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What else can I help you with?
Why was martin Luther king jr important in the 60s?
beacuse his speechand because he wants coulered peps to be treated the same as white people and for that he put his life on the line for what he stood for
What did Patrick Henry discover?
Patrick Henry did not discover anything in the traditional sense. He was a prominent figure in American history who is best known for his passionate speeches and role in the American Revolutionary War. He is particularly famous for his speech in 1775 in which he declared, "Give me liberty, or give me death!"
What does a glossary look like?
A glossary gives information just like a dictionary but little.It gives the part of speechand probably pictures of the word your looking up.A glossary is usually in the end of a childrens reading book.
When should a reader stop or pause when reading shakespeare?
We are speaking, I assume, of how to read Shakespeare's lines out loud, and especially those written in blank verse where a new line starts every ten syllables. It is important to remember that unless you are reading a sonnet or Venus and Adonis, you are reading lines from a play which a person has to say as a character. Therefore the reading of the lines should be as natural as possible and should be in character. This means that you should pause when sense demands it, not when you reach the end of a line. Consider this speech from Othello:Most potent, grave, and reverend signiors,My very noble and approved good masters,That I have ta'en away this old man's daughter,It is most true; true, I have married her:The very head and front of my offendingHath this extent, no more. Rude am I in my speechAnd little bless'd with the soft phrase of peace;There are in this speech four sentences, four units of thought. The very start of the speech is similar to the way we might start a speech with "Ladies and Gentlemen", and you wouldn't pause after saying that, so the first sentence is "Most potent, grave and reverend signiors, my very noble and approved good masters, that I have ta'en away this old man's daughter, it is most true." Pause. "True, I have married her." Another pause. "The very head and front of my offending hath this extent, no more." Pause again. "Rude am I in my speech and little bless'd with the soft phrase of peace." Bear in mind that this is a trial and Othello is building an argument for his defence. He speaks slowly and lets each point sink in. Yes he has gone off with Desdemona. He has done it by the book, having married her. That is all he has done. The last line is sort of like "Unaccustomed as I am to public speaking"; it's a throwaway and we move quickly onward to the next part of the speech. Because each sentence should be said in a breath, say long sentences more quickly and short ones slowly and forcefully.
What is the difference between TOEFL and IELTS?
You can compare the two:IELTS What happens in a test? The IELTS speaking test is one candidate and one examiner, who manages the test and evaluates the candidate at the same time. The test is separated into three parts. Each part takes about 4 minutes. In parts 1 and 2 the examiner uses a script, in part 2 a list of questions. In Part 1, the examiner asks the candidate some simple personal questions on everyday familiar topics. The examiner reads these questions from a script. Example topics are work, study, where you live, food, holidays, friends, going out, festivals, sports, schools and public transport. In Part 2, the examiner gives the candidate a topic on a card and the candidate needs to speak about it for about 2 minutes. Before speaking, the candidate has one minute to make notes. The task is to talk about a personal experience such as a memorable day or a significant person. This is followed by a quick question, which the candidate gives a short answer to. This provides some continuity for the transition to part 3. In Part 3, candidate and examiner will have a discussion relating to the subject area in Part 2. The candidate will be asked to do more complicated things, such as evaluate, justify positions and opinions, make predictions, and express preferences. The examiner has a list of questions but is not limited to these. He or she can respond freely to the candidate's answers, making this part of the test more like a normal conversation. How is the candidate evaluated? The examiner listens to the candidate as they do the test, and then evaluates their level by comparing the speaker's performance to descriptions. These say what a speaker can do in four areas. Levels go from 1 - 9. The four criteria are described below: Fluency and CoherenceThis means how good the candidate is at keeping talking at the right speed and how good they are at connecting their ideas together. This is a fairly general criteria which includes evaluating the relevance of the candidate's answers, but in terms of the elements we have identified in part 1 of this article, it refers to Speakers need to be able to understand and follow the rules of language at a word, sentence and text level. Lexical ResourceThis means how much vocabulary the candidate has and how well they use it. As well as the rules of language at a word level, this criteria considers the communicative functions of speechand the social meaning of speech.Grammatical Range and AccuracyThis means how many structures the candidate has and how well they use them. Again, as well as the rules of language, this criteria considers the communicative functions of speech.PronunciationThis means how well the candidate pronounces the language. As well as considering the communicative effect of the candidate's pronunciation, there is evaluation of how much strain it causes on a listener, and how noticeable their accent is - although accent itself is not a problem. In terms of the elements we have identified in part 1 of this article, this criteria refers to Speakers need to be able to produce the phonological features of speech.T TOEFLWhat