pwc-archive/papers-with-abstracts huggingface dataset:
TextPro-AL: An Active Learning Platform for Flexible and Efficient Production of Training Data for NLP Tasks
Authors: Bernardo Magnini, Anne-Lyse Minard, Mohammed R. H. Qwaider, Manuela Speranza
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Active Learning, BIG-bench Machine Learning, Domain Adaptation

This paper presents TextPro-AL (Active Learning for Text Processing), a platform where human annotators can efficiently work to produce high quality training data for new domains and new languages exploiting Active Learning methodologies. TextPro-AL is a web-based application integrating four components: a machine learning based NLP pipeline, an annotation editor for task definition and text annotations, an incremental re-training procedure based on active learning selection from a large pool of unannotated data, and a graphical visualization of the learning status of the system.
SideNoter: Scholarly Paper Browsing System based on PDF Restructuring and Text Annotation
Authors: Takeshi Abekawa, Akiko Aizawa
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: text annotation

In this paper, we discuss our ongoing efforts to construct a scientific paper browsing system that helps users to read and understand advanced technical content distributed in PDF. Since PDF is a format specifically designed for printing, layout and logical structures of documents are indistinguishably embedded in the file. It requires much effort to extract natural language text from PDF files, and reversely, display semantic annotations produced by NLP tools on the original page layout. In our browsing system, we tackle these issues caused by the gap between printable document and plain text. Our system provides ways to extract natural language sentences from PDF files together with their logical structures, and also to map arbitrary textual spans to their corresponding regions on page images. We setup a demonstration system using papers published in ACL anthology and demonstrate the enhanced search and refined recommendation functions which we plan to make widely available to NLP researchers.
Illinois Cross-Lingual Wikifier: Grounding Entities in Many Languages to the English Wikipedia
Authors: Chen-Tse Tsai, Dan Roth
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Cross-Lingual NER, Entity Linking, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

We release a cross-lingual wikification system for all languages in Wikipedia. Given a piece of text in any supported language, the system identifies names of people, locations, organizations, and grounds these names to the corresponding English Wikipedia entries. The system is based on two components: a cross-lingual named entity recognition (NER) model and a cross-lingual mention grounding model. The cross-lingual NER model is a language-independent model which can extract named entity mentions in the text of any language in Wikipedia. The extracted mentions are then grounded to the English Wikipedia using the cross-lingual mention grounding model. The only resources required to train the proposed system are the multilingual Wikipedia dump and existing training data for English NER. The system is online at \url{http://cogcomp.cs.illinois.edu/page/demo_view/xl_wikifier}
A Meaning-based English Math Word Problem Solver with Understanding, Reasoning and Explanation
Authors: Chao-Chun Liang, Shih-Hong Tsai, Ting-Yun Chang, Yi-Chung Lin, Keh-Yih Su
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Math

This paper presents a meaning-based statistical math word problem (MWP) solver with understanding, reasoning and explanation. It comprises a web user interface and pipelined modules for analysing the text, transforming both body and question parts into their logic forms, and then performing inference on them. The associated context of each quantity is represented with proposed role-tags (e.g., nsubj, verb, etc.), which provides the flexibility for annotating the extracted math quantity with its associated syntactic and semantic information (which specifies the physical meaning of that quantity). Those role-tags are then used to identify the desired operands and filter out irrelevant quantities (so that the answer can be obtained precisely). Since the physical meaning of each quantity is explicitly represented with those role-tags and used in the inference process, the proposed approach could explain how the answer is obtained in a human comprehensible way.
Valencer: an API to Query Valence Patterns in FrameNet
Authors: Alex Kabbach, re, Corentin Ribeyre
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Question Answering

This paper introduces Valencer: a RESTful API to search for annotated sentences matching a given combination of syntactic realizations of the arguments of a predicate {--} also called {`}valence pattern{'} {--} in the FrameNet database. The API takes as input an HTTP GET request specifying a valence pattern and outputs a list of exemplifying annotated sentences in JSON format. The API is designed to be modular and language-independent, and can therefore be easily integrated to other (NLP) server-side or client-side applications, as well as non-English FrameNet projects. Valencer is free, open-source, and licensed under the MIT license.
The Open Framework for Developing Knowledge Base And Question Answering System
Authors: Jiseong Kim, Gyu-Hyeon Choi, Jung-Uk Kim, Eun-Kyung Kim, Key-Sun Choi
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Question Answering, Reading Comprehension

Developing a question answering (QA) system is a task of implementing and integrating modules of different technologies and evaluating an integrated whole system, which inevitably goes with a collaboration among experts of different domains. For supporting a easy collaboration, this demonstration presents the open framework that aims to support developing a QA system in collaborative and intuitive ways. The demonstration also shows the QA system developed by our novel framework.
Linggle Knows: A Search Engine Tells How People Write
Authors: Jhih-Jie Chen, Hao-Chun Peng, Mei-Cih Yeh, Peng-Yu Chen, Jason Chang
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12

