Mapping lexical entries in verbs database to WordNet senses

情报学报2020年12月第39卷第12期Journal of the China Society for Scientific and Technical Information,Dec.2020,39(12):1320-1329学术文本词汇功能识别——基于BERT向量化表示的关键词自动分类研究陆伟1，2，李鹏程1，2，张国标1，2，程齐凯1，2（1.武汉大学信息管理学院，武汉430072；2.武汉大学信息检索与知识挖掘研究所，武汉430072）摘要关键词作为学术文本中映射全文主题内容的词汇或术语，能够为知识精准检索和文本大规模计算提供重要的底层语义标签。

当前学术文本中的关键词存在使用意图不明、语义功能模糊及上下文信息缺失等问题。

为此，本文提出了一种基于有监督学习的神经网络方法，对关键词所承载的语义功能进行分类，实现对学术文本中研究问题和研究方法的识别。

本文以计算机等领域为期10年的学术期刊论文为训练语料，利用BERT及LSTM方法构建分类模型，实验结果显示，本文所提出的方法较传统更优，其整体准确率、召回率和F1值分别达到0.83、0.87和0.85。

关键词学术文本；关键词；语义功能识别；深度学习Recognition of Lexical Functions in Academic Texts:Automatic Classification of Keywords Based on BERT VectorizationLu Wei1,2,Li Pengcheng1,2,Zhang Guobiao1,2and Cheng Qikai1,2(1.School of Information Management,Wuhan University,Wuhan430072;2.Institute for Information Retrieval and Knowledge Mining,Wuhan University,Wuhan430072)Abstract：As vocabulary or terminology that maps the full-text subject matter content in academic texts,keywords can provide important underlying semantic labels for knowledge retrieval and large-scale text computation.At present,there are problems in the use of keywords in academic texts,such as unclear intention,fuzzy semantic function,and lack of con‐text information.Therefore,a neural network method based on supervised learning is proposed to classify the semantic functions carried by keywords to facilitate the identification of research questions and research methods in academic texts.In this study,journal papers published during a period of10years in the field of computer science were used as the training corpus,and the classification model was constructed using BERT and LSTM models.The results show that the proposed method is better than the traditional method.Its overall accuracy,recall rate,and F1value reached0.83,0.87,and0.85.Key words：academic text;keywords;lexical function recognition;deep learning1引言随着科研社区规模的快速扩张和学术文献数量的急剧增长，从海量的学术论文中快速、精准的获取知识越发困难。

A COMPARATIVE ANALYSIS OF UK AND US ENGLISH ACCENTS INRECOGNITION AND SYNTHESISQin Yan, Saeed VaseghiDept of Electronic and Computer EngineeringBrunel University, Uxbridge, Middlesex, UK UB8 3PHQin.Yan@,Saeed.Vaseghi@ABSTRACTIn this paper, we present a comparative study of the acoustic speech features of two major English accents: British English and American English. Experiments examined the deterioration in speech recognition resulting from the mismatch between English accents of the input speech and the speech models. Mismatch in accents can increase the error rates by more than 100%. Hence a detailed study of the acoustic correlates of accent using intonation pattern and pitch characteristics was performed. Accents differences are acoustic manifestations of differences in duration, pitch and intonation pattern and of course the differences in phonetic transcriptions. Particularly, British speakers possess much steeper pitch rise and fall pattern and lower average pitch in most of vowels. Finally a possible meansto convert English accents is suggested based on above analysis.1. INTRODUCTIONIn the recent years, there have been significant advancesin speech recognition systems resulting in reduction in the error rate. Two of the most important remaining obstaclesto reliable high performance speech recognition systems are noise and speaker variations. An important aspect of speaker variation is accent. However, current speech recognisers are trained on a specific national accent group (e.g. UK or US English accents), and may have a significant deterioration in performance when processing accents unseen in the training data. An understanding of the causes and acoustic properties of English accents can also be quite useful in several areas such as speech synthesis and voice conversion.In [3] J.C. Wells described the term accent as a pattern of pronunciation used by a speaker for whom English is the native language or more generally, by the community or social grouping to which he or she belongs. Linguistically, accent variation does not only lie in phonetic characteristics but also in the prosody.There has been considerable research conducted on understanding the causes and the acoustics correlates of native English accent. A study in [3] examined a variety of native English accents from a linguistics point of view. Recently more focused studies have been made on acoustic characteristics of English accents. In [4] a method is described to decrease the recognition error rate by automatically generating the accent dictionary through comparison of standard transcription with decoded phone sequence. In [1], rather than using phonetic symbols, different