A Comparison of Word Embeddings and N-Gram Models for Dbpedia Type and Invalid Entity Detection †
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A Sentiment-Based Chat Bot
A sentiment-based chat bot Automatic Twitter replies with Python ALEXANDER BLOM SOFIE THORSEN Bachelor’s essay at CSC Supervisor: Anna Hjalmarsson Examiner: Mårten Björkman E-mail: [email protected], [email protected] Abstract Natural language processing is a field in computer science which involves making computers derive meaning from human language and input as a way of interacting with the real world. Broadly speaking, sentiment analysis is the act of determining the attitude of an author or speaker, with respect to a certain topic or the overall context and is an application of the natural language processing field. This essay discusses the implementation of a Twitter chat bot that uses natural language processing and sentiment analysis to construct a be- lievable reply. This is done in the Python programming language, using a statistical method called Naive Bayes classifying supplied by the NLTK Python package. The essay concludes that applying natural language processing and sentiment analysis in this isolated fashion was simple, but achieving more complex tasks greatly increases the difficulty. Referat Natural language processing är ett fält inom datavetenskap som innefat- tar att få datorer att förstå mänskligt språk och indata för att på så sätt kunna interagera med den riktiga världen. Sentiment analysis är, generellt sagt, akten av att försöka bestämma känslan hos en författare eller talare med avseende på ett specifikt ämne eller sammanhang och är en applicering av fältet natural language processing. Den här rapporten diskuterar implementeringen av en Twitter-chatbot som använder just natural language processing och sentiment analysis för att kunna svara på tweets genom att använda känslan i tweetet. -
Classifying Relevant Social Media Posts During Disasters Using Ensemble of Domain-Agnostic and Domain-Specific Word Embeddings
AAAI 2019 Fall Symposium Series: AI for Social Good 1 Classifying Relevant Social Media Posts During Disasters Using Ensemble of Domain-agnostic and Domain-specific Word Embeddings Ganesh Nalluru, Rahul Pandey, Hemant Purohit Volgenau School of Engineering, George Mason University Fairfax, VA, 22030 fgn, rpandey4, [email protected] Abstract to provide relevant intelligence inputs to the decision-makers (Hughes and Palen 2012). However, due to the burstiness of The use of social media as a means of communication has the incoming stream of social media data during the time of significantly increased over recent years. There is a plethora of information flow over the different topics of discussion, emergencies, it is really hard to filter relevant information which is widespread across different domains. The ease of given the limited number of emergency service personnel information sharing has increased noisy data being induced (Castillo 2016). Therefore, there is a need to automatically along with the relevant data stream. Finding such relevant filter out relevant posts from the pile of noisy data coming data is important, especially when we are dealing with a time- from the unconventional information channel of social media critical domain like disasters. It is also more important to filter in a real-time setting. the relevant data in a real-time setting to timely process and Our contribution: We provide a generalizable classification leverage the information for decision support. framework to classify relevant social media posts for emer- However, the short text and sometimes ungrammatical nature gency services. We introduce the framework in the Method of social media data challenge the extraction of contextual section, including the description of domain-agnostic and information cues, which could help differentiate relevant vs. -
Arxiv:2002.06235V1
Semantic Relatedness and Taxonomic Word Embeddings Magdalena Kacmajor John D. Kelleher Innovation Exchange ADAPT Research Centre IBM Ireland Technological University Dublin [email protected] [email protected] Filip Klubickaˇ Alfredo Maldonado ADAPT Research Centre Underwriters Laboratories Technological University Dublin [email protected] [email protected] Abstract This paper1 connects a series of papers dealing with taxonomic word embeddings. It begins by noting that there are different types of semantic relatedness and that different lexical representa- tions encode different forms of relatedness. A particularly important distinction within semantic relatedness is that of thematic versus taxonomic relatedness. Next, we present a number of ex- periments that analyse taxonomic embeddings that have been trained on a synthetic corpus that has been generated via a random walk over a taxonomy. These experiments demonstrate how the properties of the synthetic corpus, such as the percentage of rare words, are affected by the shape of the knowledge graph the corpus is generated from. Finally, we explore the interactions between the relative sizes of natural and synthetic corpora on the performance of embeddings when taxonomic and thematic embeddings are combined. 1 Introduction Deep learning has revolutionised natural language processing over the last decade. A key enabler of deep learning for natural language processing has been the development of word embeddings. One reason for this is that deep learning intrinsically involves the use of neural network models and these models only work with numeric inputs. Consequently, applying deep learning to natural language processing first requires developing a numeric representation for words. Word embeddings provide a way of creating numeric representations that have proven to have a number of advantages over traditional numeric rep- resentations of language. -
