On the Use of Parsing for Named Entity Recognition
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Semantic Parsing for Single-Relation Question Answering
Semantic Parsing for Single-Relation Question Answering Wen-tau Yih Xiaodong He Christopher Meek Microsoft Research Redmond, WA 98052, USA scottyih,xiaohe,meek @microsoft.com { } Abstract the same question. That is to say that the problem of mapping from a question to a particular relation We develop a semantic parsing framework and entity in the KB is non-trivial. based on semantic similarity for open do- In this paper, we propose a semantic parsing main question answering (QA). We focus framework tailored to single-relation questions. on single-relation questions and decom- At the core of our approach is a novel semantic pose each question into an entity men- similarity model using convolutional neural net- tion and a relation pattern. Using convo- works. Leveraging the question paraphrase data lutional neural network models, we mea- mined from the WikiAnswers corpus by Fader et sure the similarity of entity mentions with al. (2013), we train two semantic similarity mod- entities in the knowledge base (KB) and els: one links a mention from the question to an the similarity of relation patterns and re- entity in the KB and the other maps a relation pat- lations in the KB. We score relational tern to a relation. The answer to the question can triples in the KB using these measures thus be derived by finding the relation–entity triple and select the top scoring relational triple r(e1, e2) in the KB and returning the entity not to answer the question. When evaluated mentioned in the question. By using a general se- on an open-domain QA task, our method mantic similarity model to match patterns and re- achieves higher precision across different lations, as well as mentions and entities, our sys- recall points compared to the previous ap- tem outperforms the existing rule learning system, proach, and can improve F1 by 7 points. -
Semantic Parsing with Semi-Supervised Sequential Autoencoders
Semantic Parsing with Semi-Supervised Sequential Autoencoders Toma´sˇ Kociskˇ y´†‡ Gabor´ Melis† Edward Grefenstette† Chris Dyer† Wang Ling† Phil Blunsom†‡ Karl Moritz Hermann† †Google DeepMind ‡University of Oxford tkocisky,melisgl,etg,cdyer,lingwang,pblunsom,kmh @google.com { } Abstract This is common in situations where we encode nat- ural language into a logical form governed by some We present a novel semi-supervised approach grammar or database. for sequence transduction and apply it to se- mantic parsing. The unsupervised component While such an autoencoder could in principle is based on a generative model in which latent be constructed by stacking two sequence transduc- sentences generate the unpaired logical forms. ers, modelling the latent variable as a series of dis- We apply this method to a number of semantic crete symbols drawn from multinomial distributions parsing tasks focusing on domains with lim- ited access to labelled training data and ex- creates serious computational challenges, as it re- tend those datasets with synthetically gener- quires marginalising over the space of latent se- ated logical forms. quences Σx∗. To avoid this intractable marginalisa- tion, we introduce a novel differentiable alternative for draws from a softmax which can be used with 1 Introduction the reparametrisation trick of Kingma and Welling (2014). Rather than drawing a discrete symbol in Neural approaches, in particular attention-based Σx from a softmax, we draw a distribution over sym- sequence-to-sequence models, have shown great bols from a logistic-normal distribution at each time promise and obtained state-of-the-art performance step. These serve as continuous relaxations of dis- for sequence transduction tasks including machine crete samples, providing a differentiable estimator translation (Bahdanau et al., 2015), syntactic con- of the expected reconstruction log likelihood. -
SQL Generation from Natural Language: a Sequence-To- Sequence Model Powered by the Transformers Architecture and Association Rules
Journal of Computer Science Original Research Paper SQL Generation from Natural Language: A Sequence-to- Sequence Model Powered by the Transformers Architecture and Association Rules 1,2Youssef Mellah, 1Abdelkader Rhouati, 1El Hassane Ettifouri, 2Toumi Bouchentouf and 2Mohammed Ghaouth Belkasmi 1NovyLab Research, Novelis, Paris, France 2Mohammed First University Oujda, National School of Applied Sciences, LaRSA/SmartICT Lab, Oujda, Morocco Article history Abstract: Using Natural Language (NL) to interacting with relational Received: 12-03-2021 databases allows users from any background to easily query and analyze Revised: 21-04-2021 large amounts of data. This requires a system that understands user Accepted: 28-04-2021 questions and automatically converts them into structured query language such as SQL. The best performing Text-to-SQL systems use supervised Corresponding Author: Youssef Mellah learning (usually formulated as a classification problem) by approaching NovyLab Research, Novelis, this task as a sketch-based slot-filling problem, or by first converting Paris, France questions into an