regional accents are synthesized by an accent-independent keyword lexicon. During synthesis, input text is first transcribed as keyword lexicon. Until post-lexical processes, accent dependent allophonic rules were applied to deal with such features as /t//d/ topping in US English, or r-linking in British English. The advantage of this method is that it avoids applying different phonetic symbols to represent various accents. In addition, [2] established a voice conversion system between British and US English accents by HMM-based spectral mapping with set rules for mapping two different phone sets. However, it still has some residual of original source accent characteristics in the converted result.In this paper, experiments began with cross accent recognition to quantify the accent effects between British accent (BrA) and American accent (GenAm) on speechrecognition. A further detailed acoustics feature study of English accent using duration, intonation and frequency characteristics was performed.2. CROSS ACCENT RECOGNITIONAt first, a set of simple experiments was carried out to quantify the effect of accents on the speech recognisers with accent specific dictionaries. The model training and recogniser used here are based on HTK [9]. British accent speech recogniser was trained on Continuous Speech Recognition Corpus (WSJCAM0). American accent speech recogniser was trained on WSJ. Test sets used are WSJ si_dt_05 si_et_05 and WSJCAM si_dt5b, each containing 5k words. Both recognisers employ 3-state left-to-right HMMs. The features used in experiments were 39 MFCCs with energy and their differentiation and acceleration.Accent British model American model British input 12.8 29.3American input 30.6 8.8 Average 21.719.1 Table 1: % word error rate of cross accents speech recognition between British and American accentTable 1 shows that for this database the American English achieves 31% less error than the British English in matched accent conditions. Mismatched accent of the speaker and the recognition system deteriorates the performance. The result was getting worse by 139% for recognizing British English with American models and 232% for recognizing American English with British models. The results are based on word models compiled from triphone HMMs with three states per model and 20 mixtures Gaussians per state.The next section examines the acoustics features of both English accents in an attempt to identify where the main difference lies in addition to the variation in pronunciation.3. ANALYSIS OF ACOUSTIC FEATURES OFUS AND UK ENGLISH ACCENTS3.1 DurationFigure 1 shows that the vowel durations at the start andthe end of sentences in BrA is shorter than that in GenAm. This could be due to the following reason. British speakers always tend to pronounce last syllable fast. It is the case especially for consonants. However, Americans tend to realize more acoustically complete pronunciation.Table 2 gives the comparison of two database in speaking rate. The speaker rate of Wsjcam0 is 7.8% higher than that of Wsj. This is in accordance with comparison in phone duration in Figure 1.The results of these comparisons are shown in Figure 1. Note that results are only presented for models common to both system phones sets.Speak Rate(no/sec)Phone WordWsjcam0 9.77 3.04 Wsj 10.39 2.82Table 2 : Speak rate in Phone and wordfrom Wsjcam0 and WsjFigure 1: Difference of Vowel duration of GenAm and BrA at the utterance starts and ends3.2 Pitch CharacteristicsTable 3 and 4 list average pitch values and numbers of speakers from both databases. Figure 2 displays the difference of average vowel pitch frequency of male speakers of two accents while Figure 3 shows the corresponding comparison of female speakers. Even BrA has lower average pitch than GenAm in the whole phone set, for the common vowels, their average pitch in BrA is stil much more lower than corresponding part in GenAm. It is interesting to note that for most of vowels, British speakers give lower pitch than American counterparts. For British female speakers, its 118% lower than American female in average while it drops down to 7.7% when comparing with British male and American male in the common set vowels. In accordance with [4], diphthongs such as ay uw er, display more difference than other vowels. Furthermore, average pitch frequency of the last word of sentences from male speakers of both accentsalso clearly demonstrate similar results that British speakers are generally speaking lower than their counterpart. Besides, it can be noted that British male speakers gain high average pitch in three vowels : uh, ih and ae .Speaker No. Male