pwc-archive/papers-with-abstracts huggingface dataset:
Kathaa : NLP Systems as Edge-Labeled Directed Acyclic MultiGraphs
Authors: Sharada Mohanty, Nehal J Wani, Manish Srivastava, Dipti Sharma
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Machine Translation, Translation

We present Kathaa, an Open Source web-based Visual Programming Framework for Natural Language Processing (NLP) Systems. Kathaa supports the design, execution and analysis of complex NLP systems by visually connecting NLP components from an easily extensible Module Library. It models NLP systems an edge-labeled Directed Acyclic MultiGraph, and lets the user use publicly co-created modules in their own NLP applications irrespective of their technical proficiency in Natural Language Processing. Kathaa exposes an intuitive web based Interface for the users to interact with and modify complex NLP Systems; and a precise Module definition API to allow easy integration of new state of the art NLP components. Kathaa enables researchers to publish their services in a standardized format to enable the masses to use their services out of the box. The vision of this work is to pave the way for a system like Kathaa, to be the Lego blocks of NLP Research and Applications. As a practical use case we use Kathaa to visually implement the Sampark Hindi-Panjabi Machine Translation Pipeline and the Sampark Hindi-Urdu Machine Translation Pipeline, to demonstrate the fact that Kathaa can handle really complex NLP systems while still being intuitive for the end user.
LAPPS/Galaxy: Current State and Next Steps
Authors: Nancy Ide, Keith Suderman, Eric Nyberg, James Pustejovsky, Marc Verhagen
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

The US National Science Foundation (NSF) SI2-funded LAPPS/Galaxy project has developed an open-source platform for enabling complex analyses while hiding complexities associated with underlying infrastructure, that can be accessed through a web interface, deployed on any Unix system, or run from the cloud. It provides sophisticated tool integration and history capabilities, a workflow system for building automated multi-step analyses, state-of-the-art evaluation capabilities, and facilities for sharing and publishing analyses. This paper describes the current facilities available in LAPPS/Galaxy and outlines the project{'}s ongoing activities to enhance the framework.
Automatic Analysis of Flaws in Pre-Trained NLP Models
Authors: Richard Eckart de Castilho
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Task 2

Most tools for natural language processing today are based on machine learning and come with pre-trained models. In addition, third-parties provide pre-trained models for popular NLP tools. The predictive power and accuracy of these tools depends on the quality of these models. Downstream researchers often base their results on pre-trained models instead of training their own. Consequently, pre-trained models are an essential resource to our community. However, to be best of our knowledge, no systematic study of pre-trained models has been conducted so far. This paper reports on the analysis of 274 pre-models for six NLP tools and four potential causes of problems: encoding, tokenization, normalization and change over time. The analysis is implemented in the open source tool Model Investigator. Our work 1) allows model consumers to better assess whether a model is suitable for their task, 2) enables tool and model creators to sanity-check their models before distributing them, and 3) enables improvements in tool interoperability by performing automatic adjustments of normalization or other pre-processing based on the models used.
Combining Human Inputters and Language Services to provide Multi-language support system for International Symposiums
Authors: Takao Nakaguchi, Masayuki Otani, Toshiyuki Takasaki, Toru Ishida
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Automatic Speech Recognition (ASR), Machine Translation, Speech Recognition, Speech Synthesis, Translation

In this research, we introduce and implement a method that combines human inputters and machine translators. When the languages of the participants vary widely, the cost of simultaneous translation becomes very high. However, the results of simply applying machine translation to speech text do not have the quality that is needed for real use. Thus, we propose a method that people who understand the language of the speaker cooperate with a machine translation service in support of multilingualization by the co-creation of value. We implement a system with this method and apply it to actual presentations. While the quality of direct machine translations is 1.84 (fluency) and 2.89 (adequacy), the system has corresponding values of 3.76 and 3.85.
Recurrent Neural Network with Word Embedding for Complaint Classification
Authors: Panuwat Assawinjaipetch, Kiyoaki Shirai, Virach Sornlertlamvanich, Sanparith Marukata
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Classification, General Classification, Language Modeling, Language Modelling, Named Entity Recognition (NER), Relation Extraction, Sentiment Analysis, Speech Recognition, Text Classification

Complaint classification aims at using information to deliver greater insights to enhance user experience after purchasing the products or services. Categorized information can help us quickly collect emerging problems in order to provide a support needed. Indeed, the response to the complaint without the delay will grant users highest satisfaction. In this paper, we aim to deliver a novel approach which can clarify the complaints precisely with the aim to classify each complaint into nine predefined classes i.e. acces-sibility, company brand, competitors, facilities, process, product feature, staff quality, timing respec-tively and others. Given the idea that one word usually conveys ambiguity and it has to be interpreted by its context, the word embedding technique is used to provide word features while applying deep learning techniques for classifying a type of complaints. The dataset we use contains 8,439 complaints of one company.
Universal dependencies for Uyghur
Authors: Marhaba Eli, Weinila Mushajiang, Tuergen Yibulayin, Kahaerjiang Abiderexiti, Yan Liu
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Cross-Lingual Transfer

