pwc-archive/papers-with-abstracts huggingface dataset:
Large-scale news entity sentiment analysis
Authors: Ralf Steinberger, Stefanie Hegele, Hristo Tanev, Leonida della Rocca
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Articles, Bias Detection, Negation, Part-Of-Speech Tagging, Sentiment Analysis

We work on detecting positive or negative sentiment towards named entities in very large volumes of news articles. The aim is to monitor changes over time, as well as to work towards media bias detection by com-paring differences across news sources and countries. With view to applying the same method to dozens of languages, we use lin-guistically light-weight methods: searching for positive and negative terms in bags of words around entity mentions (also consid-ering negation). Evaluation results are good and better than a third-party baseline sys-tem, but precision is not sufficiently high to display the results publicly in our multilin-gual news analysis system Europe Media Monitor (EMM). In this paper, we focus on describing our effort to improve the English language results by avoiding the biggest sources of errors. We also present new work on using a syntactic parser to identify safe opinion recognition rules, such as predica-tive structures in which sentiment words di-rectly refer to an entity. The precision of this method is good, but recall is very low.
Unsupervised Learning of Morphology with Graph Sampling
Authors: Maciej Sumalvico
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Graph Sampling, Morphological Analysis, Segmentation

We introduce a language-independent, graph-based probabilistic model of morphology, which uses transformation rules operating on whole words instead of the traditional morphological segmentation. The morphological analysis of a set of words is expressed through a graph having words as vertices and structural relationships between words as edges. We define a probability distribution over such graphs and develop a sampler based on the Metropolis-Hastings algorithm. The sampling is applied in order to determine the strength of morphological relationships between words, filter out accidental similarities and reduce the set of rules necessary to explain the data. The model is evaluated on the task of finding pairs of morphologically similar words, as well as generating new words. The results are compared to a state-of-the-art segmentation-based approach.
Multi-entity sentiment analysis using entity-level feature extraction and word embeddings approach
Authors: Colm Sweeney, Deepak Padmanabhan
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Binary Classification, Entity Extraction using GAN, General Classification, Relation, Sentiment Analysis, Sentiment Classification, Word Embeddings

The sentiment analysis task has been traditionally divided into lexicon or machine learning approaches, but recently the use of word embeddings methods have emerged, that provide powerful algorithms to allow semantic understanding without the task of creating large amounts of annotated test data. One problem with this type of binary classification, is that the sentiment output will be in the form of {`}1{'} (positive) or {`}0{'} (negative) for the string of text in the tweet, regardless if there are one or more entities referred to in the text. This paper plans to enhance the word embeddings approach with the deployment of a sentiment lexicon-based technique to appoint a total score that indicates the polarity of opinion in relation to a particular entity or entities. This type of sentiment classification is a way of associating a given entity with the adjectives, adverbs, and verbs describing it, and extracting the associated sentiment to try and infer if the text is positive or negative in relation to the entity or entities.
Finding Individual Word Sense Changes and their Delay in Appearance
Authors: Nina Tahmasebi, Thomas Risse
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Word Embeddings, Word Sense Induction

We present a method for detecting word sense changes by utilizing automatically induced word senses. Our method works on the level of individual senses and allows a word to have e.g. one stable sense and then add a novel sense that later experiences change. Senses are grouped based on polysemy to find linguistic concepts and we can find broadening and narrowing as well as novel (polysemous and homonymic) senses. We evaluate on a testset, present recall and estimates of the time between expected and found change.
Streaming Text Analytics for Real-Time Event Recognition
Authors: Philippe Thomas, Johannes Kirschnick, Leonhard Hennig, Renlong Ai, Sven Schmeier, Holmer Hemsen, Feiyu Xu, Hans Uszkoreit
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Articles, Entity Linking, Event Extraction, Named Entity Recognition (NER), Relation Extraction

A huge body of continuously growing written knowledge is available on the web in the form of social media posts, RSS feeds, and news articles. Real-time information extraction from such high velocity, high volume text streams requires scalable, distributed natural language processing pipelines. We introduce such a system for fine-grained event recognition within the big data framework Flink, and demonstrate its capabilities for extracting and geo-locating mobility- and industry-related events from heterogeneous text sources. Performance analyses conducted on several large datasets show that our system achieves high throughput and maintains low latency, which is crucial when events need to be detected and acted upon in real-time. We also present promising experimental results for the event extraction component of our system, which recognizes a novel set of event types. The demo system is available at \url{http://dfki.de/sd4m-sta-demo/}.
An Eye-tracking Study of Named Entity Annotation
Authors: Takenobu Tokunaga, Hitoshi Nishikawa, Tomoya Iwakura
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Active Learning, Coreference Resolution, Named Entity Recognition (NER), Sentence Compression, Sentiment Analysis, Word Sense Disambiguation