Intermediate Logical Form (ILF) then convert it to the And corresponding SQL query. However, non-supervised modeling that LARSA Laboratory, ENSAO, directly converts questions to SQL queries has proven more difficult. In Mohammed First University, this sense, we propose an approach to directly translate NL questions into Oujda, Morocco SQL statements. In this study, we present a Sequence-to-Sequence Email: [email protected] (Seq2Seq) parsing model for the NL to SQL task, powered by the Transformers Architecture exploring the two Language Models (LM): Text-To-Text Transfer Transformer (T5) and the Multilingual pre-trained Text-To-Text Transformer (mT5). Besides, we adopt the transformation- based learning algorithm to update the aggregation predictions based on association rules. -
Semantic Parsing with Neural Hybrid Trees
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17) Semantic Parsing with Neural Hybrid Trees Raymond Hendy Susanto, Wei Lu Singapore University of Technology and Design 8 Somapah Road, Singapore 487372 {raymond susanto, luwei}@sutd.edu.sg Abstract QUERY : answer(STATE) We propose a neural graphical model for parsing natural lan- STATE : exclude(STATE, STATE) guage sentences into their logical representations. The graph- ical model is based on hybrid tree structures that jointly rep- : state( ) : next to( ) resent both sentences and semantics. Learning and decoding STATE all STATE STATE are done using efficient dynamic programming algorithms. The model is trained under a discriminative setting, which STATE : stateid(STATENAME) allows us to incorporate a rich set of features. Hybrid tree structures have shown to achieve state-of-the-art results on STATENAME :(tx ) standard semantic parsing datasets. In this work, we propose a novel model that incorporates a rich, nonlinear featurization Which states do not border Texas ? by a feedforward neural network. The error signals are com- puted with respect to the conditional random fields (CRFs) objective using an inside-outside algorithm, which are then Figure 1: An example tree-structured semantic representa- backpropagated to the neural network. We demonstrate that tion and its corresponding natural language sentence. by combining the strengths of the exact global inference in the hybrid tree models and the power of neural networks to extract high level features, our model is able to achieve new explicit labelings between words and meaning representa- state-of-the-art results on standard benchmark datasets across tion tokens are not available in the training data. -
Learning Logical Representations from Natural Languages with Weak Supervision and Back-Translation
Learning Logical Representations from Natural Languages with Weak Supervision and Back-Translation Kaylin Hagopiany∗ Qing Wangy Georgia Institute of Technology IBM Research Atlanta, GA, USA Yorktown Heights, NY, USA [email protected] [email protected] Tengfei Ma Yupeng Gao Lingfei Wu IBM Research AI IBM Research AI IBM Research AI Yorktown Heights, NY, USA Yorktown Heights, NY, USA Yorktown Heights, NY, USA [email protected] [email protected] [email protected] Abstract Semantic parsing with strong supervision requires a large parallel corpus for train- ing, whose production may be impractical due to the excessive human labour needed for annotation. Recently, weakly supervised approaches based on reinforce- ment learning have been studied, wherein only denotations, but not the logical forms, are available. However, reinforcement learning approaches based on, e.g., policy gradient training, suffer reward sparsity and progress slowly. In this work, we explore enriching the policy gradient framework with a back-translation mecha- nism that boosts the reward signal. In this vein, we jointly optimize the expected reward of obtaining the correct denotation and the likelihood of reconstructing the natural language utterance. Initial experimental results suggest the proposed method slightly outperforms a state-of-the-art semantic parser trained with policy gradient. Introduction Semantic parsing is the task of converting natural language utterances to logical representations that a machine can understand and execute as problem solving steps. There has recently been a surge of interest in developing machine learning methods for semantic parsing, owing to the creation of large- scale text corpora. There has also been a proliferation of logical representations used in this setting, including logical forms [28], domain specific languages [26], python code [24], SQL queries [31], and bash commands [20]. -
Review on Parse Tree Generation in Natural Language Processing
