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This paper describes performance of CRF based systems for Named Entity Recognition (NER) in Indian language as a part of ICON 2013 shared task. In this task we have considered a set of language independent features for all the languages. Only for English a language specific feature, i.e. capitalization, has been added.... | CRF-based Named Entity Recognition @ICON 2013 | 1,800 |
Machine Translation is one of the major oldest and the most active research area in Natural Language Processing. Currently, Statistical Machine Translation (SMT) dominates the Machine Translation research. Statistical Machine Translation is an approach to Machine Translation which uses models to learn translation patte... | Improving the Performance of English-Tamil Statistical Machine
Translation System using Source-Side Pre-Processing | 1,801 |
The Linguistic Annotation Framework (LAF) provides a general, extensible stand-off markup system for corpora. This paper discusses LAF-Fabric, a new tool to analyse LAF resources in general with an extension to process the Hebrew Bible in particular. We first walk through the history of the Hebrew Bible as text databas... | LAF-Fabric: a data analysis tool for Linguistic Annotation Framework
with an application to the Hebrew Bible | 1,802 |
We present an open source morphological analyzer for Japanese nouns, verbs and adjectives. The system builds upon the morphological analyzing capabilities of MeCab to incorporate finer details of classification such as politeness, tense, mood and voice attributes. We implemented our analyzer in the form of a finite sta... | A Morphological Analyzer for Japanese Nouns, Verbs and Adjectives | 1,803 |
Neural language models learn word representations that capture rich linguistic and conceptual information. Here we investigate the embeddings learned by neural machine translation models. We show that translation-based embeddings outperform those learned by cutting-edge monolingual models at single-language tasks requi... | Not All Neural Embeddings are Born Equal | 1,804 |
This paper proposes a methodology to prepare corpora in Arabic language from online social network (OSN) and review site for Sentiment Analysis (SA) task. The paper also proposes a methodology for generating a stopword list from the prepared corpora. The aim of the paper is to investigate the effect of removing stopwor... | Corpora Preparation and Stopword List Generation for Arabic data in
Social Network | 1,805 |
Word alignment is an important natural language processing task that indicates the correspondence between natural languages. Recently, unsupervised learning of log-linear models for word alignment has received considerable attention as it combines the merits of generative and discriminative approaches. However, a major... | Contrastive Unsupervised Word Alignment with Non-Local Features | 1,806 |
Statistics pedagogy values using a variety of examples. Thanks to text resources on the Web, and since statistical packages have the ability to analyze string data, it is now easy to use language-based examples in a statistics class. Three such examples are discussed here. First, many types of wordplay (e.g., crossword... | Language-based Examples in the Statistics Classroom | 1,807 |
Hybrid approaches for automatic vowelization of Arabic texts are presented in this article. The process is made up of two modules. In the first one, a morphological analysis of the text words is performed using the open source morphological Analyzer AlKhalil Morpho Sys. Outputs for each word analyzed out of context, ar... | Hybrid approaches for automatic vowelization of Arabic texts | 1,808 |
Euphonic conjunctions (sandhis) form a very important aspect of Sanskrit morphology and phonology. The traditional and modern methods of studying about euphonic conjunctions in Sanskrit follow different methodologies. The former involves a rigorous study of the Paninian system embodied in Panini's Ashtadhyayi, while th... | An Ontology for Comprehensive Tutoring of Euphonic Conjunctions of
Sanskrit Grammar | 1,809 |
Natural logic offers a powerful relational conception of meaning that is a natural counterpart to distributed semantic representations, which have proven valuable in a wide range of sophisticated language tasks. However, it remains an open question whether it is possible to train distributed representations to support ... | Learning Distributed Word Representations for Natural Logic Reasoning | 1,810 |
We study the performance of Arabic text classification combining various techniques: (a) tfidf vs. dependency syntax, for feature selection and weighting; (b) class association rules vs. support vector machines, for classification. The Arabic text is used in two forms: rootified and lightly stemmed. The results we obta... | Arabic Language Text Classification Using Dependency Syntax-Based
Feature Selection | 1,811 |