This paper shows the great potential of incorporating different approaches to help writing. Not only did they solve different kinds of writing problems, but also they complement and reinforce each other to be a complete and effective solution. Despite the extensive and multifaceted feedback and suggestion, writing is not all about syntactically or lexically well-written. It involves contents, structure, the certain understanding of the background, and many other factors to compose a rich, organized and sophisticated text. (e.g., conventional structure and idioms in academic writing). There is still a long way to go to accomplish the ultimate goal. We envision the future of writing to be a joyful experience with the help of instantaneous suggestion and constructive feedback.
Korean FrameNet Expansion Based on Projection of Japanese FrameNet
Authors: Jeong-uk Kim, Younggyun Hahm, Key-Sun Choi
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Machine Translation, Reading Comprehension, Semantic Role Labeling

FrameNet project has begun from Berkeley in 1997, and is now supported in several countries reflecting characteristics of each language. The work for generating Korean FrameNet was already done by converting annotated English sentences into Korean with trained translators. However, high cost of frame-preservation and error revision was a huge burden on further expansion of FrameNet. This study makes use of linguistic similarity between Japanese and Korean to increase Korean FrameNet corpus with low cost. We also suggest adapting PubAnnotation and Korean-friendly valence patterns to FrameNet for increased accessibility.
A Framework for Mining Enterprise Risk and Risk Factors from News Documents
Authors: Tirthankar Dasgupta, Lipika Dey, Prasenjit Dey, Rupsa Saha
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Management, POS, valid

Any real world events or trends that can affect the company{'}s growth trajectory can be considered as risk. There has been a growing need to automatically identify, extract and analyze risk related statements from news events. In this demonstration, we will present a risk analytics framework that processes enterprise project management reports in the form of textual data and news documents and classify them into valid and invalid risk categories. The framework also extracts information from the text pertaining to the different categories of risks like their possible cause and impacts. Accordingly, we have used machine learning based techniques and studied different linguistic features like n-gram, POS, dependency, future timing, uncertainty factors in texts and their various combinations. A manual annotation study from management experts using risk descriptions collected for a specific organization was conducted to evaluate the framework. The evaluation showed promising results for automated risk analysis and identification.
papago: A Machine Translation Service with Word Sense Disambiguation and Currency Conversion
Authors: Hyoung-Gyu Lee, Jun-Seok Kim, Joong-Hwi Shin, JaeSong Lee, Ying-Xiu Quan, Young-Seob Jeong
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Machine Translation, Optical Character Recognition (OCR), Speech Recognition, Speech Synthesis, Translation, Word Sense Disambiguation

In this paper, we introduce papago - a translator for mobile device which is equipped with new features that can provide convenience for users. The first feature is word sense disambiguation based on user feedback. By using the feature, users can select one among multiple meanings of a homograph and obtain the corrected translation with the user-selected sense. The second feature is the instant currency conversion of money expressions contained in a translation result with current exchange rate. Users can be quickly and precisely provided the amount of money converted as local currency when they travel abroad.
TopoText: Interactive Digital Mapping of Literary Text
Authors: R El Khatib, a, Julia El Zini, David Wrisley, Mohamad Jaber, Shady Elbassuoni
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Part-Of-Speech Tagging

We demonstrate TopoText, an interactive tool for digital mapping of literary text. TopoText takes as input a literary piece of text such as a novel or a biography article and automatically extracts all place names in the text. The identified places are then geoparsed and displayed on an interactive map. TopoText calculates the number of times a place was mentioned in the text, which is then reflected on the map allowing the end-user to grasp the importance of the different places within the text. It also displays the most frequent words mentioned within a specified proximity of a place name in context or across the entire text. This can also be faceted according to part of speech tags. Finally, TopoText keeps the human in the loop by allowing the end-user to disambiguate places and to provide specific place annotations. All extracted information such as geolocations, place frequencies, as well as all user-provided annotations can be automatically exported as a CSV file that can be imported later by the same user or other users.
ACE: Automatic Colloquialism, Typographical and Orthographic Errors Detection for Chinese Language
Authors: Shichao Dong, Gabriel Pui Cheong Fung, Binyang Li, Baolin Peng, Ming Liao, Jia Zhu, Kam-Fai Wong
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Language Modeling, Language Modelling

