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Word Alignment (linguistics)
Bitext word alignment or simply word alignment is the natural language processing task of identifying translation relationships among the words (or more rarely multiword units) in a bitext, resulting in a bipartite graph between the two sides of the bitext, with an arc between two words if and only if they are translations of one another. Word alignment is typically done after sentence alignment has already identified pairs of sentences that are translations of one another. Bitext word alignment is an important supporting task for most methods of statistical machine translation. The parameters of statistical machine translation models are typically estimated by observing word-aligned bitexts, and conversely automatic word alignment is typically done by choosing that alignment which best fits a statistical machine translation model. Circular application of these two ideas results in an instance of the expectation-maximization algorithm. This approach to training is an instance ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Word Alignment
Word alignment may refer to: * Word alignment (linguistics) Bitext word alignment or simply word alignment is the natural language processing task of identifying translation relationships among the words (or more rarely multiword units) in a bitext, resulting in a bipartite graph between the two sides of ... * Word alignment (computing) See also * Word boundary (other) {{disamb ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Part-of-speech
In grammar, a part of speech or part-of-speech (abbreviated as POS or PoS, also known as word class or grammatical category) is a category of words (or, more generally, of lexical items) that have similar grammatical properties. Words that are assigned to the same part of speech generally display similar syntactic behavior (they play similar roles within the grammatical structure of sentences), sometimes similar morphological behavior in that they undergo inflection for similar properties and even similar semantic behavior. Commonly listed English parts of speech are noun, verb, adjective, adverb, pronoun, preposition, conjunction, interjection, numeral, article, and determiner. Other terms than ''part of speech''—particularly in modern linguistic classifications, which often make more precise distinctions than the traditional scheme does—include word class, lexical class, and lexical category. Some authors restrict the term ''lexical category'' to refer only to a particu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Hidden Markov Model
A hidden Markov model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process — call it X — with unobservable ("''hidden''") states. As part of the definition, HMM requires that there be an observable process Y whose outcomes are "influenced" by the outcomes of X in a known way. Since X cannot be observed directly, the goal is to learn about X by observing Y. HMM has an additional requirement that the outcome of Y at time t=t_0 must be "influenced" exclusively by the outcome of X at t=t_0 and that the outcomes of X and Y at t handwriting recognition, handwriting, gesture recognition, part-of-speech tagging, musical score following, partial discharges and bioinformatics. Definition Let X_n and Y_n be discrete-time stochastic processes and n\geq 1. The pair (X_n,Y_n) is a ''hidden Markov model'' if * X_n is a Markov process whose behavior is not directly observable ("hidden"); * \operatorname\bigl(Y_n \i ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Expectation–maximization Algorithm
In statistics, an expectation–maximization (EM) algorithm is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the ''E'' step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step. History The EM algorithm was explained and given its name in a classic 1977 paper by Arthur Dempster, Nan Laird, and Donald Rubin. They pointed out that the method had been "proposed many times in special circumstances" by earlier authors. One of the earliest is the gene-counting method for estimating allele ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Word Sense Disambiguation
Word-sense disambiguation (WSD) is the process of identifying which sense of a word is meant in a sentence or other segment of context. In human language processing and cognition, it is usually subconscious/automatic but can often come to conscious attention when ambiguity impairs clarity of communication, given the pervasive polysemy in natural language. In computational linguistics, it is an open problem that affects other computer-related writing, such as discourse, improving relevance of search engines, anaphora resolution, coherence, and inference. Given that natural language requires reflection of neurological reality, as shaped by the abilities provided by the brain's neural networks, computer science has had a long-term challenge in developing the ability in computers to do natural language processing and machine learning. Many techniques have been researched, including dictionary-based methods that use the knowledge encoded in lexical resources, supervised machine le ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Word Sense
