Latent Semantic Analysis for Text-Based Research
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Combining Semantic and Lexical Measures to Evaluate Medical Terms Similarity?
Combining semantic and lexical measures to evaluate medical terms similarity? Silvio Domingos Cardoso1;2, Marcos Da Silveira1, Ying-Chi Lin3, Victor Christen3, Erhard Rahm3, Chantal Reynaud-Dela^ıtre2, and C´edricPruski1 1 LIST, Luxembourg Institute of Science and Technology, Luxembourg fsilvio.cardoso,marcos.dasilveira,[email protected] 2 LRI, University of Paris-Sud XI, France [email protected] 3 Department of Computer Science, Universit¨atLeipzig, Germany flin,christen,[email protected] Abstract. The use of similarity measures in various domains is corner- stone for different tasks ranging from ontology alignment to information retrieval. To this end, existing metrics can be classified into several cate- gories among which lexical and semantic families of similarity measures predominate but have rarely been combined to complete the aforemen- tioned tasks. In this paper, we propose an original approach combining lexical and ontology-based semantic similarity measures to improve the evaluation of terms relatedness. We validate our approach through a set of experiments based on a corpus of reference constructed by domain experts of the medical field and further evaluate the impact of ontology evolution on the used semantic similarity measures. Keywords: Similarity measures · Ontology evolution · Semantic Web · Medical terminologies 1 Introduction Measuring the similarity between terms is at the heart of many research inves- tigations. In ontology matching, similarity measures are used to evaluate the relatedness between concepts from different ontologies [9]. The outcomes are the mappings between the ontologies, increasing the coverage of domain knowledge and optimize semantic interoperability between information systems. In informa- tion retrieval, similarity measures are used to evaluate the relatedness between units of language (e.g., words, sentences, documents) to optimize search [39]. -
Probabilistic Topic Modelling with Semantic Graph
Probabilistic Topic Modelling with Semantic Graph B Long Chen( ), Joemon M. Jose, Haitao Yu, Fajie Yuan, and Huaizhi Zhang School of Computing Science, University of Glasgow, Sir Alwyns Building, Glasgow, UK [email protected] Abstract. In this paper we propose a novel framework, topic model with semantic graph (TMSG), which couples topic model with the rich knowledge from DBpedia. To begin with, we extract the disambiguated entities from the document collection using a document entity linking system, i.e., DBpedia Spotlight, from which two types of entity graphs are created from DBpedia to capture local and global contextual knowl- edge, respectively. Given the semantic graph representation of the docu- ments, we propagate the inherent topic-document distribution with the disambiguated entities of the semantic graphs. Experiments conducted on two real-world datasets show that TMSG can significantly outperform the state-of-the-art techniques, namely, author-topic Model (ATM) and topic model with biased propagation (TMBP). Keywords: Topic model · Semantic graph · DBpedia 1 Introduction Topic models, such as Probabilistic Latent Semantic Analysis (PLSA) [7]and Latent Dirichlet Analysis (LDA) [2], have been remarkably successful in ana- lyzing textual content. Specifically, each document in a document collection is represented as random mixtures over latent topics, where each topic is character- ized by a distribution over words. Such a paradigm is widely applied in various areas of text mining. In view of the fact that the information used by these mod- els are limited to document collection itself, some recent progress have been made on incorporating external resources, such as time [8], geographic location [12], and authorship [15], into topic models. -
Employee Matching Using Machine Learning Methods
Master of Science in Computer Science May 2019 Employee Matching Using Machine Learning Methods Sumeesha Marakani Faculty of Computing, Blekinge Institute of Technology, 371 79 Karlskrona, Sweden This thesis is submitted to the Faculty of Computing at Blekinge Institute of Technology in partial fulfillment of the requirements for the degree of Master of Science in Computer Science. The thesis is equivalent to 20 weeks of full time studies. The authors declare that they are the sole authors of this thesis and that they have not used any sources other than those listed in the bibliography and identified as references. They further declare that they have not submitted this thesis at any other institution to obtain a degree. Contact Information: Author(s): Sumeesha Marakani E-mail: [email protected] University advisor: Prof. Veselka Boeva Department of Computer Science External advisors: Lars Tornberg [email protected] Daniel Lundgren [email protected] Faculty of Computing Internet : www.bth.se Blekinge Institute of Technology Phone : +46 455 38 50 00 SE–371 79 Karlskrona, Sweden Fax : +46 455 38 50 57 Abstract Background. Expertise retrieval is an information retrieval technique that focuses on techniques to identify the most suitable ’expert’ for a task from a list of individ- uals. Objectives. This master thesis is a collaboration with Volvo Cars to attempt ap- plying this concept and match employees based on information that was extracted from an internal tool of the company. In this tool, the employees describe themselves in free flowing text. This text is extracted from the tool and analyzed using Natural Language Processing (NLP) techniques. -