FemaleWsjcam0 112 93 Wsj 37 41 Table 3: Number of speakers Wsjcam0 and WsjAvg PitchMaleFemale Wsjcam0 115.8 Hz 196.2 Hz Wsj 127.8 Hz 208.9 Hz Difference 9.4% 5.7% Table 4: Average pitch of Wsjcam0 and WsjFigure 2: Difference of average pitch value of vowels ofGenAm and BrA (male speakers)Figure 3: Difference of average pitch value of vowelsGenAm and BrA (female speakers)Figure 4(a)Figure 4(b)Figure 4 (a): Average of Rise and Fall patterns fromBritish and American speakersFigure 4 (b): Average of Rise and Fall patterns of lastword of the sentencesXlabel: uniform duration (1.812ms), Ylabel: frequency 3.3 ProsodyProsody is usually made up of Intonation-groups, Pitch Event and Pitch Accent .Intonation-groups are composed of a sequence of pitch events within phrase. Pitch Event is a combination of a pitch rise and fall. Pitch accent ,either a pitch rise or a pitch fall, is the most elementary unit of intonation.In [6], a rise fall connection (RFC) model was applied to model the pitch contour by Legendre polynomial function [a1, a2, a3], where a1, a2, a3, called discrete Legendre Polynomial Coefficients, were related to the average contour, average contour slope and average trend of the slope within that pitch accent. Rise and fall are detected according to f0 contour. Based on this, experiments were made on computing the average pattern of pitch accents(Fall and Rise only in this case) to explore the numerical difference of both accents in intonation. Figure 4(a) illustrates the average of rise and fall patterns from both male and female speakers. It is noticeable that British speakers intend to have steeper rise and fall than American speakers. Particularly, for rise pattern, their difference in pitch change rate reaches 34% in average while fall pattern only gives 21% difference. In addition, it is also noticeable that pitch range narrows towards the end of an utterance as [8].Further to the results that American speaker tends to speak lower in final words of sentences. Figure 4(b) indicates that BrA Rise pattern in the last words is much more steeper than that of GenAm with pitch change rate of 48% and 32% respectively.In contrast, the fall pattern is almost same in either figure. Then British speakers possess much steeper pitch accent than American speakers.5. DISCUSSIONS AND CONCLUSIONWe have presented a detailed study of acoustic features about two major English accents: BrA and GenAm. In addition to the significant difference in phonetics, the slope of Rise and Fall accent also exhibits great difference. British speakers tend to speak with lower pitch but higher pitch change rate, especially in the rise accent. Future experiments are to be extended to other context-dependent pitch pattern analysis besides utterance end.In general, accent conversion/synthesis could be simplified into two aspects: phonetics and acoustics. Beep dictionary and CMU dictionary explicitly display the phonetics difference between two accents in terms of phone substitute, delete and insert. In this paper, we began the exploration of acoustics difference between two accents in the view of duration, pitch and intonation pattern.Therefore, the accent synthesis is planned to carry on by two steps for future experiments.1) Pronunciation modelling by transcribing GenAm by BrA phones to map phonetic difference of two accents [4] or vice verse.2) Prosody modification [7] [8]. By applying Tilt model base on decision-tree HMM, tilt parameters are changed according to above analysis. The advantage of Tilt model lies in its continuous tilt parameters, which better describe the intonation pattern than RFC models or FUJISAKI models [7]. A new pitch contour is then synthesized after changing tilt parameters according above study.6 ACKNOWLEDGEMENTSThis research has been supported by Department of Computing and Electronic Engineering, Brunel University, UK. We thank Ching-Hsiang Ho for the program of detecting the pitch accents.7. REFERENCE[1] Susan Fitt, Stephen Isard, Synthesis of Regional English Using A Keyword Lexicon.Proceedings Eurospeech 99, Vol. 2, pp. 823-6.[2] Ching-Hsiang Ho, Saeed Vaseghi, Aimin Chen, Voice Conversion between UK and US Accented English, Eurospeech 99.[3] J.C. Wells, Accents of English, volume:1,2, Cambridge University Press, 1982[4] Jason John Humphries, Accent Modelling and Adaptation in Automatic Speech recognition, PhD Thesis, Cambridge University Engineering Department[5] Alan Cruttenden, Intonation, Second Edition 1997[6] Ching-Hsiang Ho, Speaker Modelling for Voice Conversion, PHD thesis, Department of Computing and Electronic Engineering, Brunel University[7] Thierry Dutoit, Introduction to text-to-speech synthesis, Kluwer (1997)[8] Paul Taylor, Analysis and Synthesis of Intonation using Tilt Model, Journal of the Acoustical Society of America. Vol 107 3, pp. 1697-1714.[9] Steve Young, Dan Kershaw, Julian Odell, Dave Ollason, Valtcho Valtchev, Phil Woodland, The HTK Book. V2.2。