We present a system called ACE for Automatic Colloquialism and Errors detection for written Chinese. ACE is based on the combination of N-gram model and rule-base model. Although it focuses on detecting colloquial Cantonese (a dialect of Chinese) at the current stage, it can be extended to detect other dialects. We chose Cantonese becauase it has many interesting properties, such as unique grammar system and huge colloquial terms, that turn the detection task extremely challenging. We conducted experiments using real data and synthetic data. The results indicated that ACE is highly reliable and effective.
A Tool for Efficient Content Compilation
Authors: Boris Galitsky
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12

We build a tool to assist in content creation by mining the web for information relevant to a given topic. This tool imitates the process of essay writing by humans: searching for topics on the web, selecting content frag-ments from the found document, and then compiling these fragments to obtain a coherent text. The process of writing starts with automated building of a table of content by obtaining the list of key entities for the given topic extracted from web resources such as Wikipedia. Once a table of content is formed, each item forms a seed for web mining. The tool builds a full-featured structured Word document with table of content, section structure, images and captions and web references for all mined text fragments. Two linguistic technologies are employed: for relevance verification, we use similarity computed as a tree similarity between parse trees for a seed and candidate text fragment. For text coherence, we use a measure of agreement between a given and consecutive paragraph by tree kernel learning of their discourse trees. The tool is available at \url{http://animatronica.io/submit.html}.
MAGES: A Multilingual Angle-integrated Grouping-based Entity Summarization System
Authors: Eun-Kyung Kim, Key-Sun Choi
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12

This demo presents MAGES (multilingual angle-integrated grouping-based entity summarization), an entity summarization system for a large knowledge base such as DBpedia based on a entity-group-bound ranking in a single integrated entity space across multiple language-specific editions. MAGES offers a multilingual angle-integrated space model, which has the advantage of overcoming missing semantic tags (i.e., categories) caused by biases in different language communities, and can contribute to the creation of entity groups that are well-formed and more stable than the monolingual condition within it. MAGES can help people quickly identify the essential points of the entities when they search or browse a large volume of entity-centric data. Evaluation results on the same experimental data demonstrate that our system produces a better summary compared with other representative DBpedia entity summarization methods.
Botta: An Arabic Dialect Chatbot
Authors: Dana Abu Ali, Nizar Habash
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Chatbot

This paper presents BOTTA, the first Arabic dialect chatbot. We explore the challenges of creating a conversational agent that aims to simulate friendly conversations using the Egyptian Arabic dialect. We present a number of solutions and describe the different components of the BOTTA chatbot. The BOTTA database files are publicly available for researchers working on Arabic chatbot technologies. The BOTTA chatbot is also publicly available for any users who want to chat with it online.
What's up on Twitter? Catch up with TWIST!
Authors: Marina Litvak, Natalia Vanetik, Efi Levi, Michael Roistacher
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Clustering, Event Detection, Sentiment Analysis

Event detection and analysis with respect to public opinions and sentiments in social media is a broad and well-addressed research topic. However, the characteristics and sheer volume of noisy Twitter messages make this a difficult task. This demonstration paper describes a TWItter event Summarizer and Trend detector (TWIST) system for event detection, visualization, textual description, and geo-sentiment analysis of real-life events reported in Twitter.
Praat on the Web: An Upgrade of Praat for Semi-Automatic Speech Annotation
Authors: M{\'o}nica Dom{\'\i}nguez, Iv{\'a}n Latorre, Mireia Farr{\'u}s, Joan Codina-Filb{\`a}, Leo Wanner
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12

This paper presents an implementation of the widely used speech analysis tool Praat as a web application with an extended functionality for feature annotation. In particular, Praat on the Web addresses some of the central limitations of the original Praat tool and provides (i) enhanced visualization of annotations in a dedicated window for feature annotation at interval and point segments, (ii) a dynamic scripting composition exemplified with a modular prosody tagger, and (iii) portability and an operational web interface. Speech annotation tools with such a functionality are key for exploring large corpora and designing modular pipelines.
YAMAMA: Yet Another Multi-Dialect Arabic Morphological Analyzer
Authors: Salam Khalifa, Nasser Zalmout, Nizar Habash
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Lemmatization, Morphological Analysis, Transliteration

In this paper, we present YAMAMA, a multi-dialect Arabic morphological analyzer and disambiguator. Our system is almost five times faster than the state-of-art MADAMIRA system with a slightly lower quality. In addition to speed, YAMAMA outputs a rich representation which allows for a wider spectrum of use. In this regard, YAMAMA transcends other systems, such as FARASA, which is faster but provides specific outputs catering to specific applications.
CamelParser: A system for Arabic Syntactic Analysis and Morphological Disambiguation
Authors: Anas Shahrour, Salam Khalifa, Dima Taji, Nizar Habash
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Dependency Parsing, Morphological Analysis, Morphological Disambiguation, Transliteration