Applying Ensembling Methods to BERT to Boost Model Performance
Applying Ensembling Methods to BERT to Boost Model Performance Charlie Xu, Solomon Barth, Zoe Solis Department of Computer Science Stanford University cxu2, sbarth, zoesolis @stanford.edu Abstract Due to its wide range of applications and difficulty to solve, Machine Comprehen- sion has become a significant point of focus in AI research. Using the SQuAD 2.0 dataset as a metric, BERT models have been able to achieve superior performance to most other models. Ensembling BERT with other models has yielded even greater results, since these ensemble models are able to utilize the relative strengths and adjust for the relative weaknesses of each of the individual submodels. In this project, we present various ensemble models that leveraged BiDAF and multiple different BERT models to achieve superior performance. We also studied the effects how dataset manipulations and different ensembling methods can affect the performance of our models. Lastly, we attempted to develop the Synthetic Self- Training Question Answering model. In the end, our best performing multi-BERT ensemble obtained an EM score of 73.576 and an F1 score of 76.721. 1 Introduction Machine Comprehension (MC) is a complex task in NLP that aims to understand written language. Question Answering (QA) is one of the major tasks within MC, requiring a model to provide an answer, given a contextual text passage and question. It has a wide variety of applications, including search engines and voice assistants, making it a popular problem for NLP researchers. The Stanford Question Answering Dataset (SQuAD) is a very popular means for training, tuning, testing, and comparing question answering systems. -
Injection of Automatically Selected Dbpedia Subjects in Electronic
Injection of Automatically Selected DBpedia Subjects in Electronic Medical Records to boost Hospitalization Prediction Raphaël Gazzotti, Catherine Faron Zucker, Fabien Gandon, Virginie Lacroix-Hugues, David Darmon To cite this version: Raphaël Gazzotti, Catherine Faron Zucker, Fabien Gandon, Virginie Lacroix-Hugues, David Darmon. Injection of Automatically Selected DBpedia Subjects in Electronic Medical Records to boost Hos- pitalization Prediction. SAC 2020 - 35th ACM/SIGAPP Symposium On Applied Computing, Mar 2020, Brno, Czech Republic. 10.1145/3341105.3373932. hal-02389918 HAL Id: hal-02389918 https://hal.archives-ouvertes.fr/hal-02389918 Submitted on 16 Dec 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Injection of Automatically Selected DBpedia Subjects in Electronic Medical Records to boost Hospitalization Prediction Raphaël Gazzotti Catherine Faron-Zucker Fabien Gandon Université Côte d’Azur, Inria, CNRS, Université Côte d’Azur, Inria, CNRS, Inria, Université Côte d’Azur, CNRS, I3S, Sophia-Antipolis, France I3S, Sophia-Antipolis, France -
Combining Word Embeddings with Semantic Resources
AutoExtend: Combining Word Embeddings with Semantic Resources Sascha Rothe∗ LMU Munich Hinrich Schutze¨ ∗ LMU Munich We present AutoExtend, a system that combines word embeddings with semantic resources by learning embeddings for non-word objects like synsets and entities and learning word embeddings that incorporate the semantic information from the resource. The method is based on encoding and decoding the word embeddings and is flexible in that it can take any word embeddings as input and does not need an additional training corpus. The obtained embeddings live in the same vector space as the input word embeddings. A sparse tensor formalization guar- antees efficiency and parallelizability. We use WordNet, GermaNet, and Freebase as semantic resources. AutoExtend achieves state-of-the-art performance on Word-in-Context Similarity and Word Sense Disambiguation tasks. 1. Introduction Unsupervised methods for learning word embeddings are widely used in natural lan- guage processing (NLP). The only data these methods need as input are very large corpora. However, in addition to corpora, there are many other resources that are undoubtedly useful in NLP, including lexical resources like WordNet and Wiktionary and knowledge bases like Wikipedia and Freebase. We will simply refer to these as resources. In this article, we present AutoExtend, a method for enriching these valuable resources with embeddings for non-word objects they describe; for example, Auto- Extend enriches WordNet with embeddings for synsets. The word embeddings and the new non-word embeddings live in the same vector space. Many NLP applications benefit if non-word objects described by resources—such as synsets in WordNet—are also available as embeddings. -
Word-Like Character N-Gram Embedding
