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Author
Title Multilabel text classification of public procurements using deep learning intent detection
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Publication Date
Discipline/Department Mathematical Statistics
University/Publisher KTH
Abstract

Textual data is one of the most widespread forms of data and the amount of such data available in the world increases at a rapid rate. Text can be understood as either a sequence of characters or words, where the latter approach is the most common. With the breakthroughs within the area of applied artificial intelligence in recent years, more and more tasks are aided by automatic processing of text in various applications. The models introduced in the following sections rely on deep-learning sequence-processing in order to process and text to produce a regression algorithm for classification of what the text input refers to. We investigate and compare the performance of several model architectures along with different hyperparameters. The data set was provided by e-Avrop, a Swedish company which hosts a web platform for posting and bidding of public procurements. It consists of titles and descriptions of Swedish public procurements posted on the website of e-Avrop, along with the respective category/categories of each text. When the texts are described by several categories (multi label case) we suggest a deep learning sequence-processing regression algorithm, where a set of deep learning classifiers are used. Each model uses one of the several labels in the multi label case, along with the text input to produce a set of text - label observation pairs. The goal becomes to investigate whether these classifiers can carry out different levels of intent, an intent which should theoretically be imposed by the different training data sets used by each of the individual deep learning classifiers.

Data i form av text är en av de mest utbredda formerna av data och mängden tillgänglig textdata runt om i världen ökar i snabb takt. Text kan tolkas som en följd av bokstäver eller ord, där tolkning av text i form av ordföljder är absolut vanligast. Genombrott inom artificiell intelligens under de senaste åren har medfört att fler och fler arbetsuppgifter med koppling till text assisteras av automatisk textbearbetning. Modellerna som introduceras i denna uppsats är baserade på djupa artificiella neuronnät med sekventiell bearbetning av textdata, som med hjälp av regression förutspår tillhörande ämnesområde för den inmatade texten. Flera modeller och tillhörande hyperparametrar utreds och jämförs enligt prestanda. Datamängden som använts är tillhandahållet av e-Avrop, ett svenskt företag som erbjuder en webbtjänst för offentliggörande och budgivning av offentliga upphandlingar. Datamängden består av titlar, beskrivningar samt tillhörande ämneskategorier för offentliga upphandlingar inom Sverige, tagna från e-Avrops webtjänst. När texterna är märkta med ett flertal kategorier, föreslås en algoritm baserad på ett djupt artificiellt neuronnät med sekventiell bearbetning, där en mängd klassificeringsmodeller används. Varje sådan modell använder en av de märkta kategorierna tillsammans med den tillhörande texten, som skapar en mängd av text - kategori par. Målet är att utreda huruvida dessa klassificerare kan uppvisa olika…

Subjects/Keywords Natural language processing; text classification; deep learning; applied mathematics; recurrent neural network; word embedding; Maskininlärning; textklassificering; artificiella neruonnät; tillämpad matematik; Probability Theory and Statistics; Sannolikhetsteori och statistik
Language en
Country of Publication se
Record ID oai:DiVA.org:kth-252558
Repository diva
Date Indexed 2020-01-03

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…1 2 2 2 3 3 4 5 5 6 2 Extended background 2.1 Natural language processing . . . . . . . . . 2.2 Word embedding . . . . . . . . . . . . . . . 2.2.1 Word embedding from n-grams . . . 2.3 Artificial Neural network . . . . . . . . . . . 2.3.1 Fully…

…was conceived independently [2]. Written language enables humans to capture and convey complex messages which in turn allows for spreading of information and knowledge around the world. Today, and since the beginning of the first written text

…with the interactions between human (natural) languages and computers, in particular processing and analyzing large amounts natural language data. In many industries, effective automated text classification could be of great use to quickly…

…sort through and analyze large amounts of data, assisting humans in such tasks or perhaps some day completely replacing text classification tasks which rely on human analysis. Recently, state of the art text classification algorithms has often relied on…

…artificial neural network (ANN). In this thesis, the task of automating manual text classification was implemented to handle the problem of categorization of public procurements in Sweden. Using the CPV code (Common public Procurement…

…purpose of this thesis is to propose a way of multi label text classification with deep learning and pre-trained word embeddings. By choosing different training sets 3 CHAPTER 1. INTRODUCTION for several ANN classifiers, the ANN’s focus on different…

…parts of the data and have different intent. The main research question is therefore: Can we construct a set of deep learning text classifiers where each classifier captures different characteristics of a text and provides different behaviour of…

…characteristics of a text. 2. Applicability: Applicability means the right amount of classifiers, observations and labels to be used. Evaluating different deep learning models according to performance on the particular data set of public procurements and their…

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