Relations computed by finite-state transducers are applied to interpret temporal propositions in terms of strings representing finite contexts or situations. Carnap–Montague intensions mapping indices to extensions are reformulated as relations between strings that can serve as indices and extensions alike. Strings are related according to information content, temporal span and granularity, the bounds on which reflect the partiality of natural language statements. That partiality shapes not only strings-as-extensions (indicating what statements are about) but also strings-as-indices (underlying truth conditions).

Quantified expressions present an interesting case for understanding pronominal reference because the quantifier which appears in the expression affects how the pronoun will be understood. The quantifier determines which sets will be placed in focus and predicts the relation between the sentence containing the quantifier and the following sentence (Moxey and Sanford 1993a). This information is essential to understanding the pronominal reference. In this paper, we discuss how a natural language processing system can take advantage of this information to understand pronominal references to quantified expressions.

This special issue presents the state-of-the-art in implemented, general-purpose Natural Language Processing (NLP) systems that use nontrivial Knowledge Representation and Reasoning (KRR). These systems use full-scale implementations of traditional KRR techniques as well as some newer knowledge-related processing mechanisms that have been developed specifically to meet the needs of natural language processing. The papers cover a wide range of natural language inputs, knowledge and formalisms, application domains and processing tasks, illustrating the key role that knowledge representation plays in all types of NLP systems.

Suppose you're a corporate vice president at a well-known international software company, and you want to check on the visibility of one of your leading researchers in the outside world. You're sitting at your desk, so the most obvious thing to do is to enter their name into a search engine. If the well-known international software company happened to be Microsoft, and if the leading researcher happened to be Microsoft's Susan Dumais, and if the search engine you decided to use happened to be Google, you might be surprised to find that the sponsored link that comes atop the search results is actually from Google itself, exhorting you to ‘Work on NLP at Google’, and alerting you to the fact that ‘Google is hiring experts in statistical language processing’.

Web search is an experience that naturally lends itself to recommendations, including query suggestions and related entities. In this article, we propose to recommend specific tasks to users, based on their search queries, such as planning a holiday trip or organizing a party. Specifically, we introduce the problem of query-based task recommendation and develop methods that combine well-established term-based ranking techniques with continuous semantic representations, including sentence representations from several transformer-based models. Using a purpose-built test collection, we find that our method is able to significantly outperform a strong text-based baseline. Further, we extend our approach to using a set of queries that all share the same underlying task, referred to as search mission, as input. The study is rounded off with a detailed feature and query analysis.

Technical writing in professional environments, such as user manual authoring, requires the use of uniform language. Nonuniform language refers to sentences in a technical document that are intended to have the same meaning within a similar context, but use different words or writing style. Addressing this nonuniformity problem requires the performance of two tasks. The first task, which we named nonuniform language detection (NLD), is detecting such sentences. We propose an NLD method that utilizes different similarity algorithms at lexical, syntactic, semantic and pragmatic levels. Different features are extracted and integrated by applying a machine learning classification method. The second task, which we named nonuniform language correction (NLC), is deciding which sentence among the detected ones is more appropriate for that context. To address this problem, we propose an NLC method that combines contraction removal, near-synonym choice, and text readability comparison. We tested our methods using smartphone user manuals. We finally compared our methods against state-of-the-art methods in paraphrase detection (for NLD) and against expert annotators (for both NLD and NLC). The experiments demonstrate that the proposed methods achieve performance that matches expert annotators.

With the increase of user-generated content on social media, the detection of abusive language has become crucial and is therefore reflected in several shared tasks that have been performed in recent years. The development of automatic detection systems is desirable, and the classification of abusive social media content can be solved with the help of machine learning. The basis for successful development of machine learning models is the availability of consistently labeled training data. But a diversity of terms and definitions of abusive language is a crucial barrier. In this work, we analyze a total of nine datasets—five English and four German datasets—designed for detecting abusive online content. We provide a detailed description of the datasets, that is, for which tasks the dataset was created, how the data were collected, and its annotation guidelines. Our analysis shows that there is no standard definition of abusive language, which often leads to inconsistent annotations. As a consequence, it is difficult to draw cross-domain conclusions, share datasets, or use models for other abusive social media language tasks. Furthermore, our manual inspection of a random sample of each dataset revealed controversial examples. We highlight challenges in data annotation by discussing those examples, and present common problems in the annotation process, such as contradictory annotations and missing context information. Finally, to complement our theoretical work, we conduct generalization experiments on three German datasets.