WordNet. An electronic lexical database. Edited by Christiane Fellbaum, with a preface by George Miller. Cambridge, MA: MIT Press; 1998. 422 p. $50.00This is a landmark book. For anyone interested in language, in dictionaries and thesauri, or natural language processing, the introduction, Chapters 1- 4, and Chapter 16 are must reading. (Select other chapters according to your special interests; see the chapter-by-chapter review). These chapters provide a thorough introduction to the preeminent electronic lexical database of today in terms of accessibility and usage in a wide range of applications. But what does that have to do with digital libraries? Natural language processing is essential for dealing efficiently with the large quantities of text now available online: fact extraction and summarization, automated indexing and text categorization, and machine translation. Another essential function is helping the user with query formulation through synonym relationships between words and hierarchical and other relationships between concepts. WordNet supports both of these functions and thus deserves careful study by the digital library community.The introduction and part I, which take almost a third of the book, give a very clear and very readable overview of the content, structure, and implementation of WordNet, of what is in WordNet and what is not; these chapters are meant to replace Five papers on WordNet(ftp:///pub/WordNet/5papers.ps), which by now are partially outdated. However, I did not throw out my copy of the Five papers; they give more detail and interesting discussions not found in the book. Chapter 16 provides a very useful complement; it includes a very good overview of WordNet relations (with examples and statistics) and describes possible extensions of content and structure.Part II, about 15% of the book, describes "extensions, enhancements, and new perspectives on WordNet", with chapters on the automatic discovery of lexical and semantic relations through analysis of text, on the inclusion of information on the syntactic patterns in which verbs occur, and on formal mathematical analysis of the WordNet structure.Part III, about half the book, deals with representative applications of WordNet, from creating a "semantic concordance" (a text corpus in which words are tagged with their proper sense), to automated word sense disambiguation, to information retrieval, to conceptual modeling. These are good examples of pure knowledge-based approaches and of approaches where statistical processing is informed by knowledge from WordNet.As one might expect, the papers in this collection (which are reviewed individually below), are of varying quality; Chapters 5, 12, and 16 stand out. Many of the authors pay insufficient heed to the simple principle that the reader needs to understand the overall purpose of work being discussed in order to best assimilate the detail. Repeatedly, one finds small differences in performance discussed as if they meant something, when it is clear that they are not statistically significant (or at least it is not shown that they are), a problem that afflicts much of information retrieval and related research. The application papers demonstrate the many uses of WordNet but also make a number of suggestions for expanding the types of information included in WordNet to make it even more useful. They also uncover weaknesses in the content of WordNet by tracing performance problems to their ultimate causes.The papers are tied together into a coherent whole, and that unity of the book is further enhanced by the presence of an index, which is often missing from such collections. One would wish the index a bit more detailed.. The reader wishing for a comprehensive bibliography on WordNet can find it at /~josephr/wn-biblio.html (and more information on WordNet can be found at /~wn).The book deals with WordNet 1.5, while WordNet 1.6 was released at just about the same time. For the papers on applications it could not be otherwise, but the chapters describing WordNet might have been updated to include the 1.6 enhancements. One would have wished for a chapter or at least a prominent mention of EuroWordNet (www.let.uva.nl/~ewn), a multilingual lexical database using WordNet's concept hierarchy.It would have been useful to enforce common use at least of key terms throughout