Set Of Semantic Features Research Articles

Are We Talking about the Same Thing? Modeling Semantic Similarity between Common and Specialized Lexica in WordNet

Specialized languages can activate different sets of semantic features when compared to general language or express concepts through different words according to the domain. The specialized lexicon, i.e., lexical units that denote more specific concepts and knowledge emerging from specific domains, however, co-exists with the common lexicon, i.e., the set of lexical units that denote concepts and knowledge shared by the average speakers, regardless of their specific training or expertise. Communication between specialists and non-specialists can show a big gap between language(s), and therefore lexical units, used by the two groups. However, quite often, semantic and conceptual overlapping between specialized and common lexical units occurs and, in many cases, the specialized and common units refer to close concepts or even point to the same reality. Considering the modeling of meaning in functional lexical resources, this paper puts forth a solution that links common and specialized lexica within the WordNet model framework. We propose a new relation expressing semantic proximity between common and specialized units and define the conditions for its establishment. Besides contributing to the observation and understanding of the process of knowledge specialization and its reflex on the lexicon, the proposed relation allows for the integration of specialized and non-specialized lexicons into a single database, contributing directly to improving communication in specialist/non-specialist contexts, such as teaching–learning situations or health professional-patient interactions, among many others, where code-switching is frequent and necessary.

«Люди в черном»: семантизация понятий сектант и баптист в антирелигиозной кампании 1958—1964 годов

In the post-revolutionary era, the population's adoption of a new political vocabulary demanded the Soviet authorities to play the role of the interpretant. They engaged in the pro­cess of assigning meanings to new concepts through mass media and fiction. Simultaneously, established concepts had to undergo reshaping to align with the new worldview of the 'Soviet citizen’. The transformation of the semantic structure of religious vocabulary, ideologically marked, reflected the shift in the fundamental values of Soviet society. This article, employing component and contextual analyses, explores the target semantization process of key concepts during Khrushchev's 1958—1964 anti-religious campaign, specifically focusing on 'sektant' and 'baptist'. Through mass media and propaganda literature, a set of semantic features for these terms was imposed on the children's audience in ideological contexts. The analysis of linguistic material from numerous sources such as the newspaper Pionerskaya Pravda, Pioner and Koster children's magazines, and selected works of children's literature reveals that the target audience — school children — led to the emergence of semantic primitives. This in­vol­ved significantly narrowing the semantics of the words 'sektant' and 'baptist.' Linguistic tools such as new lexical pairs ('sectarian-Baptist,' 'fanatics and bigots,' 'subversives and villains') and linguistic markers related to colour, sound, appearance, age, and location were employed. These tools helped develop a negative image of a Protestant believer and ultimately transformed the concepts of ‘sectant’ and ‘baptist’ into political labels.

A descriptive framework for evaluative morphology resources

A thorough review of contemporary evaluative morphology (EM) research shows that descriptions of the EM resources available to languages tend to be unsystematic and lacking in thoroughness, given the absence of an established descriptive framework. The aim of this study is to propose a framework for the systematic description of the EM resources of natural languages. The discussion is structured according to various productivity indicators attested in the construction of evaluative forms (EVALs). The study reviews a previous proposal by Körtvélyessy (2015a), addressing some perceived shortcomings and expanding its scope. Notably, the proposed framework includes a description of a) all morphological processes involved in EVAL-formation, b) the availability of different word classes as bases in EVAL-formation, c) a set of semantic features for the dynamic interpretation of EVALs, and d) the recursive possibilities of evaluative markers. Aside from the description of EM resources, the proposed framework also suggests procedures for their numerical quantification, to obtain measurable indicators that may be further used in contrastive, typological and areal EM research.

How the Brain Dynamically Constructs Sentence-Level Meanings From Word-Level Features.