Utilising effective features in machine learning-based natural language processing (NLP) is crucial in achieving good performance for a given NLP task. The paper describes a pilot study on the analysis of eye-tracking data during named entity (NE) annotation, aiming at obtaining insights into effective features for the NE recognition task. The eye gaze data were collected from 10 annotators and analysed regarding working time and fixation distribution. The results of the preliminary qualitative analysis showed that human annotators tend to look at broader contexts around the target NE than recent state-of-the-art automatic NE recognition systems and to use predicate argument relations to identify the NE categories.
A Graph-based Text Similarity Measure That Employs Named Entity Information
Authors: Leonidas Tsekouras, Iraklis Varlamis, George Giannakopoulos
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Clustering, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Part-Of-Speech Tagging, Text Clustering, text similarity

Text comparison is an interesting though hard task, with many applications in Natural Language Processing. This work introduces a new text-similarity measure, which employs named-entities{'} information extracted from the texts and the n-gram graphs{'} model for representing documents. Using OpenCalais as a named-entity recognition service and the JINSECT toolkit for constructing and managing n-gram graphs, the text similarity measure is embedded in a text clustering algorithm (k-Means). The evaluation of the produced clusters with various clustering validity metrics shows that the extraction of named entities at a first step can be profitable for the time-performance of similarity measures that are based on the n-gram graph representation without affecting the overall performance of the NLP task.
Detecting Metaphorical Phrases in the Polish Language
Authors: Aleks Wawer, er, Agnieszka Mykowiecka
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Machine Translation, Natural Language Inference, Word Embeddings

In this paper we describe experiments with automated detection of metaphors in the Polish language. We focus our analysis on noun phrases composed of an adjective and a noun, and distinguish three types of expressions: with literal sense, with metaphorical sense, and expressions both literal and methaphorical (context-dependent). We propose a method of automatically recognizing expression type using word embeddings and neural networks. We evaluate multiple neural network architectures and demonstrate that the method significantly outperforms strong baselines.
Efficient Encoding of Pathology Reports Using Natural Language Processing
Authors: Rebecka Weegar, Jan F Nyg{\aa}rd, Hercules Dalianis
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9

In this article we present a system that extracts information from pathology reports. The reports are written in Norwegian and contain free text describing prostate biopsies. Currently, these reports are manually coded for research and statistical purposes by trained experts at the Cancer Registry of Norway where the coders extract values for a set of predefined fields that are specific for prostate cancer. The presented system is rule based and achieves an average F-score of 0.91 for the fields Gleason grade, Gleason score, the number of biopsies that contain tumor tissue, and the orientation of the biopsies. The system also identifies reports that contain ambiguity or other content that should be reviewed by an expert. The system shows potential to encode the reports considerably faster, with less resources, and similar high quality to the manual encoding.
Multilingual and Cross-Lingual Complex Word Identification
Authors: Seid Muhie Yimam, Sanja {\v{S}}tajner, Martin Riedl, Chris Biemann
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Complex Word Identification, Lexical Simplification

Complex Word Identification (CWI) is an important task in lexical simplification and text accessibility. Due to the lack of CWI datasets, previous works largely depend on Simple English Wikipedia and edit histories for obtaining {`}gold standard{'} annotations, which are of doubtable quality, and limited only to English. We collect complex words/phrases (CP) for English, German and Spanish, annotated by both native and non-native speakers, and propose language independent features that can be used to train multilingual and cross-lingual CWI models. We show that the performance of cross-lingual CWI systems (using a model trained on one language and applying it on the other languages) is comparable to the performance of monolingual CWI systems.
Automatic Generation of Situation Models for Plan Recognition Problems
Authors: Kristina Yordanova
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9

