latent semantic word sense induction

Ngày tải lên: 23/03/2014, 16:20

... topic of this paper is word sense induction, that is the automatic discovery of the possible senses of a word. A related problem is word sense disambiguation: Here the senses are assumed to ... of Automatic Word Sense Induction Reinhard Rapp University of Mainz, FASK D-76711 Germersheim, Germany rapp@mail.fask.uni-mainz.de Abstract Recent studies in word sense induction are ... Fully Automatic Word Sense Induction by Semantic Clustering. Cambridge University, Master’s Thesis, M.Phil. in Com- puter Speech. Pantel, P.; Lin, D. (2002). Discovering word senses from text....

Ngày tải lên: 20/02/2014, 16:20

... generated word. Then, the word sense is selected based on the word, neighbor, and topic. Boyd-Graber et al. (2007) extend the topic mod- eling framework to include WordNet senses as a latent variable ... other tasks, besides sense induction. 3 The Sense Induction Model The core idea behind sense induction is that con- textual information provides important cues re- garding a word s meaning. The ... Introduction Sense induction is the task of discovering automat- ically all possible senses of an ambiguous word. It is related to, but distinct from, word sense disam- biguation (WSD) where the senses...

Ngày tải lên: 22/02/2014, 02:20

Ngày tải lên: 17/03/2014, 22:20

Ngày tải lên: 17/03/2014, 08:20

Ngày tải lên: 31/03/2014, 03:20

... classification- based approach to Word Sense Disambigua- tion. Instead of using a predefined monolin- gual sense- inventory such as WordNet, we use a language-independent framework where the word senses are derived ... Cross-Lingual Word Sense Disambiguation task for all five target languages. 1 Introduction Word Sense Disambiguation (WSD) is the NLP task that consists in selecting the correct sense of a polysemous word ... testing phase. This system also differs from the Uvt-WSD and ParaSense systems in the sense that the word senses are derived from WordNet, whereas the other systems do not use any external resources. The...

Ngày tải lên: 20/02/2014, 05:20

... each possible word sense. The algo- rithm looks for words in the sense definitions that overlap with context words in the given sentence, and chooses the sense that yields maximal word overlap. ... all WordNet synonyms of the target word, under all its possible senses, and picking randomly one of the synonyms as the source word. For example, the word ‘disc’ is one of the words in the Sense- val ... target word, providing a decision criterion for sense matching. In other words, we expect that under a matching sense the target word would oc- cur in prototypical contexts of the source word. To...

Ngày tải lên: 20/02/2014, 12:20

... two words. An ambiguous word has the same number of EPs as of senses. Each EP's sense maps to a sense of ambiguous word. The semantic equivalence demands further equivalence at each sense ... morpheme words for every sense of the word in construction of the EP, rich semantic information can be acquired in the training step and is an advantage for sense disambiguation. 2) Senseval-3 ... ambiguous word need to simulate the function of the real ambiguous word, and to acquire semantic knowledge as the real ambiguous word does. Thus, we call it an equivalent pseudoword (EP)...

Ngày tải lên: 20/02/2014, 12:20

... describes SENSELEARNER – a minimally supervised word sense disam- biguation system that attempts to disam- biguate all content words in a text using WordNet senses. We evaluate the accu- racy of SENSELEARNER ... minimally supervised word- sense dis- ambiguation that attempts to disambiguate all content words in a text using WordNet senses. The results ob- tained on both SENSEVAL-2 and SENSEVAL-3 data sets ... annotated with WordNet senses. The SENSEVAL-2 corpus includes a total of 2,473 annotated content words, and the SENSEVAL- 3 corpus includes annotations for an additional set of 2,081 words. Table...

Ngày tải lên: 20/02/2014, 15:20

... the Senseval 2 English lexical sample task. It includes 73 target words, among which nouns, adjectives, adverbs and verbs. For each word, training and test instances tagged with WordNet senses ... text analysis. In Proceedings of Table 1: Two of the Senseval-2 sense classes for the target word “art”, from WordNet 1.7 (Fellbaum 1998). Class Sense 1 the creation of beautiful or significant things 2 ... with sense class 1 : training example with sense class 2 : test example with unknown sense class : test example with predicted sense first principal “ quasi-axis” class 2 (correct sense...

Ngày tải lên: 20/02/2014, 16:20

... classes, where is the number of latent classes and then initializing based on the statistics of word occurrences in each cluster. We iterated over the 109 Improving Probabilistic Latent Semantic Analysis with ... Indexing by latent semantic analysis. Jour- nal of the American Society of Information Science, 41(6):391–40 7. Chris H. Q. Ding. 1999. A similarity- based probability model for latent semantic ... different latent classes correspond- ing to different senses of a word, and thus bet- ter handles polysemy (Hofmann, 1999). The LSA model has two additional desirable features. First, the word document...

