already developed and available and trained is preferred. No budget has been established for this project. All proposals are evaluated on pricing and qualifications to determine the best value.
Skills: Natural language processing Artificial Intelligence Artificial Neural Networks Data mining
Fixed-Price - Expert ($$$) - Est. Budget: $1,500 - Posted
Characters (actors) in movies often use slang in their dialogues, think of Juno or Pulp Fiction. You will build an automatic classifier that can automatically detect whether a given dialogue in an (English) movie script contains slang words or phrases. Slang words are words that are not part of the standard vocabulary but are normally used in an informal context, and often associated with a certain part of society. You are allowed to use any frameworks or external resources as you see fit, as long as these frameworks and/or resources can be used free of charge in a commercial project. Upon start of this project you will receive the annotated script of Juno where the dialogues have been extracted and have been annotated as slang/no slang. We expect your classifier to achieve at least a 65% F1-measure, but higher accuracy would of course be appreciated. Additionally, if the algorithm classifies a certain dialogue as slang, you need to output the words that are most likely slang. To complete this project successfully you will need experience in natural language processing and machine learning. The code needs to be written in python 3 and the needs to be finished AT THE LATEST january 23, 2016.
Skills: Natural language processing Machine learning Python
Fixed-Price - Expert ($$$) - Est. Budget: $5,000 - Posted
I'm a NYC-based radiologist hoping to improve patient understanding/engaging with radiology reports using natural language processing. Project Goals Patient-portals that allow patient's access to their medical information is on a steep rise. And, the #2 most common thing patient's look up and want access to is their radiology reports (#1 is laboratory data). The problem is that current surveys reveal that patient's are greatly unsatisfied by radiology reports due to their complex medical jardon, polysyllabic compound words, obscure anatomical references, etc. - they want to understand but are discouraged by the language. I think we can change that. Specifics of use for NLP We can use NLP to convert/translate complex radiology reports into much more understandable yet completely accurate/informative patient-centered reports. We can rewrite them in lay language and link certain words to additional information and anatomical maps. We can use NLP to fully engage the patient and involve them in their care by helping them understand the radiology report. One group at UPenn recently demonstrated proof of concept: http://siim.org/?page=15ab_porter. I think there's incredible potential for this technique to change how patient's engage with their medical data. Deliverables 1) Input: Input a standard/complex radiology report. 2) Run the report through our NLP algorithms 3) Output: A completely translated report written in lay terms (6th or 8th-grade reading level) with hyperlinks to additional information and anatomical maps. To start, I would like to process, for example 1000 brain MRIs or 1000 abdomen/pelvis CTs, etc. (of which I can provide) to determine the frequency of the most commonly used words and phrases for each given study, and from there we can make a "dictionary" of those words in layman terms, which could than be substituted in to improve patient understanding and engagement... I'd like to start with one exam type (i.e. Brain MRI) to prove proof of concept. If you have any interest, please feel free to contact me. Chris
Skills: Natural language processing Machine Design Machine learning Python
Hourly - Expert ($$$) - Est. Time: 1 to 3 months, 10-30 hrs/week - Posted
We are looking for veterans in cognitive computing space who have created self learning systems which involves the use data mining, pattern recognition and natural language processing to mimic the way the human brain works. Project is based on Semantic Knowledge base & Contextual Analysis. Apply if & only if you have worked on the above.
  • Number of freelancers needed: 2
Skills: Natural language processing Analytics Big Data Data mining
Hourly - Expert ($$$) - Est. Time: Less than 1 month, 10-30 hrs/week - Posted
Our team is currently seeking talented, driven and self-starting data scientist to work on a unique and challenging project that leverages the latest in data science and big data to address specific use cases in the Financial Services industry. You, the ideal candidate, will have a passion for applying the latest in statistical analysis, machine learning, and predictive analytics to derive unprecedented level of insight from extremely large datasets. This is an opportunity in which you will be able to architect and possibly implement a big data solution from concept to completion. You will be responsible for directing and driving the design and delivery of the product, so we’re looking for someone comfortable and confident enough to do so. At this stage, we’re evaluating potential candidates for this role. We anticipate one or two follow-up discussions with a short-list of candidates where we’ll provide more details on the project specifics. From there, we’d expect you to provide your proposal, and after our review we’ll choose the best one. What you’ll do - You will do cutting-edge work in several areas including: streaming data processing, NLP, machine learning and predictive analytics to solve a business problem at which legacy solutions increasingly fail to solve. -You will have freedom and responsibility end-to-end from technology stack and feature selection to designing algorithms and implementing them at scale. -You will be able to exercise your creative analysis skills to explore disparate datasets and extract valuable insights. -Your efforts will have a direct impact on business revenue and costs. What we prefer in you - The ability to absorb the business problems and articulate a solutions strategy in clear terms. - Knowledge of a wide cross-section of algorithms and models, and deep comprehension the strengths and weaknesses of each. - Strong familiarity with Hadoop, Hive and / or MapReduce (or similar). - The ability to balance engineering requirements in a deadline-driven environment. - BS or MS in data science, computer science, statistics or mathematics is required. - Commercial experience in data science is desired.
Skills: Natural language processing Apache Hive Big Data Data Analytics
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