International Journal of Application or Innovation in Engineering & Management (IJAIEM) Web Site: www.ijaiem.org Email: [email protected], [email protected] ISSN 2319 - 4847 Special Issue for National Conference On Recent Advances in Technology and Management for Integrated Growth 2013 (RATMIG 2013) Review on Parse Tree Generation in Natural Language Processing Manoj K. Vairalkar1 1 Assistant Professor, Department of Information Technology, Gurunanak Institute of Engineering & Technology Nagpur University, Maharashtra, India [email protected] ABSTRACT Natural Language Processing is a field of computer science concerned with the interactions between computers and human languagesThe syntax and semantics plays very important role in Natural Language Processing. Processing natural language such as English has always been one of the central research issues of artificial intelligence. The concept of parsing is very important. In this, the sentence gets parsed into Noun Phrase and Verb phrase modules. If there are further decomposition then these modules further gets divided. In this way, it helps to learn the meaning of word. Keywords:Parse tree, Parser, syntax, semantics etc 1.Introduction The ultimate objective of natural language processing is to allow people to communicate with computers in much the same way they communicate with each other. Natural language processing removes one of the key obstacles that keep some people from using computers. More specifically, natural language processing facilitates access to a database or a knowledge base, provides a friendly user interface, facilitates language translation and conversion, and increases user productivity by supporting English-like input. To parse a sentence, first upon the syntax and semantics of the sentence comes into consideration. -
Building a Treebank for French
Building a treebank for French £ £¥ Anne Abeillé£ , Lionel Clément , Alexandra Kinyon ¥ £ TALaNa, Université Paris 7 University of Pennsylvania 75251 Paris cedex 05 Philadelphia FRANCE USA abeille, clement, [email protected] Abstract Very few gold standard annotated corpora are currently available for French. We present an ongoing project to build a reference treebank for French starting with a tagged newspaper corpus of 1 Million words (Abeillé et al., 1998), (Abeillé and Clément, 1999). Similarly to the Penn TreeBank (Marcus et al., 1993), we distinguish an automatic parsing phase followed by a second phase of systematic manual validation and correction. Similarly to the Prague treebank (Hajicova et al., 1998), we rely on several types of morphosyntactic and syntactic annotations for which we define extensive guidelines. Our goal is to provide a theory neutral, surface oriented, error free treebank for French. Similarly to the Negra project (Brants et al., 1999), we annotate both constituents and functional relations. 1. The tagged corpus pronoun (= him ) or a weak clitic pronoun (= to him or to As reported in (Abeillé and Clément, 1999), we present her), plus can either be a negative adverb (= not any more) the general methodology, the automatic tagging phase, the or a simple adverb (= more). Inflectional morphology also human validation phase and the final state of the tagged has to be annotated since morphological endings are impor- corpus. tant for gathering constituants (based on agreement marks) and also because lots of forms in French are ambiguous 1.1. Methodology with respect to mode, person, number or gender. For exam- 1.1.1. -
Sentiment Analysis for Multilingual Corpora
Sentiment Analysis for Multilingual Corpora Svitlana Galeshchuk Julien Jourdan Ju Qiu Governance Analytics, PSL Research University / Governance Analytics, PSL Research University / University Paris Dauphine PSL Research University / University Paris Dauphine Place du Marechal University Paris Dauphine Place du Marechal de Lattre de Tassigny, Place du Marechal de Lattre de Tassigny, 75016 Paris, France de Lattre de Tassigny, 75016 Paris, France [email protected] 75016 Paris, France [email protected] [email protected] Abstract for different languages without polarity dictionar- ies using relatively small datasets. The paper presents a generic approach to the supervised sentiment analysis of social media We address the challenges for sentiment analy- content in foreign languages. The method pro- sis in Slavic languages by using the averaged vec- poses translating documents from the origi- tors for each word in a document translated in nal language to English with Google’s Neural English. The vectors derive from the Word2Vec Translation Model. The resulted texts are then model pre-trained on Google news. converted to vectors by averaging the vectorial Researchers tend to use the language-specific representation of words derived from a pre- pre-trained Word2Vec models (e.g., Word2Vec trained Word2Vec English model. Testing the approach with several machine learning meth- model pre-trained on Wikipedia corpus in Greek, ods on Polish, Slovenian and Croatian Twitter Swedish, etc.). On the contrary, we propose ben- corpora returns up to 86 % of classification ac- efiting from Google’s Neural Translation Model curacy on the out-of-sample data. translating the texts from other languages to En- glish. -
Named Entity Extraction for Knowledge Graphs: a Literature Overview