This paper describes our resource-building results for an eight-week JHU Human Language Technology Center of Excellence Summer Camp for Applied Language Exploration (SCALE-2009) on Semantically-Informed Machine Translation. Specifically, we describe the construction of a modality annotation scheme, a modality lexicon, ... | A Modality Lexicon and its use in Automatic Tagging | 1,812 |
Applying natural language processing for mining and intelligent information access to tweets (a form of microblog) is a challenging, emerging research area. Unlike carefully authored news text and other longer content, tweets pose a number of new challenges, due to their short, noisy, context-dependent, and dynamic nat... | Analysis of Named Entity Recognition and Linking for Tweets | 1,813 |
We describe a paradigm for combining manual and automatic error correction of noisy structured lexicographic data. Modifications to the structure and underlying text of the lexicographic data are expressed in a simple, interpreted programming language. Dictionary Manipulation Language (DML) commands identify nodes by u... | Correcting Errors in Digital Lexicographic Resources Using a Dictionary
Manipulation Language | 1,814 |
Social media texts are significant information sources for several application areas including trend analysis, event monitoring, and opinion mining. Unfortunately, existing solutions for tasks such as named entity recognition that perform well on formal texts usually perform poorly when applied to social media texts. I... | Experiments to Improve Named Entity Recognition on Turkish Tweets | 1,815 |
In this article, we describe an approach for automatic detection of noun-adjective agreement errors in Bulgarian texts by explaining the necessary steps required to develop a simple Java-based language processing application. For this purpose, we use the GATE language processing framework, which is capable of analyzing... | On Detecting Noun-Adjective Agreement Errors in Bulgarian Language Using
GATE | 1,816 |
Suicide is among the leading causes of death in China. However, technical approaches toward preventing suicide are challenging and remaining under development. Recently, several actual suicidal cases were preceded by users who posted microblogs with suicidal ideation to Sina Weibo, a Chinese social media network akin t... | Detecting Suicidal Ideation in Chinese Microblogs with Psychological
Lexicons | 1,817 |
The word2vec model and application by Mikolov et al. have attracted a great amount of attention in recent two years. The vector representations of words learned by word2vec models have been shown to carry semantic meanings and are useful in various NLP tasks. As an increasing number of researchers would like to experim... | word2vec Parameter Learning Explained | 1,818 |
The mathematical representation of semantics is a key issue for Natural Language Processing (NLP). A lot of research has been devoted to finding ways of representing the semantics of individual words in vector spaces. Distributional approaches --- meaning distributed representations that exploit co-occurrence statistic... | Distributed Representations for Compositional Semantics | 1,819 |
The paper aims to show how an application can be developed that converts the English language into the Punjabi Language, and the same application can convert the Text to Speech(TTS) i.e. pronounce the text. This application can be really beneficial for those with special needs. | A Text to Speech (TTS) System with English to Punjabi Conversion | 1,820 |
Vector space word representations are learned from distributional information of words in large corpora. Although such statistics are semantically informative, they disregard the valuable information that is contained in semantic lexicons such as WordNet, FrameNet, and the Paraphrase Database. This paper proposes a met... | Retrofitting Word Vectors to Semantic Lexicons | 1,821 |
The ability to extract public opinion from web portals such as review sites, social networks and blogs will enable companies and individuals to form a view, an attitude and make decisions without having to do lengthy and costly researches and surveys. In this paper machine learning techniques are used for determining t... | Opinion mining of text documents written in Macedonian language | 1,822 |
A graphical language addresses the need to communicate medical information in a synthetic way. Medical concepts are expressed by icons conveying fast visual information about patients' current state or about the known effects of drugs. In order to increase the visual language's acceptance and usability, a natural langu... | Using graph transformation algorithms to generate natural language
equivalents of icons expressing medical concepts | 1,823 |
In this paper we analyse network motifs in the co-occurrence directed networks constructed from five different texts (four books and one portal) in the Croatian language. After preparing the data and network construction, we perform the network motif analysis. We analyse the motif frequencies and Z-scores in the five n... | Network Motifs Analysis of Croatian Literature | 1,824 |