the papers; for example, the introduction says "WordNet makes the commonly accepted distinction between conceptual-semantic relations, which link concepts, and lexical relations, which link individual words [emphasis added], yet in Chapter 1 George Miller states that "the basic semantic relation in WordNet is synonymy" [emphasis added] and in Chapter 5 Marti Hearst uses the term lexical relationships to refer to both semantic relationships and lexical relationships. Priss (p. 186) confuses the issue further in the following statements: "Semantic relations, such as meronymy, synonymy, and hyponymy, are according to WordNet terminology relations that are defined among synsets. They are distinguished from lexical relations, such as antonymy, which are defined among words and not among synsets." [Emphasis added]. Synonymy is, of course, a lexical relation, and terminology relations are relations between words or between words and concepts, but not relations between concepts.This terminological confusion is indicative of a deeper problem. The authors of WordNet vacillate somewhat between a position that stays entirely in the plane of words and deals with conceptual relationships in terms of the relationships between words, and the position, commonly adopted in information science, of separating the conceptual plane from the terminological plane.What follows is a chapter-by-chapter review. By necessity, this often gets into a discussion how things are done in WordNet, rather than just staying with the quality of the description per se.Part I. The lexical database (p. 1 - 127)George Miller's preface is well worth reading for its account of the history and background of WordNet. It also illustrates the lack of communication between fields concerned with language: Thesaurus makers could learn much from WordNet, and WordNet could learn much from thesaurus-makers. The introduction gives a concise overview of WordNet and a preview of the book. It might still make clear more explicitly the structure of WordNet: The smallest unit is the word/sense pair identified by a sense key (p. 107); word/sense pairs are linked through WordNet's basic relation, synonymy, which is expressed by grouping word/sense pairs into synonym sets or synsets; each synset represents a concept, which is often explained through a brief gloss (definition). Put differently, WordNet includes the relationship word W designates concept C, which is coded implicitly by including W in the synset for C. (This is made explicitin Table 16.1.) Two words are synonyms if they have a designates relationship to the same concept. Synsets (concepts) are the basic building blocks for hierarchies and other conceptual structures in WordNet.The following three chapters each deal with a type of word. In Chapter 1, Nouns in WordNet, George Miller presents a cogent discussion of the relations between nouns: synonymy, a lexical relation, and hyponymy (has narrower concept) and meronymy (has part), semantic relations. Hyponymy gives rise to the WordNet hierarchy. Unfortunately, it is not easy to get well-designed display of that hierarchy. Meronymy (has part) / holonymy (is part of) always are subject to confusion, not completely avoided in this chapter, which stems from ignoring the fact that airplane has-part wing really means "an individual object 1 which belongs to the class airplane has-part individual object 2 which belongs to the class wing". While it is true that bird has-part wing, it is by no means the same wing or even the same type of wing. Thus the strength of a relationship established indirectly between two concepts due to a has-part relationship to the same concept may vary widely. Miller sees meronymy and hyponymy as structurally similar, both going down. But it is equally reasonable to consider a wing as something more general than an airplane and thus construe the has-part relationship s going up, as is done in Chapter 13. Chapter 2, Modifiers in WordNet by Katherine J. Miller describes how adjectives and adverbs are handled in WordNet. It is claimed that nothing like hyponymy/hypernymy is available for adjectives. However, especially for information retrieval purposes, the following definition makes sense: Adj A has narrower term (NT) Adj B if A applies whenever B applies and A and B are not synonyms (the scope of A includes the scope of B and more). For example, red NT ruby (and conversely, ruby BT red) or large NT huge. To the extent that WordNet includes specific color values, their hierarchy is expressed only for the noun form of the color names. Hierarchical relationships between adjectives are expressed as similarity. The