[PDF] Top 20 Can Topic Modelling benefit from Word Sense Information?
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Can Topic Modelling benefit from Word Sense Information?
... 4.3. Word Sense Disambiguation with Fallback Most topics from the previous experiment include differ- ent senses of the same word, often ...each sense. Our next step was to perform WSD ... See full document
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Topic Models for Word Sense Disambiguation and Token Based Idiom Detection
... of topic models for sense ...pute topic models from a large unlabelled corpus and include them as features in a supervised sys- ...predominant word senses into a topic ... See full document
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Unsupervised Domain Tuning to Improve Word Sense Disambiguation
... invariant sense distribution for each topic, p(w, s|t). Once this word sense distribution is obtained, the underlying WSD algorithm is never needed ...correct sense within an individual ... See full document
5
Improving Word Sense Disambiguation Using Topic Features
... per sense in Senseval 2 and 3 lexi- cal sample task respectively, and ...per sense in the SemCor ...drawn from any English ...context information, which the bag-of-words feature is supposed to ... See full document
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Best Topic Word Selection for Topic Labelling
... inception, topic mod- elling (Blei et ...2009), word sense discrimination (Brody and Lapata, 2009), sentiment analysis (Titov and McDonald, 2008) and information retrieval (Wei and Croft, ... See full document
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Measuring Topic Homogeneity and its Application to Dictionary Based Word Sense Disambiguation
... measure Topic Homogeneity using a variety of NLP re- ...measure word-sets created using the Word- Net::Domains package and which have varying levels of homogeneity, they are found to corre- late well ... See full document
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Word Sense Disambiguation Improves Information Retrieval
... the sense matches between terms in query and the document ...skewed sense distribution and the collocation effect from other query terms already performs some ...artificial word ambiguity in ... See full document
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Multi Sense Embeddings from Topic Models
... Distributed word embeddings have yielded state-of-the-art performance in many NLP tasks, mainly due to their success in captur- ing useful semantic ...each word whereas a large number of words are pol- ... See full document
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Deep Level Markov Chain Model for Semantic Document Retrieval
... Topic modelling is the basic model in natural language ...used topic modelling for information retrieval solution, they proved the effective of system when tested with TREC data sets ... See full document
6
Document Similarity for Texts of Varying Lengths via Hidden Topics
... dataset from the CL-SciSumm Shared Task (Jaidka et ...the topic of the ...each topic, we rank all 730 papers in terms of their relevance generated by our method and baselines using both sets of ... See full document
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A Topic Model for Word Sense Disambiguation
... any topic based information re- trieval scheme could employ topics that include se- mantically relevant (but perhaps unobserved) ...could benefit from the local context as well as the document ... See full document
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A Sense Topic Model for Word Sense Induction with Unsupervised Data Enrichment
... called word embeddings, capture information via training criteria based on predicting nearby ...words can be computed using cosine similarity of their embed- ding ...vectors. Word embeddings ... See full document
14
Word Sense Induction for Novel Sense Detection
... via topic modelling — using La- tent Dirichlet Allocation (LDA: Blei et ...the topic model to determine the appropriate sense gran- ...ularity. Topic modelling is an unsupervised ... See full document
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Can Syntactic and Logical Graphs help Word Sense Disambiguation?
... given that our logical analyzer in its current development state may not cover enough English syntactic patterns to be competitive with the syntactic graphs, which are produced by a state-of-the-art dependency parser (De ... See full document
7
International Journal of Computer Science and Mobile Computing
... Keyword extraction is an important technique for document retrieval, Web page retrieval, document clustering, summarization, text mining, and so on. By extracting appropriate keywords, we can choose easily which ... See full document
5
Word Sense Disambiguation for Cross Language Information Retrieval
... Our algorithm learns associations of WordNet synsets with words in a surrounding context to determine a word sense.. It consists of two phases.[r] ... See full document
6
Accounting ngrams and multi word terms can improve topic models
... elements from each topic at each it- ...that topic coherence does not depend highly on this parame- ter, while the best value for perplexity is achieved when selecting top-5 or top-7 ... See full document
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Can accounting information system benefit from cloud computing: the case of Saudi arabia
... the Information technology ...recovery benefit from the elimination of tape backup, offsite tape backup, making for faster online backup and recovery ... See full document
7
One Representation per Word Does it make Sense for Composition?
... senses from its ...a sense as the target sense, and from its list of example sentences randomly sampled 2 sentences, one as the target example and one as the “correct answer” for the list of ... See full document
12
Language Modelling Makes Sense: Propagating Representations through WordNet for Full Coverage Word Sense Disambiguation
... generating sense embeddings and relying on k-NN w/MFS for ...stemmed from their powerful NLM, they also introduced a label propagation method that further improved results in some ...sophisticated ... See full document
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