Word-like character n-gram embedding Geewook Kim and Kazuki Fukui and Hidetoshi Shimodaira Department of Systems Science, Graduate School of Informatics, Kyoto University Mathematical Statistics Team, RIKEN Center for Advanced Intelligence Project fgeewook, [email protected], [email protected] Abstract Table 1: Top-10 2-grams in Sina Weibo and 4-grams in We propose a new word embedding method Japanese Twitter (Experiment 1). Words are indicated called word-like character n-gram embed- by boldface and space characters are marked by . ding, which learns distributed representations FNE WNE (Proposed) Chinese Japanese Chinese Japanese of words by embedding word-like character n- 1 ][ wwww 自己 フォロー grams. Our method is an extension of recently 2 。␣ !!!! 。␣ ありがと proposed segmentation-free word embedding, 3 !␣ ありがと ][ wwww 4 .. りがとう 一个 !!!! which directly embeds frequent character n- 5 ]␣ ございま 微博 めっちゃ grams from a raw corpus. However, its n-gram 6 。。 うござい 什么 んだけど vocabulary tends to contain too many non- 7 ,我 とうござ 可以 うござい 8 !! ざいます 没有 line word n-grams. We solved this problem by in- 9 ␣我 がとうご 吗? 2018 troducing an idea of expected word frequency. 10 了, ください 哈哈 じゃない Compared to the previously proposed meth- ods, our method can embed more words, along tion tools are used to determine word boundaries with the words that are not included in a given in the raw corpus. However, these segmenters re- basic word dictionary. Since our method does quire rich dictionaries for accurate segmentation, not rely on word segmentation with rich word which are expensive to prepare and not always dictionaries, it is especially effective when the available. -
Information Extraction Based on Named Entity for Tourism Corpus
Information Extraction based on Named Entity for Tourism Corpus Chantana Chantrapornchai Aphisit Tunsakul Dept. of Computer Engineering Dept. of Computer Engineering Faculty of Engineering Faculty of Engineering Kasetsart University Kasetsart University Bangkok, Thailand Bangkok, Thailand [email protected] [email protected] Abstract— Tourism information is scattered around nowa- The ontology is extracted based on HTML web structure, days. To search for the information, it is usually time consuming and the corpus is based on WordNet. For these approaches, to browse through the results from search engine, select and the time consuming process is the annotation which is to view the details of each accommodation. In this paper, we present a methodology to extract particular information from annotate the type of name entity. In this paper, we target at full text returned from the search engine to facilitate the users. the tourism domain, and aim to extract particular information Then, the users can specifically look to the desired relevant helping for ontology data acquisition. information. The approach can be used for the same task in We present the framework for the given named entity ex- other domains. The main steps are 1) building training data traction. Starting from the web information scraping process, and 2) building recognition model. First, the tourism data is gathered and the vocabularies are built. The raw corpus is used the data are selected based on the HTML tag for corpus to train for creating vocabulary embedding. Also, it is used building. The data is used for model creation for automatic for creating annotated data. -
A Second Look at Word Embeddings W4705: Natural Language Processing
A second look at word embeddings W4705: Natural Language Processing Fei-Tzin Lee October 23, 2019 Fei-Tzin Lee Word embeddings October 23, 2019 1 / 39 Overview Last time... • Distributional representations (SVD) • word2vec • Analogy performance Fei-Tzin Lee Word embeddings October 23, 2019 2 / 39 Overview This time • Homework-related topics • Non-homework topics Fei-Tzin Lee Word embeddings October 23, 2019 3 / 39 Overview Outline 1 GloVe 2 How good are our embeddings? 3 The math behind the models? 4 Word embeddings, new and improved Fei-Tzin Lee Word embeddings October 23, 2019 4 / 39 GloVe Outline 1 GloVe 2 How good are our embeddings? 3 The math behind the models? 4 Word embeddings, new and improved Fei-Tzin Lee Word embeddings October 23, 2019 5 / 39 GloVe A recap of GloVe Our motivation: • SVD places too much emphasis on unimportant matrix entries • word2vec never gets to look at global co-occurrence statistics Can we create a new model that balances the strengths of both of these to better express linear structure? Fei-Tzin Lee Word embeddings October 23, 2019 6 / 39 GloVe Setting We'll use more or less the same setting as in previous models: • A corpus D • A word vocabulary V from which both target and context words are drawn • A co-occurrence matrix Mij , from which we can calculate conditional probabilities Pij = Mij =Mi Fei-Tzin Lee Word embeddings October 23, 2019 7 / 39 GloVe The GloVe objective, in overview Idea: we want to capture not individual probabilities of co-occurrence, but ratios of co-occurrence probability between pairs (wi ; wk ) and (wj ; wk ). -
Learned in Speech Recognition: Contextual Acoustic Word Embeddings