Matrix Decompositions and Latent Semantic Indexing
Online edition (c)2009 Cambridge UP DRAFT! © April 1, 2009 Cambridge University Press. Feedback welcome. 403 Matrix decompositions and latent 18 semantic indexing On page 123 we introduced the notion of a term-document matrix: an M N matrix C, each of whose rows represents a term and each of whose column× s represents a document in the collection. Even for a collection of modest size, the term-document matrix C is likely to have several tens of thousands of rows and columns. In Section 18.1.1 we first develop a class of operations from linear algebra, known as matrix decomposition. In Section 18.2 we use a special form of matrix decomposition to construct a low-rank approximation to the term-document matrix. In Section 18.3 we examine the application of such low-rank approximations to indexing and retrieving documents, a technique referred to as latent semantic indexing. While latent semantic in- dexing has not been established as a significant force in scoring and ranking for information retrieval, it remains an intriguing approach to clustering in a number of domains including for collections of text documents (Section 16.6, page 372). Understanding its full potential remains an area of active research. Readers who do not require a refresher on linear algebra may skip Sec- tion 18.1, although Example 18.1 is especially recommended as it highlights a property of eigenvalues that we exploit later in the chapter. 18.1 Linear algebra review We briefly review some necessary background in linear algebra. Let C be an M N matrix with real-valued entries; for a term-document matrix, all × RANK entries are in fact non-negative. -
Evaluating Lexical Similarity to Build Sentiment Similarity
Evaluating Lexical Similarity to Build Sentiment Similarity Grégoire Jadi∗, Vincent Claveauy, Béatrice Daille∗, Laura Monceaux-Cachard∗ ∗ LINA - Univ. Nantes {gregoire.jadi beatrice.daille laura.monceaux}@univ-nantes.fr y IRISA–CNRS, France [email protected] Abstract In this article, we propose to evaluate the lexical similarity information provided by word representations against several opinion resources using traditional Information Retrieval tools. Word representation have been used to build and to extend opinion resources such as lexicon, and ontology and their performance have been evaluated on sentiment analysis tasks. We question this method by measuring the correlation between the sentiment proximity provided by opinion resources and the semantic similarity provided by word representations using different correlation coefficients. We also compare the neighbors found in word representations and list of similar opinion words. Our results show that the proximity of words in state-of-the-art word representations is not very effective to build sentiment similarity. Keywords: opinion lexicon evaluation, sentiment analysis, spectral representation, word embedding 1. Introduction tend or build sentiment lexicons. For example, (Toh and The goal of opinion mining or sentiment analysis is to iden- Wang, 2014) uses the syntactic categories from WordNet tify and classify texts according to the opinion, sentiment or as features for a CRF to extract aspects and WordNet re- emotion that they convey. It requires a good knowledge of lations such as antonymy and synonymy to extend an ex- the sentiment properties of each word. This properties can isting opinion lexicons. Similarly, (Agarwal et al., 2011) be either discovered automatically by machine learning al- look for synonyms in WordNet to find the polarity of words gorithms, or embedded into language resources. -
Using Prior Knowledge to Guide BERT's Attention in Semantic