How are words connected to the thoughts they help to express? Recent brain imaging studies suggest that word representations are embodied in different neural systems through which the words are experienced. Building on this idea, embodied approaches such as the Concept Attribute Representations (CAR) theory represents concepts as a set of semantic features (attributes) mapped to different brain systems. An intriguing challenge to this theory is that people weigh concept attributes differently based on context, i.e., they construct meaning dynamically according to the combination of concepts that occur in the sentence. This research addresses this challenge through the Context-dEpendent meaning REpresentations in the BRAin (CEREBRA) neural network model. Based on changes in the brain images, CEREBRA quantifies the effect of sentence context on word meanings. Computational experiments demonstrated that words in different contexts have different representations, the changes observed in the concept attributes reveal unique conceptual combinations, and that the new representations are more similar to the other words in the sentence than to the original representations. Behavioral analysis further confirmed that the changes produced by CEREBRA are actionable knowledge that can be used to predict human responses. These experiments constitute a comprehensive evaluation of CEREBRA's context-based representations, showing that CARs can be dynamic and change based on context. Thus, CEREBRA is a useful tool for understanding how word meanings are represented in the brain, providing a framework for future interdisciplinary research on the mental lexicon.

Семантика глаголов звучания в сборнике А. Блока «Стихи о Прекрасной Даме»

The article studies the semantics of verbs of sounding in “Poems about the Beautiful Lady” by A. Blok. The aim of the study is to identify the individual author’s originality of the poetic use of words from this lexico-semantic group. The scientific originality of the work is determined by the fact that for the first time a detailed description of the verbs of sounding in the collection “Poems about the Beautiful Lady” is given, which contributes to deeper coverage of A. Blok’s idiostyle and more complete reconstruction of the author’s worldview. The results of the study made it possible to identify a set of semantic features that form the idea of sound in the collection, and to ascertain that verbs of sounding are involved in expressing the complex and tense relationship of the lyric hero with his beloved, forming a single semantic network.

New bag of deep visual words based features to classify chest x-ray images for COVID-19 diagnosis.

PurposeBecause the infection by Severe Acute Respiratory Syndrome Coronavirus 2 (COVID-19) causes the Pneumonia-like effect in the lung, the examination of Chest X-Rays (CXR) can help diagnose the disease. For automatic analysis of images, they are represented in machines by a set of semantic features. Deep Learning (DL) models are widely used to extract features from images. General deep features extracted from intermediate layers may not be appropriate to represent CXR images as they have a few semantic regions. Though the Bag of Visual Words (BoVW)-based features are shown to be more appropriate for different types of images, existing BoVW features may not capture enough information to differentiate COVID-19 infection from other Pneumonia-related infections.MethodsIn this paper, we propose a new BoVW method over deep features, called Bag of Deep Visual Words (BoDVW), by removing the feature map normalization step and adding the deep features normalization step on the raw feature maps. This helps to preserve the semantics of each feature map that may have important clues to differentiate COVID-19 from Pneumonia.ResultsWe evaluate the effectiveness of our proposed BoDVW features in CXR image classification using Support Vector Machine (SVM) to diagnose COVID-19. Our results on four publicly available COVID-19 CXR image datasets (D1, D2, D3, and D4) reveal that our features produce stable and prominent classification accuracy (82.00% on D1, 87.86% on D2, 87.92% on D3, and 83.22% on D4), particularly differentiating COVID-19 infection from other Pneumonia.ConclusionOur method could be a very useful tool for the quick diagnosis of COVID-19 patients on a large scale.

Exploring the correlation between semantic descriptors and texture analysis features in brain MRI

Brain MRI texture analysis has a lot of potential implications in neuroradiology but suffers from significant flaws slowing its use in daily practice. Texture analysis features are perceived as abstract which makes the results of texture analysis hard to interpret and to convey to other physicians and the patient. The study’s objective was to correlate computed texture analysis features with a simple set of semantic features to improve their explainability. A dataset of 300 brain MRI texture was built (T1-, T2- and diffusion-weighted slices). Textures from this dataset were assessed by two raters according to the following set of semantic dimensions: repetitiveness, directionality, linearity, roughness, randomness and coarseness. Thirty-two TA features (second-order and fractal features) were extracted from the same dataset and correlated with the aforementioned semantic dimensions. A good inter-rater reliability was observed in the assessment of semantic dimensions. The two most strongly associated semantic dimensions in 31 of 32 studied features belonged to the following set: repetitiveness, roughness, randomness, coarseness. An adequately chosen pair from this set explained at least 40% of the variance in 16 of 32 studied features (multiple quadratic regression). This study shows that a priori abstract texture analysis features extracted from brain MRI images correlate well with simple semantic descriptors. The property of these texture analysis features to be described semantically might help to enhance the interpretability of texture analysis and to communicate the results to both the other physicians and the patients.