In this paper, we present CamelParser, a state-of-the-art system for Arabic syntactic dependency analysis aligned with contextually disambiguated morphological features. CamelParser uses a state-of-the-art morphological disambiguator and improves its results using syntactically driven features. The system offers a number of output formats that include basic dependency with morphological features, two tree visualization modes, and traditional Arabic grammatical analysis.
Demonstrating Ambient Search: Implicit Document Retrieval for Speech Streams
Authors: Benjamin Milde, Jonas Wacker, Stefan Radomski, Max M{\"u}hlh{\"a}user, Chris Biemann
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Information Retrieval, Keyword Extraction, Retrieval, Speech Recognition

In this demonstration paper we describe Ambient Search, a system that displays and retrieves documents in real time based on speech input. The system operates continuously in ambient mode, i.e. it generates speech transcriptions and identifies main keywords and keyphrases, while also querying its index to display relevant documents without explicit query. Without user intervention, the results are dynamically updated; users can choose to interact with the system at any time, employing a conversation protocol that is enriched with the ambient information gathered continuously. Our evaluation shows that Ambient Search outperforms another implicit speech-based information retrieval system. Ambient search is available as open source software.
ConFarm: Extracting Surface Representations of Verb and Noun Constructions from Dependency Annotated Corpora of Russian
Authors: Nikita Mediankin
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: LEMMA

ConFarm is a web service dedicated to extraction of surface representations of verb and noun constructions from dependency annotated corpora of Russian texts. Currently, the extraction of constructions with a specific lemma from SynTagRus and Russian National Corpus is available. The system provides flexible interface that allows users to fine-tune the output. Extracted constructions are grouped by their contents to allow for compact representation, and the groups are visualised as a graph in order to help navigating the extraction results. ConFarm differs from similar existing tools for Russian language in that it offers full constructions, as opposed to extracting separate dependents of search word or working with collocations, and allows users to discover unexpected constructions as opposed to searching for examples of a user-defined construction.
ENIAM: Categorial Syntactic-Semantic Parser for Polish
Authors: Wojciech Jaworski, Jakub Kozakoszczak
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Information Retrieval, Lemmatization, Natural Language Inference, Question Answering

This paper presents ENIAM, the first syntactic and semantic parser that generates semantic representations for sentences in Polish. The parser processes non-annotated data and performs tokenization, lemmatization, dependency recognition, word sense annotation, thematic role annotation, partial disambiguation and computes the semantic representation.
Towards Non-projective High-Order Dependency Parser
Authors: Wenjing Fang, Kenny Zhu, Yizhong Wang, Jia Tan
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Dependency Parsing, Vocal Bursts Intensity Prediction

This paper presents a novel high-order dependency parsing framework that targets non-projective treebanks. It imitates how a human parses sentences in an intuitive way. At every step of the parse, it determines which word is the easiest to process among all the remaining words, identifies its head word and then folds it under the head word. Further, this work is flexible enough to be augmented with other parsing techniques.
Using Synthetically Collected Scripts for Story Generation
Authors: Takashi Ogata, Tatsuya Arai, Jumpei Ono
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Story Generation

A script is a type of knowledge representation in artificial intelligence (AI). This paper presents two methods for synthetically using collected scripts for story generation. The first method recursively generates long sequences of events and the second creates script networks. Although related studies generally use one or more scripts for story generation, this research synthetically uses many scripts to flexibly generate a diverse narrative.
CaseSummarizer: A System for Automated Summarization of Legal Texts
Authors: Seth Polsley, Pooja Jhunjhunwala, Ruihong Huang
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Text Generation, Text Summarization

Attorneys, judges, and others in the justice system are constantly surrounded by large amounts of legal text, which can be difficult to manage across many cases. We present CaseSummarizer, a tool for automated text summarization of legal documents which uses standard summary methods based on word frequency augmented with additional domain-specific knowledge. Summaries are then provided through an informative interface with abbreviations, significance heat maps, and other flexible controls. It is evaluated using ROUGE and human scoring against several other summarization systems, including summary text and feedback provided by domain experts.
WISDOM X, DISAANA and D-SUMM: Large-scale NLP Systems for Analyzing Textual Big Data
Authors: Junta Mizuno, Masahiro Tanaka, Kiyonori Ohtake, Jong-Hoon Oh, Julien Kloetzer, Chikara Hashimoto, Kentaro Torisawa
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Open-Domain Question Answering, Question Answering

We demonstrate our large-scale NLP systems: WISDOM X, DISAANA, and D-SUMM. WISDOM X provides numerous possible answers including unpredictable ones to widely diverse natural language questions to provide deep insights about a broad range of issues. DISAANA and D-SUMM enable us to assess the damage caused by large-scale disasters in real time using Twitter as an information source.