Using Prior Knowledge to Guide BERT’s Attention in Semantic Textual Matching Tasks Tingyu Xia Yue Wang School of Artificial Intelligence, Jilin University School of Information and Library Science Key Laboratory of Symbolic Computation and Knowledge University of North Carolina at Chapel Hill Engineering, Jilin University [email protected] [email protected] Yuan Tian∗ Yi Chang∗ School of Artificial Intelligence, Jilin University School of Artificial Intelligence, Jilin University Key Laboratory of Symbolic Computation and Knowledge International Center of Future Science, Jilin University Engineering, Jilin University Key Laboratory of Symbolic Computation and Knowledge [email protected] Engineering, Jilin University [email protected] ABSTRACT 1 INTRODUCTION We study the problem of incorporating prior knowledge into a deep Measuring semantic similarity between two pieces of text is a fun- Transformer-based model, i.e., Bidirectional Encoder Representa- damental and important task in natural language understanding. tions from Transformers (BERT), to enhance its performance on Early works on this task often leverage knowledge resources such semantic textual matching tasks. By probing and analyzing what as WordNet [28] and UMLS [2] as these resources contain well- BERT has already known when solving this task, we obtain better defined types and relations between words and concepts. Recent understanding of what task-specific knowledge BERT needs the works have shown that deep learning models are more effective most and where it is most needed. The analysis further motivates on this task by learning linguistic knowledge from large-scale text us to take a different approach than most existing works. Instead of data and representing text (words and sentences) as continuous using prior knowledge to create a new training task for fine-tuning trainable embedding vectors [10, 17]. -
Ying Liu, Xi Yang
Liu, Y. & Yang, X. (2018). A similar legal case retrieval Liu & Yang system by multiple speech question and answer. In Proceedings of The 18th International Conference on Electronic Business (pp. 72-81). ICEB, Guilin, China, December 2-6. A Similar Legal Case Retrieval System by Multiple Speech Question and Answer (Full paper) Ying Liu, School of Computer Science and Information Engineering, Guangxi Normal University, China, [email protected] Xi Yang*, School of Computer Science and Information Engineering, Guangxi Normal University, China, [email protected] ABSTRACT In real life, on the one hand people often lack legal knowledge and legal awareness; on the other hand lawyers are busy, time- consuming, and expensive. As a result, a series of legal consultation problems cannot be properly handled. Although some legal robots can answer some problems about basic legal knowledge that are matched by keywords, they cannot do similar case retrieval and sentencing prediction according to the criminal facts described in natural language. To overcome the difficulty, we propose a similar case retrieval system based on natural language understanding. The system uses online speech synthesis of IFLYTEK and speech reading and writing technology, integrates natural language semantic processing technology and multiple rounds of question-and-answer dialogue mechanism to realise the legal knowledge question and answer with the memory-based context processing ability, and finally retrieves a case that is most similar to the criminal facts that the user consulted. After trial use, the system has a good level of human-computer interaction and high accuracy of information retrieval, which can largely satisfy people's consulting needs for legal issues. -
Untangling Semantic Similarity: Modeling Lexical Processing Experiments with Distributional Semantic Models
Untangling Semantic Similarity: Modeling Lexical Processing Experiments with Distributional Semantic Models. Farhan Samir Barend Beekhuizen Suzanne Stevenson Department of Computer Science Department of Language Studies Department of Computer Science University of Toronto University of Toronto, Mississauga University of Toronto ([email protected]) ([email protected]) ([email protected]) Abstract DSMs thought to instantiate one but not the other kind of sim- Distributional semantic models (DSMs) are substantially var- ilarity have been found to explain different priming effects on ied in the types of semantic similarity that they output. Despite an aggregate level (as opposed to an item level). this high variance, the different types of similarity are often The underlying conception of semantic versus associative conflated as a monolithic concept in models of behavioural data. We apply the insight that word2vec’s representations similarity, however, has been disputed (Ettinger & Linzen, can be used for capturing both paradigmatic similarity (sub- 2016; McRae et al., 2012), as has one of the main ways in stitutability) and syntagmatic similarity (co-occurrence) to two which it is operationalized (Gunther¨ et al., 2016). In this pa- sets of experimental findings (semantic priming and the effect of semantic neighbourhood density) that have previously been per, we instead follow another distinction, based on distri- modeled with monolithic conceptions of DSM-based seman- butional properties (e.g. Schutze¨ & Pedersen, 1993), namely, tic similarity. Using paradigmatic and syntagmatic similarity that between syntagmatically related words (words that oc- based on word2vec, we show that for some tasks and types of items the two types of similarity play complementary ex- cur in each other’s near proximity, such as drink–coffee), planatory roles, whereas for others, only syntagmatic similar- and paradigmatically related words (words that can be sub- ity seems to matter. -
How Latent Is Latent Semantic Analysis?