Semantic parsing has made significant progress, but most current semantic parsers are extremely slow (CKY-based) and rather primitive in representation. We introduce three new techniques to tackle these problems. First, we design the first linear-time incremental shift-reduce-style semantic parsing algorithm which is m... | Type-Driven Incremental Semantic Parsing with Polymorphism | 1,825 |
This paper describes pre-processing phase of ontology graph generation system from Punjabi text documents of different domains. This research paper focuses on pre-processing of Punjabi text documents. Pre-processing is structured representation of the input text. Pre-processing of ontology graph generation includes all... | Pre-processing of Domain Ontology Graph Generation System in Punjabi | 1,826 |
We present a method for coarse-grained cross-lingual alignment of comparable texts: segments consisting of contiguous paragraphs that discuss the same theme (e.g. history, economy) are aligned based on induced multilingual topics. The method combines three ideas: a two-level LDA model that filters out words that do not... | Coarse-grained Cross-lingual Alignment of Comparable Texts with Topic
Models and Encyclopedic Knowledge | 1,827 |
The functional approach to compositional distributional semantics considers transitive verbs to be linear maps that transform the distributional vectors representing nouns into a vector representing a sentence. We conduct an initial investigation that uses a matrix consisting of the parameters of a logistic regression ... | Using Sentence Plausibility to Learn the Semantics of Transitive Verbs | 1,828 |
Many tasks in Natural Language Processing involve recognizing lexical entailment. Two different approaches to this problem have been proposed recently that are quite different from each other. The first is an asymmetric similarity measure designed to give high scores when the contexts of the narrower term in the entail... | Tiered Clustering to Improve Lexical Entailment | 1,829 |
In the following article we elected to study with NooJ the lexis of a 17 th century text, Mary Astell's seminal essay, A Serious Proposal to the Ladies, part I, published in 1694. We first focused on the semantics to see how Astell builds her vindication of the female sex, which words she uses to sensitise women to the... | Mary Astell's words in A Serious Proposal to the Ladies (part I), a
lexicographic inquiry with NooJ | 1,830 |
Answer sentence selection is the task of identifying sentences that contain the answer to a given question. This is an important problem in its own right as well as in the larger context of open domain question answering. We propose a novel approach to solving this task via means of distributed representations, and lea... | Deep Learning for Answer Sentence Selection | 1,831 |
Entity type tagging is the task of assigning category labels to each mention of an entity in a document. While standard systems focus on a small set of types, recent work (Ling and Weld, 2012) suggests that using a large fine-grained label set can lead to dramatic improvements in downstream tasks. In the absence of lab... | Context-Dependent Fine-Grained Entity Type Tagging | 1,832 |
Neural machine translation, a recently proposed approach to machine translation based purely on neural networks, has shown promising results compared to the existing approaches such as phrase-based statistical machine translation. Despite its recent success, neural machine translation has its limitation in handling a l... | On Using Very Large Target Vocabulary for Neural Machine Translation | 1,833 |
Synonym extraction is an important task in natural language processing and often used as a submodule in query expansion, question answering and other applications. Automatic synonym extractor is highly preferred for large scale applications. Previous studies in synonym extraction are most limited to small scale dataset... | Practice in Synonym Extraction at Large Scale | 1,834 |
Attributes of words and relations between two words are central to numerous tasks in Artificial Intelligence such as knowledge representation, similarity measurement, and analogy detection. Often when two words share one or more attributes in common, they are connected by some semantic relations. On the other hand, if ... | Learning Word Representations from Relational Graphs | 1,835 |
Universal Grammar (UG) theory has been one of the most important research topics in linguistics since introduced five decades ago. UG specifies the restricted set of languages learnable by human brain, and thus, many researchers believe in its biological roots. Numerous empirical studies of neurobiological and cognitiv... | Rediscovering the Alphabet - On the Innate Universal Grammar | 1,836 |