Semantic and linguocultural features of English and Uzbek proverbs with concept of friendship

The concept of "friendship" is one of the main categories of ethical character that determines the spiritual values of a national culture. The concept of "friendship" is a complex, on the one hand, but interesting, on the other hand, the object of research in a comparative aspect. This concept has its own slots: mental, moral, ethical, spiritual, etc., and "when comparing, a set of semantic features is formed, the totality of which forms a "standard of comparison".The study of the concept of "friendship" is one of the most important inexhaustible sources of social, national and cultural information about native speakers of a particular language. At the present stage of scientific development, this aspect is undoubtedly becoming relevant and is explained by the need to study it for a deep understanding of the culture of a particular nation or ethnic group.

КОНЦЕПТ «ЛИТЕРАТУРА» В ПЕРЕПИСКЕ И. С. ТУРГЕНЕВА И Ф. М. ДОСТОЕВСКОГО

The article examines the concept of literature as a unit of the linguistic image of the world of two great writers, which is associated with each of the authors’ comprehension and figurative interpretation of the literary process and journalistic activity in the 19th century. Using the letters of Turgenev and Dostoevsky during their conspicuous interaction (1860—1867) the author researches the linguistic methods of concept representation, its semantic volume, identifies a set of semantic features, explores the use of various evaluative vocabulary as a means of explication of this concept, establishes stimulus words in the correspondence. Turgenev perceives the sphere of literature as something true, superior and even miraculous, Dostoevsky proposes both a positive and a negative assessment of the events occurring in the literary process. The analysis of the two writers’ correspondence made it possible to disclose the attitude of correspondents to various literary spheres, to reveal the specific features of the concept of literature in the conceptual sphere of Turgenev and Dostoevsky. Accordingly, it was determined that the language units of the concept of literature as perceived by the Russian intelligentsia contain emotive components, and that the epistolary texts contain elements of intimization and frequent use of authors’ specific language techniques, comparisons and metaphorical images. The notions of literature and the literary process are conceptualized in writers’ letters as an animate acting force, an environment that is capable of action. The figurative language means used by writers in the correspondence endow the concepts with a specific image and sensual meaning. The concept of literature reveals the features of their work, the literary process and journalistic activities, which were an essential part of themselves and their lives for both Turgenev and Dostoevsky.

Культурная значимость слова волк по материалам фразеологии (диахронический аспект)

Предмет статьи – устойчивые выражения номинативного и коммуникативного типов, включающие опорное слово «волк». Рассматриваются символические значения слова «волк», реализуемые во фразеологизмах, составляющих семантическое поле более чем из 370 единиц. Цель работы – описать культурную значимость слова «волк», образуемую символическими значениями (символическими признаками) слова. Культурная значимость слова рассматривается в диахроническом аспекте: от ее архаического состояния в праславянский период к семантике древнерусского времени, с учетом оценочной модификации после принятия христианства и последующей трансляции традиционных признаков в современном русском языке. В работе применяется сравнительно-исторический метод, позволивший реконструировать древнерусскую пословицу (*про вълка гълка, а вълкъ из колъка) и архаичное символическое значение слова волк – ‘злой пастух’. Это значение опирается на мифологические представления праславянской эпохи: анализ пословиц показывает, что волк воспринимался как воплощение языческого божества плодородия Ярилы, сохраняющего скот и принимающего жертвы. Полученные выводы опираются на структурно-семантический метод, в соответствии с которым культурная значимость слова «волк» складывается из совокупности семантических признаков, полученных при анализе отдельных языковых единиц четырех секторов (‘овцы’, ‘одушевленный объект’, ‘ситуация взаимодействия с волками’, ‘стереотипные качества’) фразеологического семантического поля. В результате анализа был определен символический образ волка: неизменный и бескомпромиссный противник человека, опасный обитатель чужого, потустороннего мира, способный вторгаться в обыденный мир, жестокий и решительный хищник, чей характер определен самим мироустройством. Полученные результаты актуальны для исторической лексикологии и лингвокультурологии и могут быть использованы при составлении исторического словаря символических значений. Проведенным анализом установлено, что происхождение символических значений слова «волк» связано с мифологическими представлениями аграрного общества, с древним содержанием русского народного праздника Юрьев день (весенний).