Named entity recognition (NER) aims to identify mentions of named entities in an unstructured text and classify them into predefined named entity classes. While deep learning-based pre-trained language models help to achieve good predictive performances in NER, many domain-specific NER applications still call for a substantial amount of labeled data. Active learning (AL), a general framework for the label acquisition problem, has been used for NER tasks to minimize the annotation cost without sacrificing model performance. However, the heavily imbalanced class distribution of tokens introduces challenges in designing effective AL querying methods for NER. We propose several AL sentence query evaluation functions that pay more attention to potential positive tokens and evaluate these proposed functions with both sentence-based and token-based cost evaluation strategies. We also propose a better data-driven normalization approach to penalize sentences that are too long or too short. Our experiments on three datasets from different domains reveal that the proposed approach reduces the number of annotated tokens while achieving better or comparable prediction performance with conventional methods.

Randomized prospective studies represent the gold standard for experimental design. In this paper, we present a randomized prospective study to validate the benefits of combining rule-based and data-driven natural language understanding methods in a virtual patient dialogue system. The system uses a rule-based pattern matching approach together with a machine learning (ML) approach in the form of a text-based convolutional neural network, combining the two methods with a simple logistic regression model to choose between their predictions for each dialogue turn. In an earlier, retrospective study, the hybrid system yielded a nearly 50% error reduction on our initial data, in part due to the differential performance between the two methods as a function of label frequency. Given these gains, and considering that our hybrid approach is unique among virtual patient systems, we compare the hybrid system to the rule-based system by itself in a randomized prospective study. We evaluate 110 unique medical student subjects interacting with the system over 5,296 conversation turns, to verify whether similar gains are observed in a deployed system. This prospective study broadly confirms the findings from the earlier one but also highlights important deficits in our training data. The hybrid approach still improves over either rule-based or ML approaches individually, even handling unseen classes with some success. However, we observe that live subjects ask more out-of-scope questions than expected. To better handle such questions, we investigate several modifications to the system combination component. These show significant overall accuracy improvements and modest F1 improvements on out-of-scope queries in an offline evaluation. We provide further analysis to characterize the difficulty of the out-of-scope problem that we have identified, as well as to suggest future improvements over the baseline we establish here.

We examine the binary classification of sentiment views for verbal multiword expressions (MWEs). Sentiment views denote the perspective of the holder of some opinion. We distinguish between MWEs conveying the view of the speaker of the utterance (e.g., in “The company reinvented the wheel” the holder is the implicit speaker who criticizes the company for creating something already existing) and MWEs conveying the view of explicit entities participating in an opinion event (e.g., in “Peter threw in the towel” the holder is Peter having given up something). The task has so far been examined on unigram opinion words. Since many features found effective for unigrams are not usable for MWEs, we propose novel ones taking into account the internal structure of MWEs, a unigram sentiment-view lexicon and various information from Wiktionary. We also examine distributional methods and show that the corpus on which a representation is induced has a notable impact on the classification. We perform an extrinsic evaluation in the task of opinion holder extraction and show that the learnt knowledge also improves a state-of-the-art classifier trained on BERT. Sentiment-view classification is typically framed as a task in which only little labeled training data are available. As in the case of unigrams, we show that for MWEs a feature-based approach beats state-of-the-art generic methods.

Our field has enjoyed amazing growth over the years. Is this a good thing or a bad thing, or just a thing? Good: Growth sounds good. It is hard to imagine a politician arguing against jobs. There are more people working in the field than ever before, and they are publishing more and more, and creating more and more value. What could be wrong with that? Bad: Whatever you measure you get. We are all under too much pressure to publish too much too quickly. Students are graduating these days with more publications than what used to be expected for tenure. So many people are publishing so much that no one has time to think great thoughts, or take time to learn about things that may not be directly relevant to the next publication. Neutral: Inflation is a fact of life. There are long-term macro trends on publication rates that are beyond our control. These trends hold over tens and hundreds of years, and will continue over the foreseeable future.

This paper proposes a robust approach to parsing suitable for Information Extraction (IE) from texts using finite-state cascades. The approach is characterized by the construction of an approximation of the full parse tree that captures all the information relevant for IE purposes, leaving the other relations underspecified. Sequences of cascades of finite-state rules deterministically analyze the text, building unambiguous structures. Initially basic chunks are analyzed; then clauses are recognized and nested; finally modifier attachment is performed and the global parse tree is built. The parsing approach allows robust, effective and efficient analysis of real world texts. The grammar organization simplifies changes, insertion of new rules and integration of domain-oriented rules. The approach has been tested for Italian, English, and Russian. A parser based on such an approach has been implemented as part of Pinocchio, an environment for developing and running IE applications.