Quantitative linguistics has been allowed, in the last few decades, within the admittedly blurry boundaries of the field of complex systems. A growing host of applied mathematicians and statistical physicists devote their efforts to disclose regularities, correlations, patterns, and structural properties of language st... | Statistical Patterns in Written Language | 1,837 |
In this paper, we propose a new approach to construct a system of transformation rules for the Part-of-Speech (POS) tagging task. Our approach is based on an incremental knowledge acquisition method where rules are stored in an exception structure and new rules are only added to correct the errors of existing rules; th... | A Robust Transformation-Based Learning Approach Using Ripple Down Rules
for Part-of-Speech Tagging | 1,838 |
We investigate the hypothesis that word representations ought to incorporate both distributional and relational semantics. To this end, we employ the Alternating Direction Method of Multipliers (ADMM), which flexibly optimizes a distributional objective on raw text and a relational objective on WordNet. Preliminary res... | Incorporating Both Distributional and Relational Semantics in Word
Representations | 1,839 |
We study sentiment analysis beyond the typical granularity of polarity and instead use Plutchik's wheel of emotions model. We introduce RBEM-Emo as an extension to the Rule-Based Emission Model algorithm to deduce such emotions from human-written messages. We evaluate our approach on two different datasets and compare ... | Rule-based Emotion Detection on Social Media: Putting Tweets on
Plutchik's Wheel | 1,840 |
Recent works on word representations mostly rely on predictive models. Distributed word representations (aka word embeddings) are trained to optimally predict the contexts in which the corresponding words tend to appear. Such models have succeeded in capturing word similarties as well as semantic and syntactic regulari... | Rehabilitation of Count-based Models for Word Vector Representations | 1,841 |
In this work, automatic analysis of themes contained in a large corpora of judgments from public procurement domain is performed. The employed technique is unsupervised latent Dirichlet allocation (LDA). In addition, it is proposed, to use LDA in conjunction with recently developed method of unsupervised keyword extrac... | Application of Topic Models to Judgments from Public Procurement Domain | 1,842 |
The problem of word search in Sanskrit is inseparable from complexities that include those caused by euphonic conjunctions and case-inflections. The case-inflectional forms of a noun normally number 24 owing to the fact that in Sanskrit there are eight cases and three numbers-singular, dual and plural. The traditional ... | Computational Model to Generate Case-Inflected Forms of Masculine Nouns
for Word Search in Sanskrit E-Text | 1,843 |
We investigate the hypothesis that word representations ought to incorporate both distributional and relational semantics. To this end, we employ the Alternating Direction Method of Multipliers (ADMM), which flexibly optimizes a distributional objective on raw text and a relational objective on WordNet. Preliminary res... | Incorporating Both Distributional and Relational Semantics in Word
Representations | 1,844 |
Distributed representations of words have boosted the performance of many Natural Language Processing tasks. However, usually only one representation per word is obtained, not acknowledging the fact that some words have multiple meanings. This has a negative effect on the individual word representations and the languag... | A Simple and Efficient Method To Generate Word Sense Representations | 1,845 |
Supertagging is an approach originally developed by Bangalore and Joshi (1999) to improve the parsing efficiency. In the beginning, the scholars used small training datasets and somewhat na\"ive smoothing techniques to learn the probability distributions of supertags. Since its inception, the applicability of Supertags... | Supertagging: Introduction, learning, and application | 1,846 |
The bag-of-words (BOW) model is the common approach for classifying documents, where words are used as feature for training a classifier. This generally involves a huge number of features. Some techniques, such as Latent Semantic Analysis (LSA) or Latent Dirichlet Allocation (LDA), have been designed to summarize docum... | N-gram-Based Low-Dimensional Representation for Document Classification | 1,847 |
In this work, we present a novel neural network based architecture for inducing compositional crosslingual word representations. Unlike previously proposed methods, our method fulfills the following three criteria; it constrains the word-level representations to be compositional, it is capable of leveraging both biling... | Leveraging Monolingual Data for Crosslingual Compositional Word
Representations | 1,848 |
Neural language models learn word representations, or embeddings, that capture rich linguistic and conceptual information. Here we investigate the embeddings learned by neural machine translation models, a recently-developed class of neural language model. We show that embeddings from translation models outperform thos... | Embedding Word Similarity with Neural Machine Translation | 1,849 |