treatment of antonymy is somewhat tortured to take account of the fact that large vs small and big vs. little are antonym pairs, but *large vs. little or *prompt vs. slow are not. In general, when speakers express semantic opposition, they do not use just any term for each of the opposite concepts, but they apply lexical selection rules. The simplest way to deal with this problem is to establish an explicit semantic relationship of opposition between concepts and separately record antonymy as a lexical relationship between words (in their appropriate senses). Participial adjectives are maintained in a separate file and have a pointer to the verb unless they fit easily into the cluster structure of descriptive adjectives "rather than" having a pointer to the verb. Why a participial adjective in the cluster structure cannot also have a pointer to the verb is not explained.In Chapter 3, A semantic network of English verbs, Christiane Fellbaum presents a carefully reasoned analysis of the semantic relations between verbs (as presented in WordNet and as presented in alternate approaches) and of the other information for verbs, especially sentence structures, given in WordNet. Much of this discussion is based on results from psycholinguistics. The relationships between verbs are all forms of a broadly defined entailment relationship: troponymy establishes a verb hierarchy (verb A has the troponym verb B if B is a particular way of doing A, corresponding to hyponymy between nouns; the reverse relation is verb B has hypernym verb A); entailment in a more specific sense (to snore entails to sleep); cause; and backward presupposition (one must know before one can forget, not given inWordNet). More refined relationship types could be defined but are not for WordNet, since they do not seem to be psychologically salient and would complicate the interface. The last is not a good reason, since detailed relationship types could be coded internally and mapped to a more general set to keep the interface simple; the additional information would be useful for specific applications. In Section 3.3.1.3 on basic level categories in verbs, the level numbers are garbled (talk is both level L+1 and level L), making the argument unclear.In Chapter 4, Design and implementation of the WordNet lexical database and searching software, Randee I. Tengi lays out the technical issues. While it is hard to argue with a successful system, one might observe that this is not the most user-friendly system this reviewer has seen. The lexicographers have to memorize a set of special characters that encode relationship types (for example, ~ for hyponym, @ for hypernym); {apple, edible_fruit, @} means: apple has the hypernym edible_fruit. The interface is on a par with most online thesaurus interfaces, that is to say, poor. In particular, one cannot see a simple outline of the concept hierarchy; for many thesauri that is available in printed form. P. 108 says that "many relations being reflexive", when it should say reciprocal (such as hyponym / hypernym); a reflexive relation has a precise definition in mathematics (for all a, a R a holds).Chapter 16, Knowledge processing on an extended WordNet by Sanda M. Harabagiu and Dan I. Moldovan is discussed here because it should be read here. Table 16.1 gives the best overview of the relations in WordNet, with a formal specification of the entities linked, examples, properties (symmetry, transitivity, reverse of), and statistics. Table 16.2 gives further types of relationships that can be derived from the glosses (definitions) provided in WordNet. Still further relationships can be derived by inferences on relationship chains. The application of this extended WordNet to knowledge processing (for example, marker propagation, testing a text for coherence) is less well explained.Part II. Extensions, enhancements, and new perspectives on WordNet p. 129 - 196Chapter 5, Automated discovery of WordNet relations, by Marti A. Hearst is a well-written account of using Lexico-Syntactic Pattern Extraction (LSPE) to mine text for hyponym relationships. This is important to reduce the enormous workload in the strictly intellectual compilation of the large lexical databases needed for NLP and retrieval tasks. The following examples illustrate two sample patterns (the implied hyponym relationships should be obvious):red algae, such as Gelidiumbruises, broken bones, or other injuriesPatterns can be hand-coded and/or identified automatically. The results presented are promising. In the discussion of Automatic acquisition from corpora (p. 146, under Related work), only fairly recent work from computational