This paper presents morpheme-based language models developed for Amharic (a morphologically rich Semitic language) and their application to a speech recognition task. A substantial reduction in the out of vocabulary rate has been observed as a result of using subwords or morphemes. Thus a severe problem of morphologically rich languages has been addressed. Moreover, lower perplexity values have been obtained with morpheme-based language models than with word-based models. However, when comparing the quality based on the probability assigned to the test sets, word-based models seem to fare better. We have studied the utility of morpheme-based language models in speech recognition systems and found that the performance of a relatively small vocabulary (5k) speech recognition system improved significantly as a result of using morphemes as language modeling and dictionary units. However, as the size of the vocabulary increases (20k or more) the morpheme-based systems suffer from acoustic confusability and did not achieve a significant improvement over a word-based system with an equivalent vocabulary size even with the use of higher order (quadrogram) n-gram language models.

This special issue brings together representative views on what has come to be known as "best practice" in the development and evaluation of spoken language dialogue systems (SLDSs). The issue was initiated in the context of the European Esprit project DISC, which ran from June 1997 till February 2000. DISC's main goal was to identify current practice in both the development and the evaluation of SLDSs, in order to arrive at a useful definition and description of best practice. The project has resulted in a collection of guidelines which are intended for different target groups, in particular developers, deployers and customers.DISC partners were: Natural Interactive Systems Laboratory, Odense University, Denmark (coordination); Department of Speech, Music and Hearing (KTH), Stockholm, Sweden; Human-Machine Communication Department, CNRS-LIMSI, Orsay, France; Institute for Natural Language Processing (IMS), University of Stuttgart, Germany; Vocalis Ltd, Cambridge, United Kingdom; DaimlerChrysler Research Center Ulm, Germany; and the ELSNET foundation, Utrecht, The Netherlands.

The last few years the interest in SLDSs has increased enormously. At present there is a large number of systems available, many of them for commercial use. Their number is growing rapidly, and so are the variety of their functionalities and the diversity of their application domains. The tasks that advanced systems are able to perform are often more complex, less stereotypical, and are often carried out in the context of several interconnected domains of application. With these advances have come higher expectations of the naturalness and intelligence with which SLDSs fulfill their assignments, and as a consequence the interest in such systems has even grown more, both within academic and commercial circles. As far as natural human- system interaction is concerned, one significant change in SLDS design concerns the interaction between natural language understanding and dialogue management. Here we see a clear tendency towards models that incorporate a substantial amount of discourse semantics and make use of some conception of context-change. This allows for more natural interactions between the system and its human users, due on the one hand to the system's improved ability to compute the intended meaning of the user's input and on the other to the increased sophistication of the strategies it uses for planning its own responses. Such improved capacities are crucial when the system is to leave more of the initiative to the user, instead of keeping the dialogue on a narrowly circumscribed path of largely predictable exchanges. Further, there is a tendency to combine spoken language human-system interaction with other modalities of information exchange and representation (e.g., images and gestures), asking for both modality-specific and modality-integrating syntactic and semantic processing capabilities. All these developments have led to a situation in which there is a great need, shared by developers, deployers and customers alike, for effective guidelines, which will enable them to make accurate and successful design and implementation decisions, in accordance with broad consensus of what must be best practice in this particular engineering domain.

Finite state methods have been in common use in various areas of natural language processing (NLP) for many years. A series of specialized workshops in this area illustrates this. In 1996, András Kornai organized a very successful workshop entitled Extended Finite State Models of Language. One of the results of that workshop was a special issue of Natural Language Engineering (Volume 2, Number 4). In 1998, Kemal Oflazer organized a workshop called Finite State Methods in Natural Language Processing. A selection of submissions for this workshop were later included in a special issue of Computational Linguistics (Volume 26, Number 1). Inspired by these events, Lauri Karttunen, Kimmo Koskenniemi and Gertjan van Noord took the initiative for a workshop on finite state methods in NLP in Helsinki, as part of the European Summer School in Language, Logic and Information. As a related special event, the 20th anniversary of two-level morphology was celebrated. The appreciation of these events led us to believe that once again it should be possible, with some additional submissions, to compose an interesting special issue of this journal.