We consider learning representations of entities and relations in KBs using the neural-embedding approach. We show that most existing models, including NTN (Socher et al., 2013) and TransE (Bordes et al., 2013b), can be generalized under a unified learning framework, where entities are low-dimensional vectors learned f... | Embedding Entities and Relations for Learning and Inference in Knowledge
Bases | 1,850 |
This paper presents an in-depth investigation on integrating neural language models in translation systems. Scaling neural language models is a difficult task, but crucial for real-world applications. This paper evaluates the impact on end-to-end MT quality of both new and existing scaling techniques. We show when expl... | Pragmatic Neural Language Modelling in Machine Translation | 1,851 |
Sign language, which is a medium of communication for deaf people, uses manual communication and body language to convey meaning, as opposed to using sound. This paper presents a prototype Malayalam text to sign language translation system. The proposed system takes Malayalam text as input and generates corresponding S... | A prototype Malayalam to Sign Language Automatic Translator | 1,852 |
SentiWordNet is an important lexical resource supporting sentiment analysis in opinion mining applications. In this paper, we propose a novel approach to construct a Vietnamese SentiWordNet (VSWN). SentiWordNet is typically generated from WordNet in which each synset has numerical scores to indicate its opinion polarit... | Construction of Vietnamese SentiWordNet by using Vietnamese Dictionary | 1,853 |
Research into the stylistic properties of translations is an issue which has received some attention in computational stylistics. Previous work by Rybicki (2006) on the distinguishing of character idiolects in the work of Polish author Henryk Sienkiewicz and two corresponding English translations using Burrow's Delta m... | Chasing the Ghosts of Ibsen: A computational stylistic analysis of drama
in translation | 1,854 |
In this paper we present REG, a graph-based approach for study a fundamental problem of Natural Language Processing (NLP): the automatic text summarization. The algorithm maps a document as a graph, then it computes the weight of their sentences. We have applied this approach to summarize documents in three languages. | Un résumeur à base de graphes, indépéndant de la langue | 1,855 |
Part of Speech (POS) is a very vital topic in Natural Language Processing (NLP) task in any language, which involves analysing the construction of the language, behaviours and the dynamics of the language, the knowledge that could be utilized in computational linguistics analysis and automation applications. In this co... | Unknown Words Analysis in POS tagging of Sinhala Language | 1,856 |
Analysis of scripts plays an important role in paleography and in quantitative linguistics. Especially in the field of digital paleography quantitative features are much needed to differentiate glyphs. We describe an elaborate set of metrics that quantify qualitative information contained in characters and hence indire... | Quantifying Scripts: Defining metrics of characters for quantitative and
descriptive analysis | 1,857 |
This paper is concerned with nearest neighbor search in distributional semantic models. A normal nearest neighbor search only returns a ranked list of neighbors, with no information about the structure or topology of the local neighborhood. This is a potentially serious shortcoming of the mode of querying a distributio... | Navigating the Semantic Horizon using Relative Neighborhood Graphs | 1,858 |
Turkic languages exhibit extensive and diverse etymological relationships among lexical items. These relationships make the Turkic languages promising for exploring automated translation lexicon induction by leveraging cognate and other etymological information. However, due to the extent and diversity of the types of ... | Annotating Cognates and Etymological Origin in Turkic Languages | 1,859 |
We consider phrase based Language Models (LM), which generalize the commonly used word level models. Similar concept on phrase based LMs appears in speech recognition, which is rather specialized and thus less suitable for machine translation (MT). In contrast to the dependency LM, we first introduce the exhaustive phr... | Phrase Based Language Model For Statistical Machine Translation | 1,860 |
Reordering is a challenge to machine translation (MT) systems. In MT, the widely used approach is to apply word based language model (LM) which considers the constituent units of a sentence as words. In speech recognition (SR), some phrase based LM have been proposed. However, those LMs are not necessarily suitable or ... | Phrase Based Language Model for Statistical Machine Translation:
Empirical Study | 1,861 |
Syntactic parsing is a necessary task which is required for NLP applications including machine translation. It is a challenging task to develop a qualitative parser for morphological rich and agglutinative languages. Syntactic analysis is used to understand the grammatical structure of a natural language sentence. It o... | Survey:Natural Language Parsing For Indian Languages | 1,862 |
Statistical methods have been widely employed in many practical natural language processing applications. More specifically, complex networks concepts and methods from dynamical systems theory have been successfully applied to recognize stylistic patterns in written texts. Despite the large amount of studies devoted to... | Authorship recognition via fluctuation analysis of network topology and
word intermittency | 1,863 |
Given a set of terms from a given domain, how can we structure them into a taxonomy without manual intervention? This is the task 17 of SemEval 2015. Here we present our simple taxonomy structuring techniques which, despite their simplicity, ranked first in this 2015 benchmark. We use large quantities of text (English ... | INRIASAC: Simple Hypernym Extraction Methods | 1,864 |
Language model is one of the most important modules in statistical machine translation and currently the word-based language model dominants this community. However, many translation models (e.g. phrase-based models) generate the target language sentences by rendering and compositing the phrases rather than the words. ... | Beyond Word-based Language Model in Statistical Machine Translation | 1,865 |
We present a language complexity analysis of World of Warcraft (WoW) community texts, which we compare to texts from a general corpus of web English. Results from several complexity types are presented, including lexical diversity, density, readability and syntactic complexity. The language of WoW texts is found to be ... | An investigation into language complexity of World-of-Warcraft
game-external texts | 1,866 |
Generating a novel textual description of an image is an interesting problem that connects computer vision and natural language processing. In this paper, we present a simple model that is able to generate descriptive sentences given a sample image. This model has a strong focus on the syntax of the descriptions. We tr... | Phrase-based Image Captioning | 1,867 |
This paper discusses a new metric that has been applied to verify the quality in translation between sentence pairs in parallel corpora of Arabic-English. This metric combines two techniques, one based on sentence length and the other based on compression code length. Experiments on sample test parallel Arabic-English ... | A new hybrid metric for verifying parallel corpora of Arabic-English | 1,868 |
This paper presents generalized probabilistic models for high-order projective dependency parsing and an algorithmic framework for learning these statistical models involving dependency trees. Partition functions and marginals for high-order dependency trees can be computed efficiently, by adapting our algorithms which... | Probabilistic Models for High-Order Projective Dependency Parsing | 1,869 |
Word reordering is one of the most difficult aspects of statistical machine translation (SMT), and an important factor of its quality and efficiency. Despite the vast amount of research published to date, the interest of the community in this problem has not decreased, and no single method appears to be strongly domina... | A Survey of Word Reordering in Statistical Machine Translation:
Computational Models and Language Phenomena | 1,870 |
We develop novel first- and second-order features for dependency parsing based on the Google Syntactic Ngrams corpus, a collection of subtree counts of parsed sentences from scanned books. We also extend previous work on surface $n$-gram features from Web1T to the Google Books corpus and from first-order to second-orde... | Web-scale Surface and Syntactic n-gram Features for Dependency Parsing | 1,871 |
Recently proposed Skip-gram model is a powerful method for learning high-dimensional word representations that capture rich semantic relationships between words. However, Skip-gram as well as most prior work on learning word representations does not take into account word ambiguity and maintain only single representati... | Breaking Sticks and Ambiguities with Adaptive Skip-gram | 1,872 |
In this paper, we address the problems of Arabic Text Classification and stemming using Transducers and Rational Kernels. We introduce a new stemming technique based on the use of Arabic patterns (Pattern Based Stemmer). Patterns are modelled using transducers and stemming is done without depending on any dictionary. U... | Rational Kernels for Arabic Stemming and Text Classification | 1,873 |