Latin Case System: Towards a Motivated Paradigmatic Structure

The article attempts, firstly, to critically analyze the traditional order of cases in Latin, secondly, to discover an internal mechanism that brings the elements of a paradigm together, and thirdly, to present a new model of the nominal and pronominal case paradigms in Latin. The authors develop the idea that the crucial role in structuring a case paradigm belongs to morphemic syncretism. The syncretism is treated as a systemic phenomenon of morpheme neutralization rather than a result of phonetic reduction. In the paradigm built on this principle, the cases marked with the same endings necessarily take adjacent positions. There is a certain correlation between the morphemic syncretism and the semantics of cases extensively exemplified in the Latin literature. Taking this as reference point, the authors establish a formally motivated paradigmatic order of cases and single out a set of semantic features that shape the case paradigm. This method enables authors to find the non-contradictory paradigmatic positions for both the core and the “marginal” cases (vocative and locative). Applied to the pronominal cases, however, it reveals the significant discrepancy between the nominal and pronominal paradigms concerning two cases — nominative and genitive. The pronominal nominative’s special status is determined by its pragmatic rather than syntactic functions, which is typical for pro-drop languages. The genitive case appears in three different forms that originate from the possessive pronouns and correspond to the three basic functions of the genitive — possessive, objective, and partitive ones. Such transparadigmatic syncretism brings together the paradigms of personal and possessive pronouns, which are related by nature. The approach suggested in this study makes it possible to present in a new way the nominal and pronominal case paradigms, to demonstrate in what points they differ from each other, and to highlight some functional and semantic features of the particular cases.

Modeling early lexico-semantic network development: Perceptual features matter most.

What aspects of word meaning are important in early word learning and lexico-semantic network development? Adult lexico-semantic systems flexibly encode multiple types of semantic features, including functional, perceptual, taxonomic, and encyclopedic. However, various theoretical accounts of lexical development differ on whether and how these semantic properties of word meanings are initially encoded into young children's emerging lexico-semantic networks. Whereas some accounts highlight the importance of early perceptual versus conceptual properties, others posit that thematic or functional aspects of word meaning are primary relative to taxonomic knowledge. We seek to shed light on these debates with 2 modeling studies that explore patterns in early word learning using a large database of early vocabulary in 5,450 children, and a newly developed set of semantic features of early acquired nouns. In Study 1, we ask whether semantic properties of early acquired words relate to order in which these words are typically learned; Study 2 models normative lexico-semantic noun-feature network development compared to random network growth. Both studies provide converging evidence that perceptual properties of word meanings play a key role in early word learning and lexico-semantic network development. The findings lend support to theoretical accounts of language learning that highlight the importance of the child's perceptual experience. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Reconstructing meaning from bits of information

Modern theories of semantics posit that the meaning of words can be decomposed into a unique combination of semantic features (e.g., “dog” would include “barks”). Here, we demonstrate using functional MRI (fMRI) that the brain combines bits of information into meaningful object representations. Participants receive clues of individual objects in form of three isolated semantic features, given as verbal descriptions. We use machine-learning-based neural decoding to learn a mapping between individual semantic features and BOLD activation patterns. The recorded brain patterns are best decoded using a combination of not only the three semantic features that were in fact presented as clues, but a far richer set of semantic features typically linked to the target object. We conclude that our experimental protocol allowed us to demonstrate that fragmented information is combined into a complete semantic representation of an object and to identify brain regions associated with object meaning.

‘Who Likes What and, Why?’ Insights into Modeling Users’ Personality Based on Image ‘Likes’

The increased proliferation of data production technologies (e.g., cameras) and consumption avenues (e.g., social media) has led to images and videos being utilized by users to convey innate preferences and tastes. This has opened up the possibility of using multimedia as a source for user-modeling. This work attempts to model personality traits (based on the Five Factor Theory) of users using a collection of images they tag as ‘favorite’ (or like) on Flickr. First, a set of semantic features are proposed to be used for representing different concepts in images which influence users to like them. The addition of the proposed features led to improvement over state-of-the-art by 12 percent. Second, a novel machine learning approach is developed to model users’ personality based on the image features (resulting in upto 15 percent improvement). Third, efficacy of the semantic features and the modeling approach is shown in recommending images based on personality modeling. Using the modeling approach, recommendations are made regarding the factors that might influence users with different personality traits to like an image.