linguistics is cited. Resnik, in Chapter 10, cites work back to 1957. There is also early work done in the context of information retrieval, for example, Giuliano 1965; for more references on early work see Soergel 1974, Chapter H.Chapter 6, Representing verb alternations in WordNet, by Karen T. Kohl, Douglas A. Jones, Robert C. Berwick, and Naoyuki Nomura discusses a format for representing syntactic patterns of verbs in WordNet. It is clearly written for linguists and a heavy read for others. It is best to look at the appendix first to get an idea of the ultimate purpose.Chapter 7, The formalization of WordNet by methods of relational concept analysis, by Uta E. Priss is somewhat of an overkill in formalization. Formal concept analysis might be a useful method, but the starkly abbreviated presentation given here is not enough to really understand it. The paper does reveal a proper analysis of meronymy, once one penetrates the formalism and the notation. The WordNet problems identified on p. 191-195 are quite obvious to the experienced thesaurus builder once the hierarchy is presented in a clear format and could in any event be detected automatically based on simple componential analysis.Part III Applications of WordNet. p. 197-406Chapters 8 Building semantic concordances, by Shari Landes, Claudia Leacock, and Randee I. Tengi and Chapter 9, Performance and confidence in a semantic annotation task, by Christiane Fellbaum, Joachim Grabowski, and Shari Landes both deal with manually tagging each word in a corpus (in the example, 103 passages from the Brown corpus and the complete text of Crane's The red badge of courage) with the appropriate WordNet sense. This is useful for many purposes: detecting new senses, obtaining statistics on sense occurrence, testing the effectiveness of correct disambiguation for retrieval or clustering of documents, and training disambiguation algorithms. The tagged corpus is available with WordNet.Chapter 10, WordNet and class-based probabilities, by Philip Resnik, presents an interesting and discerning discourse on "how does one work with corpus-based statistical methods in the context of a taxonomy?" or, put differently, how does one exploit the knowledge inherent in a taxonomy to make statistical approaches to language analysis and processing (including retrieval) more effective. However, the real usefulness of the statistical approach does not appear clearly until Section 10.3, where the approach is applied to the study of selectional preferences of verbs (another example of mining semantic and lexical information from text), and thus the reader lacks the motivation to understand the sophisticated statistical modeling. The reader deeply interested in these matters might prefer to turn to the fuller version in Resnik's thesis (access from /~Resnik),Chapters 11, Combining local context and WordNet similarity for word sense identification, by Claudia Leacok and Martin Chodorov and 13, Lexical chains as representations of context for the detection and correction of malapropisms, by Graeme Hirst and David St-Onge present methods for sense disambiguation. Chapter 11 uses a statistical approach, computing from a sense-tagged corpus (the training corpus) three distributions that measure the associations of a word in a given sense with part-of-speech tags, open-class words, and closed-class words. These distributions are then used to predict the sense of a word in arbitrary text. The key idea of the paper is very close to the basic idea of Chapter 10: exploit knowledge to improve statistical methods, in this case use knowledge on word sense similarity gleaned from WordNet to extend the usefulness of the training corpus by using the distributions for a sense A1 of word A todisambiguate word B, one of whose senses is similar to A1. Results of experiments are inconclusive (they lack statistical significance), but he idea seems definitely worth pursuing. Hirst and St-Onge take an entirely different approach. Using semantic relations from WordNet they build what they call lexical chains (semantic chain would be a better term) of words that occur in the text. Initially there are many competing chains, corresponding to different senses of the words occurring in the text, but eventually some chains grow long and others do not, and the word senses in the long chains are selected. (Instead of chains one might use semantic networks.) Various patterns of direct and indirect relationships are defined as allowable chain links, and each pattern has a given strength. In the paper, this method is used to detect and correct malapropisms, defined here as misspellings that are