Text analytics in industry: Challenges,desiderata and trends

The recent decades have witnessed an unprecedented expansion in the volume of unstructured data in digital textual formats. Companies are now starting to recognize the potential economic value lying untapped in their text data repositories and sources, including external ones, such as social media platforms, and internal ones, such as safety reports and other company-specific document collections. Information extracted from these textual data sources is valuable for a range of enterprise application and for informed decision making. In this article we provide a systematic review of the current state of the art in the application of text analytics in industry. Our review is structured along three dimensions: the application context, the methods and techniques utilized, and the evaluation procedure. Based on the review, we identify the different challenges and constraints that an real-world, industrial environment imposes on text analytics techniques, as opposed to their deployment in more controlled, research environments. In addition, we formulate a set of desiderata that text analytics techniques should satisfy in order to alleviate these challenges and to ensure their successful deployment in industry. Furthermore, we discuss future trends in text analytics and their potential application in industry.     

Declarative semantics of transactions in ORM

In order to specify databases completely at the conceptual level, conceptual database specification languages should contain a data definition (sub)language (DDL), for specifying data structures (+constraints), a data retrieval (sub)language (DRL), for specifying queries, as well as a (declarative) data manipulation (sub)language (DML), for specifying transactions.Object Role Modeling (ORM) is a powerful method for designing and querying database models at the conceptual level. By means of verbalization the application is also described in natural language as used by domain experts, for communication and validation purposes. ORM currently comprises a DDL and a DRL (ConQuer). However, the ORM-method does not yet contain an expressive DML for specifying transactions at the conceptual level.In an earlier paper we designed a syntactic extension of the ORM-method with a DML for specifying transactions at the conceptual level in a purely declarative way. For all transactions we proposed syntaxes, verbalizations, and diagrams. However, we did not give a formal semantics then.The purpose of this paper is to add a clear, formal and purely declarative semantics to the proposed ORM-transactions. The paper also formally defines rollbacks and illustrates everything with examples (including a solution to a well-known transaction specification problem). The extension of ORM with an expressive set of completely declaratively specified transactions makes ORM complete as a database specification method at the conceptual level.     

First-past-the-post games

Informally, a first-past-the-post game is a (probabilistic) game where the winner is the person who predicts the event that occurs first among a set of events. Examples of first-past-the-post games include so-called block and hidden patterns and the Penney-Ante game invented by Walter Penney. We formalise the abstract notion of a first-past-the-post game, and the process of extending a probability distribution on symbols of an alphabet to the plays of a game. We establish a number of properties of such games, for example, the property that an incomplete first-past-the-post game is also a first-past-the-post game.Penney-Ante games are multi-player games characterised by a collection of regular, prefix-free languages. Analysis of such games is facilitated by a collection of simultaneous (non-linear) equations in languages. Essentially, the equations are due to Guibas and Odlyzko. However, they did not formulate them as equations in languages but as equations in generating functions detailing lengths of words. For such games, we show how to use the equations in languages to calculate the probability of winning and how to calculate the expected length of a game for a given outcome. We also exploit the properties of first-past-the-post games to show how to calculate the probability of winning in the course of a play of the game. In this way, we avoid the construction of a deterministic finite-state machine or the use of generating functions, the two methods traditionally used for the task.We observe that Aho and Corasick's generalisation of the Knuth–Morris–Pratt pattern-matching algorithm can be used to construct the deterministic finite-state machine that recognises the language underlying a Penney-Ante game. The two methods of calculating the probabilities and expected values, one based on the finite-state machine and the other based on the non-linear equations in languages, have been implemented and verified to yield the same results.     

Lightweight and static verification of UML executable models

Executable models play a key role in many software development methods by facilitating the (semi)automatic implementation/execution of the software system under development. This is possible because executable models promote a complete and fine-grained specification of the system behaviour. In this context, where models are the basis of the whole development process, the quality of the models has a high impact on the final quality of software systems derived from them. Therefore, the existence of methods to verify the correctness of executable models is crucial. Otherwise, the quality of the executable models (and in turn the quality of the final system generated from them) will be compromised. In this paper a lightweight and static verification method to assess the correctness of executable models is proposed. This method allows us to check whether the operations defined as part of the behavioural model are able to be executed without breaking the integrity of the structural model and returns a meaningful feedback that helps repairing the detected inconsistencies.     

Robust comprehension of natural language instructions by a domestic service robot

We present a method through which domestic service robots can comprehend natural language instructions. For each action type, a variety of natural language expressions can be used, for example, the instruction, ‘Go to the kitchen’ can also be expressed as ‘Move to the kitchen.’ We are of the view that natural language instructions are intuitive and, therefore, constitute one of the most user-friendly robot instruction methods. In this paper, we propose a method that enables robots to comprehend instructions spoken by a human user in his/her natural language. The proposed method combines action-type classification, which is based on a support vector machine, and slot extraction, which is based on conditional random fields, both of which are required in order for a robot to execute an action. Further, by considering the co-occurrence relationship between the action type and the slots along with the speech recognition score, the proposed method can avoid degradation of the robot’s comprehension accuracy in noisy environments, where inaccurate speech recognition can be problematic. We conducted experiments using a Japanese instruction data-set collected using a questionnaire-based survey. Experimental results show that the robot’s comprehension accuracy is higher in a noisy environment using our method than when using a baseline method with only a 1-best speech recognition result.     

Graph-based Lexicalized Reordering Models for Statistical Machine Translation

Lexicalized reordering models are very important components of phrasebased translation systems.By examining the reordering relationships between adjacent phrases,conventional methods learn these models from the word aligned bilingual corpus,while ignoring the effect of the number of adjacent bilingual phrases.In this paper,we propose a method to take the number of adjacent phrases into account for better estimation of reordering models.Instead of just checking whether there is one phrase adjacent to a given phrase,our method firstly uses a compact structure named reordering graph to represent all phrase segmentations of a parallel sentence,then the effect of the adjacent phrase number can be quantified in a forward-backward fashion,and finally incorporated into the estimation of reordering models.Experimental results on the NIST Chinese-English and WMT French-Spanish data sets show that our approach significantly outperforms the baseline method.     

Collecting and evaluating the CUNY ASL corpus for research on American Sign Language animation

While there is great potential for sign language animation generation software to improve the accessibility of information for deaf individuals with low written-language literacy, the understandability of current sign language animation systems is limited. Data-driven methodologies using annotated sign language corpora encoding detailed human movement have enabled some researchers to address several key linguistic challenges in ASL generation. This article motivates and describes our current research on collecting a motion-capture corpus of American Sign Language (ASL). As an evaluation of our motion-capture configuration, calibration, and recording protocol, we have conducted several rounds of evaluation studies with native ASL signers, and we have made use of our collected data to synthesize novel animations of ASL, which have also been evaluated in experimental studies with native signers.