Features Mining and Analysis of Smart Bracelet Based on Online Reviews

As the basis of review analysis, product feature mining is a prerequisite to sentiment analysis. We aim to construct a relatively complete set of semantic features for a kind of wearable smart devices. Firstly, we mined features based on reviews using the association rules and word frequency statistics. Then combining the semantic knowledge and a feature set extracted from the product specification, we completed the improvement and extension of the feature set of smart bracelet. Finally, using the excavated features, we analyzed concerns of a selected smart bracelet. The results of this paper can be used as the basic work for the review mining of smart bracelet, and the analysis of concerns can help to guide businesses to improve products performance and service.

The semantic and syntactic differences between 至于 and 至於 in Warring States period texts = 戰國文獻裡出現的'至于'、'至於'的語意上、語法上的差異

This paper supports and extends the results of previous studies, such as Pulleyblank (1986) and Chang (2012), that argue that the choice of 于 or 於 in Classical Chinese is not random, but rather is conditioned by the features of the preceding verb. Through a close examination of the cooccurrence of 于 and 於 with the high-frequency verb 至 in six Warring States period texts, it is shown that additional semantic and syntactic factors influence the choice between the two. Semantically speaking, while both 至于 and 至於 occur followed by a noun indicating a place, a time, or a person—all of which can be described as [+concrete] goals—it is only 至於 that occurs followed by a degree, result, or extent, which can be described as [-concrete] goals. The [-concrete] goals that appear as objects of 至於 include a near demonstrative pronoun 此, a noun (including an abstract noun) or noun phrase, a verb phrase, and even a sentence. Syntactically speaking, only 至於+ Noun appears in nominalized contexts in complex syntactic structures, such as before a nominalizer 者, before or after a relativizer 所, and as a head noun after a possessive/attributive marker 之. These findings provide additional support for the claim that 于 and 於 represent distinct lexical items, with distinct semantic feature sets, in Classical Chinese. 提要: 本文經過調查戰國文獻裡出現的'至于'和'至於'的例子來證明它們 肯定有語意上、語法上的差異. 在語意上, 只有'至於'後邊出現表示 程度或者結果的成分. 表示程度或者結果的成分包括近指代詞 '此', 名詞句(包括抽象名詞), 動詞句, 甚至連主謂結構都可以出現在'至於' 的後邊表示 達到這樣的程度或者結果. 在語法上, 只有 '至於' 在複雜的語法結構裡出現. 複雜的語法結構就是說含有名詞化標記 '者' 、 '所' 、 '之' 等的句子. 以前, 作者曾經主張過'于' 或 '於'的選擇常常跟前邊的動詞的性質來決定. 本文強調'于' 或 '於'的選擇不但跟前邊的動詞的性質有關, 而且跟後邊的名詞的性質 和'于' 或 '於'出現的句子的語法結構有關.

Event-based Multi-document Summarization

Daily amount of news reporting real-world events is growing exponentially. At the same time, Organizations are looking for information about current and past events that affects them, such as mergers and acquisitions of companies. The Organizations need to obtain event information in a fast and summarized form to make decisions. Event-based retrieval and summarization systems offer an efficient solution to this problem. Most summarization research work uses news stories. Although this type of documents is characterized by conveying information about events, almost all work concentrates on approaches that do not take into account this aspect. The proposed multi-document summarization methods are based on the hierarchical combination of single-document summaries. We improved our multi-document summarization methods using event information. Our approach is based on a two-stage single-document\ method that extracts a collection of key phrases, which are then used in a centrality-asrelevance passage retrieval model. To adapt centrality-as-relevance single-document summarization for multi-document summarization that is able to use event information, we needed a good and adaptable baseline system. Because the key phrase extraction play a significant role in the summarization, we improved a state-of-the-art key phrase extraction toolkit using four additional sets of semantic features. The event detection method is based on Fuzzy Fingerprint, which is a supervised method trained on documents with annotated event tags. We explored three different ways to integrate event information, achieving state-of-the-art results in both single and multi-document summarization using filtering and event-based features. To cope with the possible usage of different terms to describe the same event, we explored distributed representations of text in the form of word embeddings, which contributed to improve the multi-document summarization results. The automatic evaluation and user study performed show that these methods improve upon current state-of-the-art multi-document summarization systems on two mainstream evaluation datasets, DUC 2007 and TAC 2009. We show a relative improvement in ROUGE- 1 scores of 16% for TAC 2009 and of 17% for DUC 2007. We have also obtained improvements in ROUGE-1 upon current state-of-the-art single-document summarization systems of between 32% in clean data and 19% in noisy data. These improvements derived from the inclusion of key phrases and event information. The extraction of key phrases was also refined with additional pre-processing steps and features, which lead to a relative improvement in NDCG scores of 9%. The introduction of Fuzzy Fingerprints for event detection enabled the detection of all event types, while the best competitor, an SVM with enhanced features, only detects roughly 85% of the different types of events. This lead to a large increase in the G-Mean and variants results when using the Fuzzy Fingerprints method. This doctoral work was carried under the Dual Ph.D. program in Languages and Information Technologies of Carnegie Mellon Portugal Program. The program took place at both the Language Technologies Institute at Carnegie Mellon University (CMU) and at the Department of Computer of Science and Engineering at Instituto Superior Técnico (IST). The doctoral work was performed under the supervision of University Professor Jaime Carbonell (CMU), Distinguished Career Professor Anatole Gershman (CMU), Assistant Professor David Martins de Matos (IST), and Assistant Professor Joo P. Neto (IST). Full Professor Ricardo Baeza-Yates (Yahoo!Research/Univ. Pompeu Fabra/Univ. de Chile), Research Professor Eduard Hovy (CMU), Associate Professor Ana Paiva (IST), and Full Professor Isabel Trancoso served as dissertation committee members.