Received December 12, 2019, accepted January 7, 2020, date of publication February 14, 2020, date of current version February 25, 2020. Digital Object Identifier 10.1109/ACCESS.2020.2973928 Named Entity Extraction for Knowledge Graphs: A Literature Overview TAREQ AL-MOSLMI , MARC GALLOFRÉ OCAÑA , ANDREAS L. OPDAHL, AND CSABA VERES Department of Information Science and Media Studies, University of Bergen, 5007 Bergen, Norway Corresponding author: Tareq Al-Moslmi ([email protected]) This work was supported by the News Angler Project through the Norwegian Research Council under Project 275872. ABSTRACT An enormous amount of digital information is expressed as natural-language (NL) text that is not easily processable by computers. Knowledge Graphs (KG) offer a widely used format for representing information in computer-processable form. Natural Language Processing (NLP) is therefore needed for mining (or lifting) knowledge graphs from NL texts. A central part of the problem is to extract the named entities in the text. The paper presents an overview of recent advances in this area, covering: Named Entity Recognition (NER), Named Entity Disambiguation (NED), and Named Entity Linking (NEL). We comment that many approaches to NED and NEL are based on older approaches to NER and need to leverage the outputs of state-of-the-art NER systems. There is also a need for standard methods to evaluate and compare named-entity extraction approaches. We observe that NEL has recently moved from being stepwise and isolated into an integrated process along two dimensions: the first is that previously sequential steps are now being integrated into end-to-end processes, and the second is that entities that were previously analysed in isolation are now being lifted in each other's context. -
A Sequence to Sequence Architecture for Task-Oriented Semantic Parsing
Don’t Parse, Generate! A Sequence to Sequence Architecture for Task-Oriented Semantic Parsing Subendhu Rongali∗ Luca Soldaini University of Massachusetts Amherst Amazon Alexa Search Amherst, MA, USA Manhattan Beach, CA, USA [email protected] [email protected] Emilio Monti Wael Hamza Amazon Alexa Amazon Alexa AI Cambridge, UK New York, NY, USA [email protected] [email protected] ABSTRACT ArtistName Virtual assistants such as Amazon Alexa, Apple Siri, and Google Play the song don't stop believin by Journey Assistant often rely on a semantic parsing component to understand PlaySongIntent SongName which action(s) to execute for an utterance spoken by its users. Traditionally, rule-based or statistical slot-filling systems have been Intent: PlaySongIntent used to parse “simple” queries; that is, queries that contain a single Slots: SongName(don't stop believin), action and can be decomposed into a set of non-overlapping entities. ArtistName(Journey) More recently, shift-reduce parsers have been proposed to process more complex utterances. These methods, while powerful, impose specific limitations on the type of queries that can be parsed; namely, Figure 1: Semantic parsing of a “simple” query. Simple they require a query to be representable as a parse tree. queries define single action (intent) and can be decomposed In this work, we propose a unified architecture based on Se- into a set of non-overlapping entities (slots). quence to Sequence models and Pointer Generator Network to handle both simple and complex queries. Unlike other works, our 1 INTRODUCTION approach does not impose any restriction on the semantic parse Adoption of intelligent voice assistants such as Amazon Alexa, schema. -
Text-Based Relation Extraction from the Web”
ΤΜΗΜΑ ΠΛΗΡΟΦΟΡΙΚΗΣ ΜΕΤΑΠΤΥΧΙΑΚΟ ΔΙΠΛΩΜΑ ΕΙΔΙΚΕΥΣΗΣ (MSc) στα ΠΛΗΡΟΦΟΡΙΑΚΑ ΣΥΣΤΗΜΑΤΑ ΔΙΠΛΩΜΑΤΙKH ΕΡΓΑΣΙΑ “Εξαγωγή Σχέσεων από Κείμενα του Παγκόσμιου Ιστού” Τσιμπίδη Δωροθέα-Κωνσταντίνα M315009 ΑΘΗΝΑ, ΦΕΒΡΟΥΑΡΙΟΣ 2017 MASTER THESIS “Text-based Relation Extraction from the Web” Tsimpidi Dorothea-Konstantina SUPERVISORS: Anastasia Krithara, Research Associate, NCSR “Demokritos” Ion Androutsopoulos, Associate Professor, AUEB February 2017 Text-based Relation Extraction from the Web ACKNOWLEDGMENTS I would first like to thank my thesis advisor Anastasia Krithara for the constant guidance that she offered me and for allowing me to grow as a research scientist in the area of Information Extraction. I would also like to thank the AUEB Natural Language Processing Group and especially professor Ion Androutsopoulos for the valuable feedback every time it was needed. D. – K. Tsimpidi 4 Text-based Relation Extraction from the Web TABLE OF CONTENTS LIST OF TABLES……………………………………..………………………………………6 LIST OF FIGURES……………………………………………………………………………7 1. Introduction……………………………………………………………………………...8 2. State of the Art………………………………………………………………………….9 2.1 Traditional Relation Extraction………………………………………...……………9 2.2 Open Relation Extraction………………………….……………………...………….9 2.2.1 Existing Approaches………………………………………………………...10 2.2.2 Open Relation Extraction and Conditional Random Fields……………..15 2.3 Problem Statement…………………………………………………………………..16 3. Methodology……………………………………………………………………………18 3.1 Rule-based System…………………………………………………………………..18 3.1.1 Rules identifying Named Entities…………………………………………26 -
A Comparative Study of Structured Prediction Methods for Sequence Labeling
GraduadoGrad ado en Matemáticas e Informática Universidad Politécnica de Madrid Escuela Técnica Superior de Ingenieros Informáticos TRABAJO FIN DE GRADO A comparative study of structured prediction methods for sequence labeling Autor: Aitor Palacios Cuesta Director: Michael Minock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bstract Some machine learning tasks have a complex output, rather than a real number or a class.