valid words (and therefore not detected by a spell checker), for example, “Much of the data is available toady electronically” or “Lexical relations very in number within the text”. (Incidentally, this sense of malapropism is not found in standard dictionaries, including WordNet; in a book on WordNet, one should use language carefully.) The problem of detecting misspellings that are themselves words is the same as the homonym problem: If one accepts, for example, both today and toady as variations of the word today and of the word toady, then [today. toady] becomes a homonym having all the senses of the two words that are misspellings of each other; that is to say, the occurrence of either word in the text may indicate any of the senses of both words. Finding the sense that fits the context selects the form correctly associated with that sense. This is an ingenious application of sense disambiguation methods to spell checking.Chapter 14, Temporal indexing through lexical chaining, by Reem Al-Halimi and Rick Kazman uses a very similar approach to indexing text, an idea that seems quite good (as opposed to the writing). The texts in question are transcripts from video conferences, but that is tangential and their temporal nature is not considered in the paper. Instead of chains they build lexical trees (again, semantic tree would be a better name, and why not use more general semantic networks).A text is represented by one or more trees (presumably each tree representing a logical unit of the text). Retrieval then is based on these trees as target objects. A key point in this method, not stressed in the paper, is the word sense disambiguation that comes about in building the semantic trees. The method appears promising, but no retrieval test results are reported.In Chapter 12, Using WordNet for text retrieval, Ellen M. Voorhees reports on retrieval experiments using WordNet for query term expansion and word sense disambiguation. No conclusions as to the usefulness of these methods should be drawn from the results. The algorithms for query term expansion and for word sense disambiguation performed poorly —partially due to problems in the algorithms themselves, partially due to problems in WordNet —so it is not surprising that retrieval results were poor. Later work on improved algorithms, including work by Voorhees, is cited.In Chapter 15, COLOR-X: Using knowledge from WordNet for conceptual modeling [in software engineering], J. F. M. Burg and R. P. van de Riet describe the use of WordNet to clarify word senses in software requirement documents and for detecting semantic relationships between entity types and relationship types in an E-R model. The case they make is not very convincing. They also bring the art of making acronym soup to new heights, misleading the reader in the process (the chapter has nothing to do with colors).All in all, this is a useful collection of papers and a rich source of ideas.ReferencesGiuliano, Vincent E. 1965 The interpretation of word associations. In Stevens, Mary E.; Giuliano, Vincent E.; Heilprin, Lawrence B., eds. Statistical association methods for mechanical documentation. Washington, DC: Government Printing Office; 1965 December. 261 p. (National Bureau of Standards Miscellaneous Publication 269)Soergel, Dagobert. 1974. Indexing languages and thesauri: Construction and maintenance. Los Angeles, CA: Melville; 1974. 632 p., 72 fig., ca 850 ref. (Wiley Information Science Series)。

13. ULTRA: ULTR的官方文档,封装了常见的算法. 13.1 Github地址: https:///ULTR-Community/ULTRA 1.3.2 官方文档: https://ultr-community.github.io/ULTRA/ranking_model_reference.html
sigir2019-LTR-tutorial PPT链接地址: r.it/slides/ 搭配中文理解: 排序学习(LTR)杂谈 (下) https:///p/138436960
4. (2020-04)Transformer 在美团搜索排序中的实践: https:///2020/04/16/transformer-in-meituan.html 5. （2020-03）美团-WSDM Cup 2020检索排序评测任务第一名经验总结(多模融合排序检索)： https:///2020/03/26/wsdm-2020-bert-lightgbm.html 6. (2019-01)大众点评搜索基于知识图谱的深度学习排序实践: https:///2019/01/17/dianping-search-deeplearning.html 7. 全面理解搜索Query：当你在搜索引擎中敲下回车后，发生了什么？ https:///p/112719984 8. 漫谈搜索引擎 https:///p/99624706 ] LTR论文整理:
VSCode 如何编写运行 C、C++ 程序？ - 谭九鼎的回答 - 知乎： https:///question/30315894/answer/154979413 几乎可以当作问题大全查看;
： VSCode实现C++多文件编译: 1. 命令行 : https:///qq_34801642/article/details/103770219?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.add_param_isCf&depth_1-utm_source=distribute.pc_relevant.none-task-bl 2. 基于MinGW: https:///u012030174/article/details/107791407?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.add_param_isCf&depth_1-utm_source=distribute.pc_relevant.none-task2.1 单纯修改tasks.json中的参数; VS Code：编译运行根目录下不同文件夹中的文件： https:///qq_34801642/article/details/106419763