Recent attempts at behaviour understanding through language grounding have shown that it is possible to automatically generate models for planning problems from textual instructions. One drawback of these approaches is that they either do not make use of the semantic structure behind the model elements identified in the text, or they manually incorporate a collection of concepts with semantic relationships between them. We call this collection of knowledge situation model. The situation model introduces additional context information to the model. It could also potentially reduce the complexity of the planning problem compared to models that do not use situation models. To address this problem, we propose an approach that automatically generates the situation model from textual instructions. The approach is able to identify various hierarchical, spatial, directional, and causal relations. We use the situation model to automatically generate planning problems in a PDDL notation and we show that the situation model reduces the complexity of the PDDL model in terms of number of operators and branching factor compared to planning models that do not make use of situation models.
A Simple Model for Improving the Performance of the Stanford Parser for Action Detection in Textual Instructions
Authors: Kristina Yordanova
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Action Detection, POS, Sentence

Different approaches for behaviour understanding rely on textual instructions to generate models of human behaviour. These approaches usually use state of the art parsers to obtain the part of speech (POS) meaning and dependencies of the words in the instructions. For them it is essential that the parser is able to correctly annotate the instructions and especially the verbs as they describe the actions of the person. State of the art parsers usually make errors when annotating textual instructions, as they have short sentence structure often in imperative form. The inability of the parser to identify the verbs results in the inability of behaviour understanding systems to identify the relevant actions. To address this problem, we propose a simple rule-based model that attempts to correct any incorrectly annotated verbs. We argue that the model is able to significantly improve the parser{'}s performance without the need of additional training data. We evaluate our approach by extracting the actions from 61 textual instructions annotated only with the Stanford parser and once again after applying our model. The results show a significant improvement in the recognition rate when applying the rules (75{\%} accuracy compared to 68{\%} without the rules, p-value {\textless} 0.001).
Using NLP for Enhancing Second Language Acquisition
Authors: Leonardo Zilio, Rodrigo Wilkens, C{\'e}drick Fairon
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Language Acquisition

This study presents SMILLE, a system that draws on the Noticing Hypothesis and on input enhancements, addressing the lack of salience of grammatical infor mation in online documents chosen by a given user. By means of input enhancements, the system can draw the user{'}s attention to grammar, which could possibly lead to a higher intake per input ratio for metalinguistic information. The system receives as input an online document and submits it to a combined processing of parser and hand-written rules for detecting its grammatical structures. The input text can be freely chosen by the user, providing a more engaging experience and reflecting the user{'}s interests. The system can enhance a total of 107 fine-grained types of grammatical structures that are based on the CEFR. An evaluation of some of those structures resulted in an overall precision of 87{\%}.
Dish Classification using Knowledge based Dietary Conflict Detection
Authors: Nadia Clairet
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Classification, General Classification

The present paper considers the problem of dietary conflict detection from dish titles. The proposed method explores the semantics associated with the dish title in order to discover a certain or possible incompatibility of a particular dish with a particular diet. Dish titles are parts of the elusive and metaphoric gastronomy language, their processing can be viewed as a combination of short text and domain-specific texts analysis. We build our algorithm on the basis of a common knowledge lexical semantic network and show how such network can be used for domain specific short text processing.
Analysing Market Sentiments: Utilising Deep Learning to Exploit Relationships within the Economy
Authors: Tobias Daudert
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Opinion Mining, Sentiment Analysis

In today{'}s world, globalisation is not only affecting inter-culturalism but also linking markets across the globe. Given that all markets are affecting each other and are not only driven by fundamental data but also by sentiments, sentiment analysis regarding the markets becomes a tool to predict, anticipate, and milden future economic crises such as the one we faced in 2008. In this paper, an approach to improve sentiment analysis by exploiting relationships among different kinds of sentiment, together with supplementary information, from and across various data sources is proposed.
Evaluating Dialogs based on Grice's Maxims
Authors: Prathyusha Jwalapuram
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9

There is no agreed upon standard for the evaluation of conversational dialog systems, which are well-known to be hard to evaluate due to the difficulty in pinning down metrics that will correspond to human judgements and the subjective nature of human judgment itself. We explored the possibility of using Grice{'}s Maxims to evaluate effective communication in conversation. We collected some system generated dialogs from popular conversational chatbots across the spectrum and conducted a survey to see how the human judgements based on Gricean maxims correlate, and if such human judgments can be used as an effective evaluation metric for conversational dialog.
Word Sense Disambiguation with Recurrent Neural Networks
Authors: Alex Popov, er
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Word Sense Disambiguation