Statistical machine translation models have made great progress in improving the translation quality. However, the existing models predict the target translation with only the source- and target-side local context information. In practice, distinguishing good translations from bad ones does not only depend on the local... | Local Translation Prediction with Global Sentence Representation | 1,874 |
We reduce phrase-representation parsing to dependency parsing. Our reduction is grounded on a new intermediate representation, "head-ordered dependency trees", shown to be isomorphic to constituent trees. By encoding order information in the dependency labels, we show that any off-the-shelf, trainable dependency parser... | Parsing as Reduction | 1,875 |
We present a novel learning method for word embeddings designed for relation classification. Our word embeddings are trained by predicting words between noun pairs using lexical relation-specific features on a large unlabeled corpus. This allows us to explicitly incorporate relation-specific information into the word e... | Task-Oriented Learning of Word Embeddings for Semantic Relation
Classification | 1,876 |
It is commonly accepted that machine translation is a more complex task than part of speech tagging. But how much more complex? In this paper we make an attempt to develop a general framework and methodology for computing the informational and/or processing complexity of NLP applications and tasks. We define a universa... | The NLP Engine: A Universal Turing Machine for NLP | 1,877 |
Hyperlinks and other relations in Wikipedia are a extraordinary resource which is still not fully understood. In this paper we study the different types of links in Wikipedia, and contrast the use of the full graph with respect to just direct links. We apply a well-known random walk algorithm on two tasks, word related... | Studying the Wikipedia Hyperlink Graph for Relatedness and
Disambiguation | 1,878 |
Most state-of-the-art systems today produce morphological analysis based only on orthographic patterns. In contrast, we propose a model for unsupervised morphological analysis that integrates orthographic and semantic views of words. We model word formation in terms of morphological chains, from base words to the obser... | An Unsupervised Method for Uncovering Morphological Chains | 1,879 |
Recent work on end-to-end neural network-based architectures for machine translation has shown promising results for En-Fr and En-De translation. Arguably, one of the major factors behind this success has been the availability of high quality parallel corpora. In this work, we investigate how to leverage abundant monol... | On Using Monolingual Corpora in Neural Machine Translation | 1,880 |
Morphological Analysis is an important branch of linguistics for any Natural Language Processing Technology. Morphology studies the word structure and formation of word of a language. In current scenario of NLP research, morphological analysis techniques have become more popular day by day. For processing any language,... | An implementation of Apertium based Assamese morphological analyzer | 1,881 |
We propose a novel convolutional architecture, named $gen$CNN, for word sequence prediction. Different from previous work on neural network-based language modeling and generation (e.g., RNN or LSTM), we choose not to greedily summarize the history of words as a fixed length vector. Instead, we use a convolutional neura... | $gen$CNN: A Convolutional Architecture for Word Sequence Prediction | 1,882 |
word2vec affords a simple yet powerful approach of extracting quantitative variables from unstructured textual data. Over half of healthcare data is unstructured and therefore hard to model without involved expertise in data engineering and natural language processing. word2vec can serve as a bridge to quickly gather i... | Prediction Using Note Text: Synthetic Feature Creation with word2vec | 1,883 |
In machine translation it is common phenomenon that machine-readable dictionaries and standard parsing rules are not enough to ensure accuracy in parsing and translating English phrases into Korean language, which is revealed in misleading translation results due to consequent structural and semantic ambiguities. This ... | Phrase database Approach to structural and semantic disambiguation in
English-Korean Machine Translation | 1,884 |
This paper discusses the structure of Syntagma's Lexical Database (focused on Italian). The basic database consists in four tables. Table Forms contains word inflections, used by the POS-tagger for the identification of input-words. Forms is related to Lemma. Table Lemma stores all kinds of grammatical features of word... | Syntagma Lexical Database | 1,885 |
This work addresses the problem of measuring how many languages a person "effectively" speaks given that some of the languages are close to each other. In other words, to assign a meaningful number to her language portfolio. Intuition says that someone who speaks fluently Spanish and Portuguese is linguistically less p... | On measuring linguistic intelligence | 1,886 |
