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Similarity based methods for word sense disambiguation
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vini89
Words can have more than one distinct meaning and many words can be interpreted in multiple ways depending on the context in which they occur. The process of automatically identifying the meaning of a polysemous word in a sentence is a fundamental task in Natural Language Processing (NLP). This phenomenon poses challenges to Natural Language Processing systems. There have been many efforts on word sense disambiguation for English; however, the amount of efforts for Amharic is very little. Many natural language processing applications, such as Machine Translation, Information Retrieval, Question Answering, and Information Extraction, require this task, which occurs at the semantic level. In this thesis, a knowledge-based word sense disambiguation method that employs Amharic WordNet is developed. Knowledge-based Amharic WSD extracts knowledge from word definitions and relations among words and senses. The proposed system consists of preprocessing, morphological analysis and disambiguation components besides Amharic WordNet database. Preprocessing is used to prepare the input sentence for morphological analysis and morphological analysis is used to reduce various forms of a word to a single root or stem word. Amharic WordNet contains words along with its different meanings, synsets and semantic relations with in concepts. Finally, the disambiguation component is used to identify the ambiguous words and assign the appropriate sense of ambiguous words in a sentence using Amharic WordNet by using sense overlap and related words. We have evaluated the knowledge-based Amharic word sense disambiguation using Amharic WordNet system by conducting two experiments. The first one is evaluating the effect of Amharic WordNet with and without morphological analyzer and the second one is determining an optimal windows size for Amharic WSD. For Amharic WordNet with morphological analyzer and Amharic WordNet without morphological analyzer we have achieved an accuracy of 57.5% and 80%, respectively. In the second experiment, we have found that two-word window on each side of the ambiguous word is enough for Amharic WSD. The test results have shown that the proposed WSD methods have performed better than previous Amharic WSD methods. Keywords: Natural Language Processing, Amharic WordNet, Word Sense Disambiguation, Knowledge Based Approach, Lesk Algorithm
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This thesis studies weakly supervised learning for information extraction methods in two settings: (1) unimodal weakly supervised learning, where annotated texts are augmented with a large corpus of unlabeled texts and (2) multimodal weakly supervised learning, where images or videos are augmented with texts that describe the content of these images or videos. In the <b>unimodal</b> setting we find that traditional semi-supervised methods based on generative Bayesian models are not suitable for the textual domain because of the violation of the assumptions made by these models. We develop an unsupervised model, the latent words language model (LWLM), that learns accurate word similarities from a large corpus of unlabeled texts. We show that this model is a good model of natural language, offering better predictive quality of unseen texts than previously proposed state-of-the-art language models. In addition, the learned word similarities can be used successfully to automatically expand words in the annotated training with synonyms, where the correct synonyms are chosen depending on the context. We show that this approach improves classifiers for word sense disambiguation and semantic role labeling. <br> The second part of this thesis discusses weakly supervised learning in a <b>multimodal</b> setting. We develop information extraction methods to information from texts that describe an image or video, and use this extracted information as a weak annotation of the image/video. A first model for the prediction of entities in an image uses two novel measures: The salience measure captures the importance of an entity, depending on the position of that entity in the discourse and in the sentence. The visualness measure captures the probability that an entity can be perceived visually, extracted from the WordNet database. We show that combining these measures results in an accurate prediction of the entities present in the image. We then discuss how this model can be used to learn a mapping from names in the text to faces in the image, and to retrieve images of a certain entity. We then turn to the automatic annotation of video. We develop a model that annotates a video with the visual verbs and their visual arguments, i.e. actions and arguments that can be observed in the video. The annotations of this system are successfully used to train a classifier that detects and classifies actions in the video. A second system annotates every scene in the video with the location of that scene. This system comprises a multimodal scene cut classifier that combines information from the text and the video, an IE algorithm that extracts possible locations from the text and a novel way to propagate location labels from one scene to another, depending the similarity of the scenes in the textual and visual domain.