This paper presents a neural network architecture for word sense disambiguation (WSD). The architecture employs recurrent neural layers and more specifically LSTM cells, in order to capture information about word order and to easily incorporate distributed word representations (embeddings) as features, without having to use a fixed window of text. The paper demonstrates that the architecture is able to compete with the most successful supervised systems for WSD and that there is an abundance of possible improvements to take it to the current state of the art. In addition, it explores briefly the potential of combining different types of embeddings as input features; it also discusses possible ways for generating {``}artificial corpora{''} from knowledge bases {--} for the purpose of producing training data and in relation to possible applications of embedding lemmas and word senses in the same space.
Multi-Document Summarization of Persian Text using Paragraph Vectors
Authors: Morteza Rohanian
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Abstractive Text Summarization, Document Summarization, Multi-Document Summarization

A multi-document summarizer finds the key topics from multiple textual sources and organizes information around them. In this paper we propose a summarization method for Persian text using paragraph vectors that can represent textual units of arbitrary lengths. We use these vectors to calculate the semantic relatedness between documents, cluster them to a number of predetermined groups, weight them based on their distance to the centroids and the intra-cluster homogeneity and take out the key paragraphs. We compare the final summaries with the gold-standard summaries of 21 digital topics using the ROUGE evaluation metric. Experimental results show the advantages of using paragraph vectors over earlier attempts at developing similar methods for a low resource language like Persian.
Gradient Emotional Analysis
Authors: Lilia Simeonova
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Sentence, Sentiment Analysis

Over the past few years a lot of research has been done on sentiment analysis, however, the emotional analysis, being so subjective, is not a well examined dis-cipline. The main focus of this proposal is to categorize a given sentence in two dimensions - sentiment and arousal. For this purpose two techniques will be com-bined {--} Machine Learning approach and Lexicon-based approach. The first di-mension will give the sentiment value {--} positive versus negative. This will be re-solved by using Na{\"\i}ve Bayes Classifier. The second and more interesting dimen-sion will determine the level of arousal. This will be achieved by evaluation of given a phrase or sentence based on lexi-con with affective ratings for 14 thousand English words.
Applying Deep Neural Network to Retrieve Relevant Civil Law Articles
Authors: Anh Hang Nga Tran
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Articles, Clustering, Information Retrieval, Natural Language Inference, Part-Of-Speech Tagging, Question Answering, Retrieval

The paper aims to achieve the legal question answering information retrieval (IR) task at Competition on Legal Information Extraction/Entailment (COLIEE) 2017. Our proposal methodology for the task is to utilize deep neural network, natural language processing and word2vec. The system was evaluated using training and testing data from the competition on legal information extraction/entailment (COLIEE). Our system mainly focuses on giving relevant civil law articles for given bar exams. The corpus of legal questions is drawn from Japanese Legal Bar exam queries. We implemented a combined deep neural network with additional features NLP and word2vec to gain the corresponding civil law articles based on a given bar exam {`}Yes/No{'} questions. This paper focuses on clustering words-with-relation in order to acquire relevant civil law articles. All evaluation processes were done on the COLIEE 2017 training and test data set. The experimental result shows a very promising result.
Consistency of Interpolation with Laplace Kernels is a High-Dimensional Phenomenon
Authors: Alexander Rakhlin, Xiyu Zhai
Date: 2018-12-28 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We show that minimum-norm interpolation in the Reproducing Kernel Hilbert
Space corresponding to the Laplace kernel is not consistent if input dimension
is constant. The lower bound holds for any choice of kernel bandwidth, even if
selected based on data. The result supports the empirical observation that
minimum-norm interpolation (that is, exact fit to training data) in RKHS
generalizes well for some high-dimensional datasets, but not for
low-dimensional ones.
Learning to Reconstruct Shapes from Unseen Classes
Authors: Xiuming Zhang, Zhoutong Zhang, Chengkai Zhang, Joshua B. Tenenbaum, William T. Freeman, Jiajun Wu
Date: 2018-12-28 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: 3D Reconstruction

From a single image, humans are able to perceive the full 3D shape of an
object by exploiting learned shape priors from everyday life. Contemporary
single-image 3D reconstruction algorithms aim to solve this task in a similar
fashion, but often end up with priors that are highly biased by training
classes. Here we present an algorithm, Generalizable Reconstruction (GenRe),
designed to capture more generic, class-agnostic shape priors. We achieve this
with an inference network and training procedure that combine 2.5D
representations of visible surfaces (depth and silhouette), spherical shape
representations of both visible and non-visible surfaces, and 3D voxel-based
representations, in a principled manner that exploits the causal structure of
how 3D shapes give rise to 2D images. Experiments demonstrate that GenRe
performs well on single-view shape reconstruction, and generalizes to diverse
novel objects from categories not seen during training.