The Universal Dependencies (UD) Project seeks to build a cross-lingual studies of treebanks, linguistic structures and parsing. Its goal is to create a set of multilingual harmonized treebanks that are designed according to a universal annotation scheme. In this paper, we report on the conversion of the Uyghur dependency treebank to a UD version of the treebank which we term the Uyghur Universal Dependency Treebank (UyDT). We present the mapping of the Uyghur dependency treebank{'}s labelling scheme to the UD scheme, along with a clear description of the structural changes required in this conversion.
A non-expert Kaldi recipe for Vietnamese Speech Recognition System
Authors: Hieu-Thi Luong, Hai-Quan Vu
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Vietnamese Speech Recognition

In this paper we describe a non-expert setup for Vietnamese speech recognition system using Kaldi toolkit. We collected a speech corpus over fifteen hours from about fifty Vietnamese native speakers and using it to test the feasibility of our setup. The essential linguistic components for the Automatic Speech Recognition (ASR) system was prepared basing on the written form of the language instead of expertise knowledge on linguistic and phonology as commonly seen in rich resource languages like English. The modeling of tones by integrating them into the phoneme and using the phonetic decision tree is also discussed. Experimental results showed this setup for ASR systems does yield competitive results while still have potentials for further improvements.
Evaluating Ensemble Based Pre-annotation on Named Entity Corpus Construction in English and Chinese
Authors: Tingming Lu, Man Zhu, Zhiqiang Gao, Yaocheng Gui
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Named Entity Recognition (NER)

Annotated corpora are crucial language resources, and pre-annotation is an usual way to reduce the cost of corpus construction. Ensemble based pre-annotation approach combines multiple existing named entity taggers and categorizes annotations into normal annotations with high confidence and candidate annotations with low confidence, to reduce the human annotation time. In this paper, we manually annotate three English datasets under various pre-annotation conditions, report the effects of ensemble based pre-annotation, and analyze the experimental results. In order to verify the effectiveness of ensemble based pre-annotation in other languages, such as Chinese, three Chinese datasets are also tested. The experimental results show that the ensemble based pre-annotation approach significantly reduces the number of annotations which human annotators have to add, and outperforms the baseline approaches in reduction of human annotation time without loss in annotation performance (in terms of F1-measure), on both English and Chinese datasets.
An Ontology for Language Service Composability
Authors: Yohei Murakami, Takao Nakaguchi, Donghui Lin, Toru Ishida
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

Fragmentation and recombination is a key to create customized language environments for supporting various intercultural activities. Fragmentation provides various language resource components for the customized language environments and recombination builds each language environment according to user{'}s request by combining these components. To realize this fragmentation and recombination process, existing language resources (both data and programs) should be shared as language services and combined beyond mismatch of their service interfaces. To address this issue, standardization is inevitable: standardized interfaces are necessary for language services as well as data format required for language resources. Therefore, we have constructed a hierarchy of language services based on inheritance of service interfaces, which is called language service ontology. This ontology allows users to create a new customized language service that is compatible with existing ones. Moreover, we have developed a dynamic service binding technology that instantiates various executable customized services from an abstract workflow according to user{'}s request. By using the ontology and service binding together, users can bind the instantiated language service to another abstract workflow for a new customized one.
Between Platform and APIs: Kachako API for Developers
Authors: Yoshinobu Kano
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

Different types of users require different functions in NLP software. It is difficult for a single platform to cover all types of users. When a framework aims to provide more interoperability, users are required to learn more concepts; users{'} application designs are restricted to be compliant with the framework. While an interoperability framework is useful in certain cases, some types of users will not select the framework due to the learning cost and design restrictions. We suggest a rather simple framework for the interoperability aiming at developers. Reusing an existing NLP platform Kachako, we created an API oriented NLP system. This system loosely couples rich high-end functions, including annotation visualizations, statistical evaluations, an-notation searching, etc. This API do not require users much learning cost, providing customization ability for power users while also allowing easy users to employ many GUI functions.
Vectors or Graphs? On Differences of Representations for Distributional Semantic Models
Authors: Chris Biemann
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Information Retrieval