Ngày tải lên: 22/02/2014, 02:20

... each sense of a word to predict the predominant sense. As in our earlier work (Chan and Ng, 2005b), we normalized the prevalence score of each sense to obtain estimated sense priors for each word, ... @comp.nus.edu.sg Abstract Instances of a word drawn from different domains may have different sense priors (the proportions of the different senses of a word) . This in turn affects the accuracy of word sense disambiguation ... difference in sense priors (i.e., the proportions of the different senses of a word) between BC and WSJ. For in- stance, the noun interest has these 6 senses in the DSO corpus: sense 1, 2, 3,...

Ngày tải lên: 08/03/2014, 02:21

... Maximizing Semantic Re- latedness to Perform Word Sense Disambiguation. Sinopalnikova, A. (2004). Word Association Thesau- rus as a Resource for Building Wordnet. In GWC 2004. Sussna, M. (1993). Word ... lexical hub to disambiguate word sense, using both syn- tagmatic and paradigmatic relations of words. It only employs the semantic net- work of WordNet to calculate word simi- larity, and the ... Then for each context word we retrieve its corresponding words in each word list and calculate the similarity be- tween the target and these words including the context words. As a result we...

Ngày tải lên: 08/03/2014, 02:21

... Introduction Many words have multiple meanings, depending on the context in which they are used. Word sense dis- ambiguation (WSD) is the task of determining the correct meaning or sense of a word in context. ... that in resolving sense ambiguity, a WSD system will be able to help an MT system to determine the correct translation for an ambiguous word. To determine the correct sense of a word, WSD systems ... extra English words pro- vided by Hiero+WSD, though appropriate, do not 39 translation choices for a word w were defined as the set of words or phrases aligned to w, as gathered from a word- aligned...

Ngày tải lên: 08/03/2014, 02:21

... words to the right and left of the verb, identified using POS tags, represented by has_narrow(snt, word_ position, word) : has_narrow(snt 1 , 1st _word_ left, mind). has_narrow(snt 1 , 1st _word_ right, ... the positions of the words, represented by has_narrow_trns(snt, word_ position, portuguese _word) : has_narrow_trns(snt 1 , 1st _word_ right, como). has_narrow_trns(snt 1 , 2nd _word_ right, um). … ... simple, containing only the sentence identifier and the sense of the verb in that sentence, i.e. sense( snt, sense) : sense( snt 1 ,voltar). sense( snt 2 ,ir). … Based on the examples, background...

Ngày tải lên: 08/03/2014, 02:21

... Introduction In natural language, a word often assumes different meanings, and the task of determining the correct meaning, or sense, of a word in different contexts is known as word sense disambiguation (WSD). ... the dif- ference in sense priors (i.e., the proportions of the different senses of a word) between BC and WSJ. When the authors assumed they knew the sense pri- ors of each word in BC and WSJ, ... of the different senses of each word were the same between BC and WSJ. We can simi- larly choose BC examples such that the sense priors in the BC training data adhere to the sense priors in the...

Ngày tải lên: 08/03/2014, 02:21

... method of word sense disambiguation that com- putes the intended sense of a target word, using WordNet-based measures of seman- tic relatedness (Patwardhan et al., 2003). SenseRelate::TargetWord is ... by selecting that sense of the target word which is most related to the context words. Relatedness between word senses is mea- sured using the WordNet::Similarity Perl modules. 1 Introduction Many words ... Relatedness Measure Context Target Sense Preprocessing Format Filter Sense Inventory Context Selection Postprocessing Pick Sense Figure 1: A generalized framework for Word Sense Disambiguation. modules...

Ngày tải lên: 08/03/2014, 04:22

... significance. L1 words that translate into the same L2 word are grouped into clusters; SALAAM identifies the appropriate senses for the words in those clusters based on the words senses’ proximity in WordNet. ... that when words in one language, L1, are translated into the same word in a second language, L2, then those L1 words are semantically similar. For example, when the English — L1 — words bank, ... GenCor (Mihalcea&Moldovan, 1999). GenCor creates seeds from monosemous words in WordNet, Sem- cor data, sense tagged examples from the glosses of polysemous words in WordNet, and other hand tagged data if available....

Ngày tải lên: 08/03/2014, 04:22

Ngày tải lên: 08/03/2014, 04:22