There are several tasks where is preferable not responding than responding incorrectly. This idea is not new, but despite several previous attempts there isn’t a commonly accepted measure to assess non-response. We study here an extension of accuracy measure with this feature and a very easy to understand interpretation. The measure proposed (c@1) has a good balance of discrimination power, stability and sensitivity properties.
Variants of Naive Bayes (NB) and Support Vector Machines (SVM) are often used as baseline methods for text classiﬁcation, but their performance varies greatly depending on the model variant, features used and task/ dataset.
If we take an existing supervised NLP system, a simple and general way to improve accuracy is to use unsupervised word representations as extra word features. We evaluate Brown clusters, Collobert and Weston (2008) embeddings, and HLBL (Mnih & Hinton, 2009) embeddings of words on both NER and chunking. We use near state-of-the-art supervised baselines, and ﬁnd that each of the three word representations improves the accuracy of these baselines.
This paper describes a simple patternmatching algorithm for recovering empty nodes and identifying their co-indexed antecedents in phrase structure trees that do not contain this information. The patterns are minimal connected tree fragments containing an empty node and all other nodes co-indexed with it.
Hypertext Markup Language (HTML) was developed by Tim Berners-Lee in 19921 along with his
invention of Hypertext Transfer Protocol (HTTP). Together HTML and HTTP created the World Wide
Web. Berners-Lee adapted Standard Generalized Markup Language2 (SGML) tags for HTML, carrying
over some basic ones. HTML is used by browsers such as Internet Explorer and Firefox to format web
Curious about Google Sites and how team collaboration Web sites can help you share documents online from various locations? Curious about Google’s new Chrome browser? Google Sites & Chrome For Dummies has what you want to know!
Today, Google is so much more than another word for “search.” Google Sites & Chrome For Dummies shows you how to create great collaborative Web sites with Google Sites and surf the Web with the super-fast Google Chrome browser. Find out how they work with other Google Apps, too. You’ll learn to:...
Named entity (NE) recognition is a task in which proper nouns and numerical information in a document are detected and classiﬁed into categories such as person, organization, location, and date. NE recognition plays an essential role in information extraction systems and question answering systems. It is well known that hand-crafted systems with a large set of heuristic rules are difﬁcult to maintain, and corpus-based statistical approaches are expected to be more robust and require less human intervention. ...
A network is a connected collection of devices that can communicate with each other. Networks carry data in many kinds of environments, including homes, small businesses, and large enterprises. There are four major categories of physical components in a computer network: the computer, interconnections, switches, and routers. The major resources that are shared in a computer network include data and applications, peripherals, storage devices, and backup devices.
We propose a new, simple model for the automatic induction of selectional preferences, using corpus-based semantic similarity metrics. Focusing on the task of semantic role labeling, we compute selectional preferences for semantic roles. In evaluations the similarity-based model shows lower error rates than both Resnik’s WordNet-based model and the EM-based clustering model, but has coverage problems.
This paper presents an approach to identifying conjuncts of coordinate conjunctions appearing in text which has been labelled with syntactic and semantic tags. The overall project of which this research is a part is also briefly discussed. The program was tested on a 10,000 word chapter of the Merck Veterinary Manual. The algorithm is deterministic and domain independent and it performs relatively well on a large real-life domain. Constructs not handled by the simple algorithm are also described in some detail. ...
This paper investigates two elements of Maximum Entropy tagging: the use of a correction feature in the Generalised Iterative Scaling (Gis) estimation algorithm, and techniques for model smoothing. We show analytically and empirically that the correction feature, assumed to be required for the correctness of GIS, is unnecessary. We also explore the use of a Gaussian prior and a simple cutoff for smoothing. The experiments are performed with two tagsets: the standard Penn Treebank POS tagset and the larger set of lexical types from Combinatory Categorial Grammar. ...
This paper describes some operational aspects of a language comprehension model which unifies the linguistic theory and the semantic theory in respect to operations. The computational model, called Augmented Dependency Grammar (ADG), formulates not only the linguistic dependency structure of sentences but also the semantic dependency structure using the extended deep case grammar and fleld-oriented fact-knowledge based inferences. Fact knowledge base and ADG model clarify the qualitative difference between what we call semantics and logical meaning. ...
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Origami is the art of paper folding (origami or art) originating from Japan.
The Japanese word origami comes from two words: oru is folded or put a paper and kami. Origami is used only from 1880; before, the Japanese use the word orikata.
How to fold origami combine to make a simple rectangular piece of paper (2 pm), which is usually square, into the complex (3 pm), no cut and paste in the process of folding, this is the trend of modern origami.
The letter shown on the next page is from a prrivate in denmark to a company in the UK.It shows some of the features of a simple busibess the page.Sender's address in corresspndence that does not have a printed on the top righ - hand side of the page.In the UK , in contrast to the practice in some coutries, it is not usual to write the sender's name before the sender's address
Present continuous and present simple
Unit 3. Present continuous and present simple (1) A Hãy nghiên cứu các lời giải thích và so sánh các ví dụ sau: Present continuous (I am doing) Hãy dùng thì Present Continuous để diễn tả những sự việc xảy ra ngay lúc ta nói hay xung quanh thời điểm đó, và hành động chưa chấm dứt. The water is boiling. Can you turn it off? (Nước đang sôi. Bạn có thể tắt bếp được không) Listen to those people. What language are they speaking? (Hãy nghe những người kia.
Family and Friends Starter gives young learners a solid foundation in English. With a carefully graded reading and writing syllabus, accompanied by a clear phonics programme, the course takes learners from recognizing and tracing letters to writing and reading simple sentences with confidence. Inviting you to refer part 1 of ebook.
What are the keys to success? Scientists have studied the traits, beliefs, and practices of successful people in all walks of life. But the answers they find wind up in stuffy academic journals aimed at other scientists.
The 100 Simple Secrets of Successful People takes the best and most important research results from over a thousand studies and spells out the key findings in ways we can all understand. Each entry contains advice based on those findings, a real life example of what to do or not to do, and a telling statistic based on scientific researc...
"There is no more powerful way to prove that we know something well than to draw a simple picture of it. And there is no more powerful way to see hidden solutions than to pick up a pen and draw out the pieces of our problem."
So writes Dan Roam in The Back of the Napkin, the international bestseller that proves that a simple drawing on a humble napkin can be more powerful than the slickest PowerPoint presentation. Drawing on twenty years of experience and the latest discoveries in vision science, Roam teaches readers how to clarify any problem or sell...