Distributional Semantic Models (DSMs) have recently received increased attention, together with the rise of neural architectures for scalable training of dense vector embeddings. While some of the literature even includes terms like {`}vectors{'} and {`}dimensionality{'} in the definition of DSMs, there are some good reasons why we should consider alternative formulations of distributional models. As an instance, I present a scalable graph-based solution to distributional semantics. The model belongs to the family of {`}count-based{'} DSMs, keeps its representation sparse and explicit, and thus fully interpretable. I will highlight some important differences between sparse graph-based and dense vector approaches to DSMs: while dense vector-based models are computationally easier to handle and provide a nice uniform representation that can be compared and combined in many ways, they lack interpretability, provenance and robustness. On the other hand, graph-based sparse models have a more straightforward interpretation, handle sense distinctions more naturally and can straightforwardly be linked to knowledge bases, while lacking the ability to compare arbitrary lexical units and a compositionality operation. Since both representations have their merits, I opt for exploring their combination in the outlook.
``Beware the Jabberwock, dear reader!'' Testing the distributional reality of construction semantics
Authors: Gianluca Lebani, Aless Lenci, ro
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

Notwithstanding the success of the notion of construction, the computational tradition still lacks a way to represent the semantic content of these linguistic entities. Here we present a simple corpus-based model implementing the idea that the meaning of a syntactic construction is intimately related to the semantics of its typical verbs. It is a two-step process, that starts by identifying the typical verbs occurring with a given syntactic construction and building their distributional vectors. We then calculated the weighted centroid of these vectors in order to derive the distributional signature of a construction. In order to assess the goodness of our approach, we replicated the priming effect described by Johnson and Golberg (2013) as a function of the semantic distance between a construction and its prototypical verbs. Additional support for our view comes from a regression analysis showing that our distributional information can be used to model behavioral data collected with a crowdsourced elicitation experiment.
Regular polysemy: from sense vectors to sense patterns
Authors: Anastasiya Lopukhina, Konstantin Lopukhin
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Word Embeddings, Word Sense Disambiguation

Regular polysemy was extensively investigated in lexical semantics, but this phenomenon has been very little studied in distributional semantics. We propose a model for regular polysemy detection that is based on sense vectors and allows to work directly with senses in semantic vector space. Our method is able to detect polysemous words that have the same regular sense alternation as in a given example (a word with two automatically induced senses that represent one polysemy pattern, such as ANIMAL / FOOD). The method works equally well for nouns, verbs and adjectives and achieves average recall of 0.55 and average precision of 0.59 for ten different polysemy patterns.
The Power of Language Music: Arabic Lemmatization through Patterns
Authors: Mohammed Attia, Ayah Zirikly, Mona Diab
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Information Retrieval, LEMMA, Lemmatization

The interaction between roots and patterns in Arabic has intrigued lexicographers and morphologists for centuries. While roots provide the consonantal building blocks, patterns provide the syllabic vocalic moulds. While roots provide abstract semantic classes, patterns realize these classes in specific instances. In this way both roots and patterns are indispensable for understanding the derivational, morphological and, to some extent, the cognitive aspects of the Arabic language. In this paper we perform lemmatization (a high-level lexical processing) without relying on a lookup dictionary. We use a hybrid approach that consists of a machine learning classifier to predict the lemma pattern for a given stem, and mapping rules to convert stems to their respective lemmas with the vocalization defined by the pattern.
Towards a resource based on users' knowledge to overcome the Tip of the Tongue problem.
Authors: Michael Zock, Chris Biemann
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

Language production is largely a matter of words which, in the case of access problems, can be searched for in an external resource (lexicon, thesaurus). In this kind of dialogue the user provides the momentarily available knowledge concerning the target and the system responds with the best guess(es) it can make given this input. As tip-of-the-tongue (ToT)-studies have shown, people always have some knowledge concerning the target (meaning fragments, number of syllables, ...) even if its complete form is eluding them. We will show here how to tap on this knowledge to build a resource likely to help authors (speakers/writers) to overcome the ToT-problem. Yet, before doing so we need a better understanding of the various kinds of knowledge people have when looking for a word. To this end, we asked crowdworkers to provide some cues to describe a given target and to specify then how each one of them relates to the target, in the hope that this could help others to find the elusive word. Next, we checked how well a given search strategy worked when being applied to differently built lexical networks. The results showed quite dramatic differences, which is not really surprising. After all, different networks are built for different purposes; hence each one of them is more or less suited for a given task. What was more surprising though is the fact that the relational information given by the users did not allow us to find the elusive word in WordNet better than without it.
The CogALex-V Shared Task on the Corpus-Based Identification of Semantic Relations
Authors: Enrico Santus, Anna Gladkova, Stefan Evert, Aless Lenci, ro
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Language Acquisition, Paraphrase Generation