How Latent is Latent Semantic Analysis? Peter Wiemer-Hastings University of Memphis Department of Psychology Campus Box 526400 Memphis TN 38152-6400 [email protected]* Abstract In the late 1980's, a group at Bellcore doing re- search on information retrieval techniques developed a Latent Semantic Analysis (LSA) is a statisti• statistical, corpus-based method for retrieving texts. cal, corpus-based text comparison mechanism Unlike the simple techniques which rely on weighted that was originally developed for the task of matches of keywords in the texts and queries, their information retrieval, but in recent years has method, called Latent Semantic Analysis (LSA), cre• produced remarkably human-like abilities in a ated a high-dimensional, spatial representation of a cor• variety of language tasks. LSA has taken the pus and allowed texts to be compared geometrically. Test of English as a Foreign Language and per• In the last few years, several researchers have applied formed as well as non-native English speakers this technique to a variety of tasks including the syn• who were successful college applicants. It has onym section of the Test of English as a Foreign Lan• shown an ability to learn words at a rate sim• guage [Landauer et al., 1997], general lexical acquisi• ilar to humans. It has even graded papers as tion from text [Landauer and Dumais, 1997], selecting reliably as human graders. We have used LSA texts for students to read [Wolfe et al., 1998], judging as a mechanism for evaluating the quality of the coherence of student essays [Foltz et a/., 1998], and student responses in an intelligent tutoring sys• the evaluation of student contributions in an intelligent tem, and its performance equals that of human tutoring environment [Wiemer-Hastings et al., 1998; raters with intermediate domain knowledge. -
Measuring Semantic Similarity of Words Using Concept Networks
Measuring semantic similarity of words using concept networks Gabor´ Recski Eszter Iklodi´ Research Institute for Linguistics Dept of Automation and Applied Informatics Hungarian Academy of Sciences Budapest U of Technology and Economics H-1068 Budapest, Benczur´ u. 33 H-1117 Budapest, Magyar tudosok´ krt. 2 [email protected] [email protected] Katalin Pajkossy Andras´ Kornai Department of Algebra Institute for Computer Science Budapest U of Technology and Economics Hungarian Academy of Sciences H-1111 Budapest, Egry J. u. 1 H-1111 Budapest, Kende u. 13-17 [email protected] [email protected] Abstract using word embeddings and WordNet. In Sec- tion 3 we briefly introduce the 4lang resources We present a state-of-the-art algorithm and the formalism it uses for encoding the mean- for measuring the semantic similarity of ing of words as directed graphs of concepts, then word pairs using novel combinations of document our efforts to develop novel 4lang- word embeddings, WordNet, and the con- based similarity features. Besides improving the cept dictionary 4lang. We evaluate our performance of existing systems for measuring system on the SimLex-999 benchmark word similarity, the goal of the present project is to data. Our top score of 0.76 is higher than examine the potential of 4lang representations in any published system that we are aware of, representing non-trivial lexical relationships that well beyond the average inter-annotator are beyond the scope of word embeddings and agreement of 0.67, and close to the 0.78 standard linguistic ontologies. average correlation between a human rater Section 4 presents our results and pro- and the average of all other ratings, sug- vides rough error analysis. -