PhD defense Koen Deschacht
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1.Introduction 2.Qu’est ce que les sens d’un mot ? 3.Ambiguïtés sémantiques 4.Définition de WSD 5.Pourquoi la WSD ? 6.Les types de WSD 7.Les méthodes de WSD 8.Evaluation Conclusion
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Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embeddings. Determining the most qualitative word embeddings is of crucial importance for such models. However, selecting the appropriate word embeddings is a perplexing task since the projected embedding space is not intuitive to humans.In this paper, we explore different approaches for creating distributed word representations. We perform an intrinsic evaluation of several state-of-the-art word embedding methods. Their performance on capturing word similarities is analysed with existing benchmark datasets for word pairs similarities. The research in this paper conducts a correlation analysis between ground truth word similarities and similarities obtained by different word embedding methods.
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
kevig
Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embeddings. Determining the most qualitative word embeddings is of crucial importance for such models. However, selecting the appropriate word embeddings is a perplexing task since the projected embedding space is not intuitive to humans. In this paper, we explore different approaches for creating distributed word representations. We perform an intrinsic evaluation of several state-of-the-art word embedding methods. Their performance on capturing word similarities is analysed with existing benchmark datasets for word pairs similarities. The research in this paper conducts a correlation analysis between ground truth word similarities and similarities obtained by different word embedding methods.
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
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P r e d i c t i n g t h e Semantic Orientation of A d j e c t i v e s V a s i l e i o s H a t z i v a s s i l o g l o u a n d K a t h l e e n R . M c K e o w n D e p a r t m e n t o f C o m p u t e r S c i e n c e 450 C o m p u t e r S c i e n c e B u i l d i n g C o l u m b i a U n i v e r s i t y N e w Y o r k , N . Y . 1 0 0 2 7 , U S A { v h , kathy)©cs, columbia, edu Abstract We identify and validate from a large cor- pus constraints from conjunctions on the positive or negative semantic orientation of the conjoined adjectives. A log-linear regression model uses these constraints to predict whether conjoined adjectives are of same or different orientations, achiev- ing 82% accuracy in this task when each conjunction is considered independently. Combining the constraints across m a n y ad- jectives, a clustering algorithm separates the adjectives into groups of different orien- tations, and finally, adjectives are labeled positive or negative. Evaluations on real d a t a and simulation experiments indicate high levels of performance: classification precision is more t h a n 90% for adjectives t h a t occur in a modest number of conjunc- tions in the corpus. 1 I n t r o d u c t i o n T h e semantic orientation or polarity of a word indi- cates the direction the word deviates from the norm for its semantic group or lezical field (Lehrer, 1974). It also constrains the word's usage in the language (Lyons, 1977), due to its evaluative characteristics (Battistella, 1990). For example, some nearly syn- onymous words differ in orientation because one im- plies desirability and the other does not (e.g., sim- ple versus simplisfic). In linguistic constructs such as conjunctions, which impose constraints on the se- mantic orientation of their arguments (Anscombre and Ducrot, 1983; Elhadad and McKeown, 1990), the choices of arguments and connective are mutu- ally constrained, as illustrated by: T h e t a x proposal was simple and well-received } simplistic but well-received *simplistic and well-received by the public. In addition, almost all antonyms have different se- mantic orientations3 If we know t h a t two words relate to the same property (for example, members of the same scalar group such as hot and cold) but have different orientations, we can usually infer t h a t they are antonyms. Given t h a t semantically similar words can be identified automatically on the basis of distributional properties and linguistic cues (Brown et al., 1992; Pereira et al., 1993; Hatzivassiloglou and McKeown, 1993), identifying the semantic orienta- tion of words would allow a system to fu rt h er refine the retrieved semantic similarity relationships, ex- tracting antonyms. Unfortunately, dictionaries and similar sources (theusari, WordNet (Miller et al., 1990)) do not in- clude semantic orientation information. 2 Explicit links between an t o n y m s.
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