The shared task of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex-V) aims at providing a common benchmark for testing current corpus-based methods for the identification of lexical semantic relations (synonymy, antonymy, hypernymy, part-whole meronymy) and at gaining a better understanding of their respective strengths and weaknesses. The shared task uses a challenging dataset extracted from EVALution 1.0, which contains word pairs holding the above-mentioned relations as well as semantically unrelated control items (random). The task is split into two subtasks: (i) identification of related word pairs vs. unrelated ones; (ii) classification of the word pairs according to their semantic relation. This paper describes the subtasks, the dataset, the evaluation metrics, the seven participating systems and their results. The best performing system in subtask 1 is GHHH (F1 = 0.790), while the best system in subtask 2 is LexNet (F1 = 0.445). The dataset and the task description are available at \url{https://sites.google.com/site/cogalex2016/home/shared-task}.
CogALex-V Shared Task: GHHH - Detecting Semantic Relations via Word Embeddings
Authors: Mohammed Attia, Suraj Maharjan, Younes Samih, Laura Kallmeyer, Thamar Solorio
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Binary Classification, General Classification, Information Retrieval, Knowledge Graphs, regression, Semantic Similarity, Semantic Textual Similarity, Task 2, Word Embeddings

This paper describes our system submission to the CogALex-2016 Shared Task on Corpus-Based Identification of Semantic Relations. Our system won first place for Task-1 and second place for Task-2. The evaluation results of our system on the test set is 88.1{\%} (79.0{\%} for TRUE only) f-measure for Task-1 on detecting semantic similarity, and 76.0{\%} (42.3{\%} when excluding RANDOM) for Task-2 on identifying finer-grained semantic relations. In our experiments, we try word analogy, linear regression, and multi-task Convolutional Neural Networks (CNNs) with word embeddings from publicly available word vectors. We found that linear regression performs better in the binary classification (Task-1), while CNNs have better performance in the multi-class semantic classification (Task-2). We assume that word analogy is more suited for deterministic answers rather than handling the ambiguity of one-to-many and many-to-many relationships. We also show that classifier performance could benefit from balancing the distribution of labels in the training data.
CogALex-V Shared Task: Mach5 -- A traditional DSM approach to semantic relatedness
Authors: Stefan Evert
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

This contribution provides a strong baseline result for the CogALex-V shared task using a traditional {``}count{''}-type DSM (placed in rank 2 out of 7 in subtask 1 and rank 3 out of 6 in subtask 2). Parameter tuning experiments reveal some surprising effects and suggest that the use of random word pairs as negative examples may be problematic, guiding the parameter optimization in an undesirable direction.
CogALex-V Shared Task: CGSRC - Classifying Semantic Relations using Convolutional Neural Networks
Authors: Chinnappa Guggilla
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Machine Translation, Paraphrase Generation, Question Answering, Relation, Relation Classification, Word Embeddings

In this paper, we describe a system (CGSRC) for classifying four semantic relations: synonym, hypernym, antonym and meronym using convolutional neural networks (CNN). We have participated in CogALex-V semantic shared task of corpus-based identification of semantic relations. Proposed approach using CNN-based deep neural networks leveraging pre-compiled word2vec distributional neural embeddings achieved 43.15{\%} weighted-F1 accuracy on subtask-1 (checking existence of a relation between two terms) and 25.24{\%} weighted-F1 accuracy on subtask-2 (classifying relation types).
CogALex-V Shared Task: LOPE
Authors: Kanan Luce, Jiaxing Yu, Shu-Kai Hsieh
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Word Embeddings

Automatic discovery of semantically-related words is one of the most important NLP tasks, and has great impact on the theoretical psycholinguistic modeling of the mental lexicon. In this shared task, we employ the word embeddings model to testify two thoughts explicitly or implicitly assumed by the NLP community: (1). Word embedding models can reflect syntagmatic similarities in usage between words to distances in projected vector space. (2). Word embedding models can reflect paradigmatic relationships between words.
CogALex-V Shared Task: HsH-Supervised -- Supervised similarity learning using entry wise product of context vectors
Authors: Christian Wartena, Rosa Tsegaye Aga
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: General Classification

The CogALex-V Shared Task provides two datasets that consists of pairs of words along with a classification of their semantic relation. The dataset for the first task distinguishes only between related and unrelated, while the second data set distinguishes several types of semantic relations. A number of recent papers propose to construct a feature vector that represents a pair of words by applying a pairwise simple operation to all elements of the feature vector. Subsequently, the pairs can be classified by training any classification algorithm on these vectors. In the present paper we apply this method to the provided datasets. We see that the results are not better than from the given simple baseline. We conclude that the results of the investigated method are strongly depended on the type of data to which it is applied.