LEARNED IN SPEECH RECOGNITION: CONTEXTUAL ACOUSTIC WORD EMBEDDINGS Shruti Palaskar∗, Vikas Raunak∗ and Florian Metze Carnegie Mellon University, Pittsburgh, PA, U.S.A. fspalaska j vraunak j fmetze [email protected] ABSTRACT model [10, 11, 12] trained for direct Acoustic-to-Word (A2W) speech recognition [13]. Using this model, we jointly learn to End-to-end acoustic-to-word speech recognition models have re- automatically segment and classify input speech into individual cently gained popularity because they are easy to train, scale well to words, hence getting rid of the problem of chunking or requiring large amounts of training data, and do not require a lexicon. In addi- pre-defined word boundaries. As our A2W model is trained at the tion, word models may also be easier to integrate with downstream utterance level, we show that we can not only learn acoustic word tasks such as spoken language understanding, because inference embeddings, but also learn them in the proper context of their con- (search) is much simplified compared to phoneme, character or any taining sentence. We also evaluate our contextual acoustic word other sort of sub-word units. In this paper, we describe methods embeddings on a spoken language understanding task, demonstrat- to construct contextual acoustic word embeddings directly from a ing that they can be useful in non-transcription downstream tasks. supervised sequence-to-sequence acoustic-to-word speech recog- Our main contributions in this paper are the following: nition model using the learned attention distribution. On a suite 1. We demonstrate the usability of attention not only for aligning of 16 standard sentence evaluation tasks, our embeddings show words to acoustic frames without any forced alignment but also for competitive performance against a word2vec model trained on the constructing Contextual Acoustic Word Embeddings (CAWE). -
Cw2vec: Learning Chinese Word Embeddings with Stroke N-Gram Information
The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) cw2vec: Learning Chinese Word Embeddings with Stroke n-gram Information Shaosheng Cao,1,2 Wei Lu,2 Jun Zhou,1 Xiaolong Li1 1 AI Department, Ant Financial Services Group 2 Singapore University of Technology and Design {shaosheng.css, jun.zhoujun, xl.li}@antfin.com [email protected] Abstract We propose cw2vec, a novel method for learning Chinese word embeddings. It is based on our observation that ex- ploiting stroke-level information is crucial for improving the learning of Chinese word embeddings. Specifically, we de- sign a minimalist approach to exploit such features, by us- ing stroke n-grams, which capture semantic and morpholog- ical level information of Chinese words. Through qualita- tive analysis, we demonstrate that our model is able to ex- Figure 1: Radical v.s. components v.s. stroke n-gram tract semantic information that cannot be captured by exist- ing methods. Empirical results on the word similarity, word analogy, text classification and named entity recognition tasks show that the proposed approach consistently outperforms Bojanowski et al. 2016; Cao and Lu 2017). While these ap- state-of-the-art approaches such as word-based word2vec and proaches were shown effective, they largely focused on Eu- GloVe, character-based CWE, component-based JWE and ropean languages such as English, Spanish and German that pixel-based GWE. employ the Latin script in their writing system. Therefore the methods developed are not directly applicable to lan- guages such as Chinese that employ a completely different 1. Introduction writing system. Word representation learning has recently received a sig- In Chinese, each word typically consists of less charac- nificant amount of attention in the field of natural lan- ters than English1, where each character conveys fruitful se- guage processing (NLP). -
Aconcorde: Towards a Proper Concordance for Arabic
aConCorde: towards a proper concordance for Arabic Andrew Roberts, Dr Latifa Al-Sulaiti and Eric Atwell School of Computing University of Leeds LS2 9JT United Kingdom {andyr,latifa,eric}@comp.leeds.ac.uk July 12, 2005 Abstract Arabic corpus linguistics is currently enjoying a surge in activity. As the growth in the number of available Arabic corpora continues, there is an increased need for robust tools that can process this data, whether it be for research or teaching. One such tool that is useful for both groups is the concordancer — a simple tool for displaying a specified target word in its context. However, obtaining one that can reliably cope with the Arabic lan- guage had proved extremely difficult. Therefore, aConCorde was created to provide such a tool to the community. 1 Introduction A concordancer is a simple tool for summarising the contents of corpora based on words of interest to the user. Otherwise, manually navigating through (of- ten very large) corpora would be a long and tedious task. Concordancers are therefore extremely useful as a time-saving tool, but also much more. By isolat- ing keywords in their contexts, linguists can use concordance output to under- stand the behaviour of interesting words. Lexicographers can use the evidence to find if a word has multiple senses, and also towards defining their meaning. There is also much research and discussion about how concordance tools can be beneficial for data-driven language learning (Johns, 1990). A number of studies have demonstrated that providing access to corpora and concordancers benefited students learning a second language.