Action relevance in linguistic context drives word-induced motor activity.

Many neurocognitive studies on the role of motor structures in action-language processing have implicitly adopted a “dictionary-like” framework within which lexical meaning is constructed on the basis of an invariant set of semantic features. The debate has thus been centered on the question of whether motor activation is an integral part of the lexical semantics (embodied theories) or the result of a post-lexical construction of a situation model (disembodied theories). However, research in psycholinguistics show that lexical semantic processing and context-dependent meaning construction are narrowly integrated. An understanding of the role of motor structures in action-language processing might thus be better achieved by focusing on the linguistic contexts under which such structures are recruited. Here, we therefore analyzed online modulations of grip force while subjects listened to target words embedded in different linguistic contexts. When the target word was a hand action verb and when the sentence focused on that action (John signs the contract) an early increase of grip force was observed. No comparable increase was detected when the same word occurred in a context that shifted the focus toward the agent's mental state (John wants to sign the contract). There mere presence of an action word is thus not sufficient to trigger motor activation. Moreover, when the linguistic context set up a strong expectation for a hand action, a grip force increase was observed even when the tested word was a pseudo-verb. The presence of a known action word is thus not required to trigger motor activation. Importantly, however, the same linguistic contexts that sufficed to trigger motor activation with pseudo-verbs failed to trigger motor activation when the target words were verbs with no motor action reference. Context is thus not by itself sufficient to supersede an “incompatible” word meaning. We argue that motor structure activation is part of a dynamic process that integrates the lexical meaning potential of a term and the context in the online construction of a situation model, which is a crucial process for fluent and efficient online language comprehension.

Semantic significance: a new measure of feature salience

According to the feature-based model of semantic memory, concepts are described by a set of semantic features that contribute, with different weights, to the meaning of a concept. Interestingly, this theoretical framework has introduced numerous dimensions to describe semantic features. Recently, we proposed a new parameter to measure the importance of a semantic feature for the conceptual representation-that is, semantic significance. Here, with speeded verification tasks, we tested the predictive value of our index and investigated the relative roles of conceptual and featural dimensions on the participants' performance. The results showed that semantic significance is a good predictor of participants' verification latencies and suggested that it efficiently captures the salience of a feature for the computation of the meaning of a given concept. Therefore, we suggest that semantic significance can be considered an effective index of the importance of a feature in a given conceptual representation. Moreover, we propose that it may have straightforward implications for feature-based models of semantic memory, as an important additional factor for understanding conceptual representation.

The two faces of agreement

The goal of this discussion note is to argue that the complex facts of hybrid agreement in Serbo-Croatian can and should be accounted for by assuming that only two sets of features are relevant to agreement: a syntactic set and a semantic set. This is in opposition to existing proposals that claim that two distinct sets of syntactic features are required in addition to the set of semantic features in order to account for those agreement facts (Wechsler & &Zlatić 2000, 2003). The proposal defended here with two sets of agreement features is shown to be superior to the alternative with three feature sets because it not only is simpler and accounts for the facts just as well, but it also does not make some incorrect predictions that the alternative makes when crucial facts are considered.