happens in a test? The TOEFL speaking test is one candidate and a computer, which provides tasks for the candidate, records their answers and times them (with an on-screen clock). The recorded sample is evaluated later by a group of examiners. The test is separated into 6 tasks, two independent tasks (just the candidate speaking) and four integrated tasks (with the candidate integrating information from other sources, such as a written text or listening). The test takes about 20 minutes. In task 1, the candidate reads and listens to a short question based on a familiar topic. For example, the candidate could be asked to describe a class. In task 2, they are asked to choose between two options and explain why. In both questions, the candidate has 15 seconds to prepare an answer and needs to speak for 45 seconds. In part 2 of the speaking the four tasks are integrated with other skills. In task 3, the candidate reads a short text on a campus-related issue, then hears one or two students expressing opinions. The candidate then needs to summarise what the speakers have said. In task 4, the candidate reads about an academic subject, then hears a professor lecturing on the same subject. There is then a question based on both sources. In task 5 the candidate listens to a short conversation about a campus-related situation and then answers a question. This answer includes choosing between options and justifying this choice. The final task is to listen to a brief extract from a lecture and then explain a point with examples. How is the candidate evaluated? The candidate is recorded, and then at least three different examiners listen to this recording. They grade each of the six tasks on the recording separately against criteria in four areas. Levels go from 0 - 4, so there each band is broader than in an IELTS test. The criteria are below: Delivery This means how well the candidate uses pronunciation, rhythm, and intonation, and whether their rate of speech, pausing and fluency is appropriate. In terms of the elements we have identified in part 1 of this article, this criteria refers to Speakers need to be able to produce the phonological features of speech. Language Use This means how much vocabulary and how many structures the candidate has, and how well they use these two elements. As above, this includes the rules of language at a word level, the communicative functions of speech and the social meaning of speech.Topic Development This is a different kind of criteria because as well as evaluating the general cohesion and coherence of the candidate's answers (the rules of language at a word, sentence and text level and the communicative functions of speech), this criteria asks if the candidate has completed the task, which includes using the information they were given. In this way this criteria evaluates both language and content.
What advantages are the natural language processing?
The following is a list of some of the most commonly researched tasks in NLP. Note that some of these tasks have direct real-world applications, while others more commonly serve as subtasks that are used to aid in solving larger tasks. What distinguishes these tasks from other potential and actual NLP tasks is not only the volume of research devoted to them but the fact that for each one there is typically a well-defined problem setting, a standard metric for evaluating the task, standard corpora on which the task can be evaluated, and competitions devoted to the specific task.Automatic summarization: Produce a readable summary of a chunk of text. Often used to provide summaries of text of a known type, such as articles in the financial section of a newspaper.Coreference resolution: Given a sentence or larger chunk of text, determine which words ("mentions") refer to the same objects ("entities"). Anaphora resolution is a specific example of this task, and is specifically concerned with matching up pronouns with the nouns or names that they refer to. For example, in a sentence such as "He entered John's house through the front door", "the front door" is a referring expression and the bridging relationship to be identified is the fact that the door being referred to is the front door of John's house (rather than of some other structure that might also be referred to).Discourse analysis: This rubric includes a number of related tasks. One task is identifying the discoursestructure of connected text, i.e. the nature of the discourse relationships between sentences (e.g. elaboration, explanation, contrast). Another possible task is recognizing and classifying the speech actsin a chunk of text (e.g. yes-no question, content question, statement, assertion, etc.).Machine translation: Automatically translate text from one human language to another. This is one of the most difficult problems, and is a member of a class of problems colloquially termed "AI-complete", i.e. requiring all of the different types of knowledge that humans possess (grammar, semantics, facts about the real world, etc.) in order to solve properly.Morphological segmentation: Separate words into individual morphemes and identify the class of the morphemes. The difficulty of this task depends greatly on the complexity of the morphology(i.e. the structure of words) of the language being considered.English has fairly simple morphology, especially inflectional morphology, and thus it is often possible to ignore this task entirely and simply model all possible forms of a word (e.g. "open, opens, opened, opening") as separate words. In languages such as Turkish, however, such an approach is not possible, as each dictionary entry has thousands of possible word forms.Named entity recognition (NER): Given a stream of text, determine which items in the text map to proper names, such as people or places, and what the type of each such name is (e.g. person, location, organization). Note that, although capitalizationcan aid in recognizing named entities in languages such as English, this information cannot aid in determining the