In linguistics, a word sense is one of the meanings of a word. For example, a dictionary may have over 50 different senses of the word "play", each of these having a different meaning based on the context of the word's usage in a sentence, as follows: In each sentence different collocates of "play" signal its different meanings. People and computers, as they read words, must use a process called word-sense disambiguationR. Navigli''Word Sense Disambiguation: A Survey', ACM Computing Surveys, 41(2), 2009, pp. 1-69. to reconstruct the likely intended meaning of a word. This process uses context to narrow the possible senses down to the probable ones. The context includes such things as the ideas conveyed by adjacent words and nearby phrases, the known or probable purpose and register of the conversation or document, and the orientation (time and place) implied or expressed. The disambiguation is thus context-sensitive. Advanced semantic analysis has resulted in a sub-di ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Generative Statistical Model
In statistical classification, two main approaches are called the generative approach and the discriminative approach. These compute classifiers by different approaches, differing in the degree of statistical modelling. Terminology is inconsistent, but three major types can be distinguished, following : # A generative model is a statistical model of the joint probability distribution P(X, Y) on given observable variable ''X'' and target variable ''Y'';: "Generative classifiers learn a model of the joint probability, p(x, y), of the inputs ''x'' and the label ''y'', and make their predictions by using Bayes rules to calculate p(y\mid x), and then picking the most likely label ''y''. # A discriminative model is a model of the conditional probability P(Y\mid X = x) of the target ''Y'', given an observation ''x''; and # Classifiers computed without using a probability model are also referred to loosely as "discriminative". The distinction between these last two classes is not con ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Translation Lexicon
A bilingual dictionary or translation dictionary is a specialized dictionary used to translate words or phrases from one language to another. Bilingual dictionaries can be ''unidirectional'', meaning that they list the meanings of words of one language in another, or can be '' bidirectional'', allowing translation to and from both languages. Bidirectional bilingual dictionaries usually consist of two sections, each listing words and phrases of one language alphabetically along with their translation. In addition to the translation, a bilingual dictionary usually indicates the part of speech, gender, verb type, declension model and other grammatical clues to help a non-native speaker use the word. Other features sometimes present in bilingual dictionaries are lists of phrases, usage and style guides, verb tables, maps and grammar references. In contrast to the bilingual dictionary, a monolingual dictionary defines words and phrases instead of translating them. History ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Syntactic Structure
In linguistics, syntax () is the study of how words and morphemes combine to form larger units such as phrases and sentences. Central concerns of syntax include word order, grammatical relations, hierarchical sentence structure ( constituency), agreement, the nature of crosslinguistic variation, and the relationship between form and meaning (semantics). There are numerous approaches to syntax that differ in their central assumptions and goals. Etymology The word ''syntax'' comes from Ancient Greek roots: "coordination", which consists of ''syn'', "together", and ''táxis'', "ordering". Topics The field of syntax contains a number of various topics that a syntactic theory is often designed to handle. The relation between the topics is treated differently in different theories, and some of them may not be considered to be distinct but instead to be derived from one another (i.e. word order can be seen as the result of movement rules derived from grammatical relations). Se ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Natural Language Processing
Natural language processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves. Challenges in natural language processing frequently involve speech recognition, natural-language understanding, and natural-language generation. History Natural language processing has its roots in the 1950s. Already in 1950, Alan Turing published an article titled "Computing Machinery and Intelligence" which proposed what is now called the Turing test as a criterion of intelligence, t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Unsupervised Learning
Unsupervised learning is a type of algorithm that learns patterns from untagged data. The hope is that through mimicry, which is an important mode of learning in people, the machine is forced to build a concise representation of its world and then generate imaginative content from it. In contrast to supervised learning where data is tagged by an expert, e.g. tagged as a "ball" or "fish", unsupervised methods exhibit self-organization that captures patterns as probability densities or a combination of neural feature preferences encoded in the machine's weights and activations. The other levels in the supervision spectrum are reinforcement learning where the machine is given only a numerical performance score as guidance, and semi-supervised learning where a small portion of the data is tagged. Neural networks Tasks vs. methods Neural network tasks are often categorized as discriminative (recognition) or generative (imagination). Often but not always, discriminative tas ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