Navigating Dbpedia by Topic Tanguy Raynaud, Julien Subercaze, Delphine Boucard, Vincent Battu, Frederique Laforest
Fouilla: Navigating DBpedia by Topic Tanguy Raynaud, Julien Subercaze, Delphine Boucard, Vincent Battu, Frederique Laforest To cite this version: Tanguy Raynaud, Julien Subercaze, Delphine Boucard, Vincent Battu, Frederique Laforest. Fouilla: Navigating DBpedia by Topic. CIKM 2018, Oct 2018, Turin, Italy. hal-01860672 HAL Id: hal-01860672 https://hal.archives-ouvertes.fr/hal-01860672 Submitted on 23 Aug 2018 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. Fouilla: Navigating DBpedia by Topic Tanguy Raynaud, Julien Subercaze, Delphine Boucard, Vincent Battu, Frédérique Laforest Univ Lyon, UJM Saint-Etienne, CNRS, Laboratoire Hubert Curien UMR 5516 Saint-Etienne, France [email protected] ABSTRACT only the triples that concern this topic. For example, a user is inter- Navigating large knowledge bases made of billions of triples is very ested in Italy through the prism of Sports while another through the challenging. In this demonstration, we showcase Fouilla, a topical prism of Word War II. For each of these topics, the relevant triples Knowledge Base browser that offers a seamless navigational expe- of the Italy entity differ. In such circumstances, faceted browsing rience of DBpedia. We propose an original approach that leverages offers no solution to retrieve the entities relative to a defined topic both structural and semantic contents of Wikipedia to enable a if the knowledge graph does not explicitly contain an adequate topic-oriented filter on DBpedia entities. -
Document and Topic Models: Plsa
10/4/2018 Document and Topic Models: pLSA and LDA Andrew Levandoski and Jonathan Lobo CS 3750 Advanced Topics in Machine Learning 2 October 2018 Outline • Topic Models • pLSA • LSA • Model • Fitting via EM • pHITS: link analysis • LDA • Dirichlet distribution • Generative process • Model • Geometric Interpretation • Inference 2 1 10/4/2018 Topic Models: Visual Representation Topic proportions and Topics Documents assignments 3 Topic Models: Importance • For a given corpus, we learn two things: 1. Topic: from full vocabulary set, we learn important subsets 2. Topic proportion: we learn what each document is about • This can be viewed as a form of dimensionality reduction • From large vocabulary set, extract basis vectors (topics) • Represent document in topic space (topic proportions) 푁 퐾 • Dimensionality is reduced from 푤푖 ∈ ℤ푉 to 휃 ∈ ℝ • Topic proportion is useful for several applications including document classification, discovery of semantic structures, sentiment analysis, object localization in images, etc. 4 2 10/4/2018 Topic Models: Terminology • Document Model • Word: element in a vocabulary set • Document: collection of words • Corpus: collection of documents • Topic Model • Topic: collection of words (subset of vocabulary) • Document is represented by (latent) mixture of topics • 푝 푤 푑 = 푝 푤 푧 푝(푧|푑) (푧 : topic) • Note: document is a collection of words (not a sequence) • ‘Bag of words’ assumption • In probability, we call this the exchangeability assumption • 푝 푤1, … , 푤푁 = 푝(푤휎 1 , … , 푤휎 푁 ) (휎: permutation) 5 Topic Models: Terminology (cont’d) • Represent each document as a vector space • A word is an item from a vocabulary indexed by {1, … , 푉}. We represent words using unit‐basis vectors.