type of named entity, and in any case is often inaccurate or insufficient. For example, the first word of a sentence is also capitalized, and named entities often span several words, only some of which are capitalized. Furthermore, many other languages in non-Western scripts (e.g. ChineseorArabic) do not have any capitalization at all, and even languages with capitalization may not consistently use it to distinguish names. For example, Germancapitalizes all nouns, regardless of whether they refer to names, and French and Spanish do not capitalize names that serve as adjectives.Natural language generation: Convert information from computer databases into readable human language.Natural language understanding: Convert chunks of text into more formal representations such as first-order logic structures that are easier for computerprograms to manipulate. Natural language understanding involves the identification of the intended semantic from the multiple possible semantics which can be derived from a natural language expression which usually takes the form of organized notations of natural languages concepts. Introduction and creation of language metamodel and ontology are efficient however empirical solutions. An explicit formalization of natural languages semantics without confusions with implicit assumptions such as closed world assumption (CWA) vs. open world assumption, or subjective Yes/No vs. objective True/False is expected for the construction of a basis of semantics formalization.[4]Optical character recognition (OCR): Given an image representing printed text, determine the corresponding text.Part-of-speech tagging: Given a sentence, determine the part of speech for each word. Many words, especially common ones, can serve as multiple parts of speech. For example, "book" can be a noun ("the book on the table") or verb ("to book a flight"); "set" can be a noun, verb or adjective; and "out" can be any of at least five different parts of speech. Note that some languages have more such ambiguity than others. Languages with little inflectional morphology, such as English are particularly prone to such ambiguity.Chinese is prone to such ambiguity because it is a tonal language during verbalization. Such inflection is not readily conveyed via the entities employed within the orthography to convey intended meaning.Parsing: Determine the parse tree(grammatical analysis) of a given sentence. The grammar for natural languages is ambiguous and typical sentences have multiple possible analyses. In fact, perhaps surprisingly, for a typical sentence there may be thousands of potential parses (most of which will seem completely nonsensical to a human).Question answering: Given a human-language question, determine its answer. Typical questions have a specific right answer (such as "What is the capital of Canada?"), but sometimes open-ended questions are also considered (such as "What is the meaning of life?").Relationship extraction: Given a chunk of text, identify the relationships among named entities (e.g. who is the wife of whom).Sentence breaking (also known as sentence boundary disambiguation): Given a chunk of text, find the sentence boundaries. Sentence boundaries are often marked by periods or other punctuation marks, but these same characters can serve other purposes (e.g. marking abbreviations).Sentiment analysis: Extract subjective information usually from a set of documents, often using online reviews to determine "polarity" about specific objects. It is especially useful for identifying trends of public opinion in the social media, for the purpose of marketing.Speech recognition: Given a sound clip of a person or people speaking, determine the textual representation of the speech. This is the opposite of text to speech and is one of the extremely difficult problems colloquially termed "AI-complete" (see above). In natural speech there are hardly any pauses between successive words, and thus speech segmentation is a necessary subtask of speech recognition (see below). Note also that in most spoken languages, the sounds representing successive letters blend into each other in a process termed coarticulation, so the conversion of the analog signal to discrete characters can be a very difficult process.Speech segmentation: Given a sound clip of a person or people speaking, separate it into words. A subtask of speech recognitionand typically grouped with it.Topic segmentation and recognition: Given a chunk of text, separate it into segments each of which is devoted to a topic, and identify the topic of the segment.Word segmentation: Separate a chunk of continuous text into separate words. For a language like English, this is fairly trivial, since words are usually separated by spaces. However, some written languages like Chinese, Japanese and Thai do not mark word boundaries in such a fashion, and in those languages text segmentation is a significant task requiring knowledge of the vocabularyand morphology of words in the language.Word sense disambiguation: Many words have more than one meaning; we have to select the meaning which makes the most sense in context. For this problem, we are typically given a list of words and associated word senses, e.g. from a dictionary or from an online resource such as WordNet.In some cases, sets of related tasks are grouped into subfields of NLP that are often considered separately from NLP as a whole. Examples include:Information retrieval (IR): This is concerned with storing, searching and retrieving information. It is a separate field within computer science (closer to databases), but IR relies on some NLP methods (for example, stemming). Some current research and applications seek to bridge the gap between IR and NLP.Information extraction (IE): This is concerned in general with the extraction of semantic information from text. This covers tasks such as named entity recognition, Coreference resolution, relationship extraction, etc.Speech processing: This covers speech recognition, text-to-speechand related tasks.