Open domain relation extraction systems identify relation and argument phrases in a sentence without relying on any underlying schema. However, current state-of-the-art relation extraction systems are available only for English because of their heavy reliance on linguistic tools such as part-of-speech taggers and depen... | Multilingual Open Relation Extraction Using Cross-lingual Projection | 1,887 |
Dependency parsers are among the most crucial tools in natural language processing as they have many important applications in downstream tasks such as information retrieval, machine translation and knowledge acquisition. We introduce the Yara Parser, a fast and accurate open-source dependency parser based on the arc-e... | Yara Parser: A Fast and Accurate Dependency Parser | 1,888 |
Unsupervised word embeddings have been shown to be valuable as features in supervised learning problems; however, their role in unsupervised problems has been less thoroughly explored. In this paper, we show that embeddings can likewise add value to the problem of unsupervised POS induction. In two representative model... | Unsupervised POS Induction with Word Embeddings | 1,889 |
pymorphy2 is a morphological analyzer and generator for Russian and Ukrainian languages. It uses large efficiently encoded lexi- cons built from OpenCorpora and LanguageTool data. A set of linguistically motivated rules is developed to enable morphological analysis and generation of out-of-vocabulary words observed in ... | Morphological Analyzer and Generator for Russian and Ukrainian Languages | 1,890 |
We describe a technique for attributing parts of a written text to a set of unknown authors. Nothing is assumed to be known a priori about the writing styles of potential authors. We use multiple independent clusterings of an input text to identify parts that are similar and dissimilar to one another. We describe algor... | Unsupervised authorship attribution | 1,891 |
This paper presents text normalization which is an integral part of any text-to-speech synthesis system. Text normalization is a set of methods with a task to write non-standard words, like numbers, dates, times, abbreviations, acronyms and the most common symbols, in their full expanded form are presented. The whole t... | Normalization of Non-Standard Words in Croatian Texts | 1,892 |
Discourse markers are universal linguistic events subject to language variation. Although an extensive literature has already reported language specific traits of these events, little has been said on their cross-language behavior and on building an inventory of multilingual lexica of discourse markers. This work descr... | Towards Using Machine Translation Techniques to Induce Multilingual
Lexica of Discourse Markers | 1,893 |
Learning representations for semantic relations is important for various tasks such as analogy detection, relational search, and relation classification. Although there have been several proposals for learning representations for individual words, learning word representations that explicitly capture the semantic relat... | Embedding Semantic Relations into Word Representations | 1,894 |
In recent years, There has been a variety of research on discourse parsing, particularly RST discourse parsing. Most of the recent work on RST parsing has focused on implementing new types of features or learning algorithms in order to improve accuracy, with relatively little focus on efficiency, robustness, or practic... | Fast Rhetorical Structure Theory Discourse Parsing | 1,895 |
Text segmentation task is an essential processing task for many of Natural Language Processing (NLP) such as text summarization, text translation, dialogue language understanding, among others. Turns segmentation considered the key player in dialogue understanding task for building automatic Human-Computer systems. In ... | Turn Segmentation into Utterances for Arabic Spontaneous Dialogues and
Instance Messages | 1,896 |
Building dialogues systems interaction has recently gained considerable attention, but most of the resources and systems built so far are tailored to English and other Indo-European languages. The need for designing systems for other languages is increasing such as Arabic language. For this reasons, there are more inte... | A Survey of Arabic Dialogues Understanding for Spontaneous Dialogues and
Instant Message | 1,897 |
Sarcasm is considered one of the most difficult problem in sentiment analysis. In our ob-servation on Indonesian social media, for cer-tain topics, people tend to criticize something using sarcasm. Here, we proposed two additional features to detect sarcasm after a common sentiment analysis is conducted. The features a... | Indonesian Social Media Sentiment Analysis With Sarcasm Detection | 1,898 |
The rise of social media such as blogs and social networks has fueled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new ... | Sentiment Analysis For Modern Standard Arabic And Colloquial | 1,899 |
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